topic_modelling_factchecked_stories.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Topic Modelling on Fact-Checked Stories\n",
    "\n",
    "This notebook builds LDA topic models on the headlines and body text of 13,000+ fact-checking stories in the Tattle archive. The first model is built on headlines and the second model on body text.\n",
    "\n",
    "\n",
    "#### Brief description of the model\n",
    "\n",
    "Latent Dirichlet Allocation is an unsupervised, probabilistic model that generates topic-document and word-topic probability distributions from a collection of text documents. Topics are themes that occur in documents. Here, document = headline or body text.\n",
    "\n",
    "### Process\n",
    "\n",
    "#### Part One: modelling headlines \n",
    "1. Getting the data from MongoDB and storing locally\n",
    "2. Text cleaning (removing noise, English / non-English headlines separation using regex)\n",
    "3. Translating non-English headlines and storing translations locally\n",
    "4. Pre-processing all the headlines (tokenizing, stop word removal, lemmatizing, creating bigrams)\n",
    "5. Text transformation: creating a corpus of vectors\n",
    "6. Selecting the number of topics \n",
    "7. Building the topic model \n",
    "8. Topic interpretation\n",
    "9. Interactive model visualisation\n",
    "10. N-gram analysis of the headlines\n",
    "\n",
    "#### Part Two: modelling body text\n",
    "11. Preprocessing the body text (regex cleanup, tokenizing, stop word removal, lemmatizing, creating bigrams)\n",
    "12. Text transformation: creating a corpus of vectors\n",
    "13. Selecting the number of topics \n",
    "14. Building the topic model \n",
    "15. Topic interpretation\n",
    "16. Interactive model visualisation\n",
    "17. N-gram analysis of the body text\n",
    "\n",
    "11. Possible applications"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part One: Topic Modelling on headlines\n",
    "## 1. Getting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "remove_cell"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kruttikanadig/anaconda3/lib/python3.7/site-packages/nbconvert/exporters/exporter_locator.py:28: DeprecationWarning: `nbconvert.exporters.exporter_locator` is deprecated in favor of `nbconvert.exporters.base` since nbconvert 5.0.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# Importing libraries\n",
    "import os\n",
    "import requests\n",
    "import time\n",
    "from time import sleep\n",
    "from random import uniform\n",
    "import datetime\n",
    "from datetime import date\n",
    "import csv\n",
    "from pymongo import MongoClient\n",
    "from dotenv import load_dotenv\n",
    "load_dotenv()\n",
    "import os\n",
    "from os import environ\n",
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pprint import pprint\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "import spacy\n",
    "import gensim\n",
    "import gensim.corpora as corpora\n",
    "from gensim.corpora import Dictionary\n",
    "from gensim.utils import simple_preprocess\n",
    "from gensim.models import ldamodel\n",
    "from gensim.models import CoherenceModel \n",
    "import re\n",
    "from langdetect import detect\n",
    "from gensim.models.phrases import Phrases, Phraser\n",
    "from nltk import FreqDist\n",
    "from nltk.corpus import RegexpTokenizer as regextoken\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "import logging\n",
    "logging.basicConfig(filename='lda_model.log', format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\n",
    "import googletrans\n",
    "from googletrans import Translator\n",
    "import pyLDAvis\n",
    "import pyLDAvis.gensim\n",
    "from gensim import similarities\n",
    "import nbconvert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get data from factchecking sites MongoDB\n",
    "def initialize_mongo():\n",
    "    mongo_url = \"mongodb+srv://os.environ.get(\"FACTCHECK_DB_USERNAME\"):os.environ.get(\"FACTCHECK_DB_PASSWORD\")@tattle-data-fkpmg.mongodb.net/test?retryWrites=true&w=majority&ssl=true&ssl_cert_reqs=CERT_NONE\"   \n",
    "    cli = MongoClient(mongo_url)\n",
    "    db = cli[os.environ.get(\"FACTCHECK_DB_NAME\")]\n",
    "    coll = db[os.environ.get(\"FACTCHECK_DB_COLLECTION\")]\n",
    "    if coll.count_documents({}) > 0:\n",
    "        return coll \n",
    "    else:\n",
    "        print(\"Error accessing Mongo collection\")\n",
    "        sys.exit()\n",
    "        \n",
    "coll = initialize_mongo()\n",
    "df = pd.DataFrame.from_records(coll.find({}))\n",
    "df.to_csv(\"factchecking_stories.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"factchecking_stories.csv\")\n",
    "df = df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Madhu Kishwar tweets photoshopped image of Amu...\n",
       "1    Photoshopped promo of ABP News over Chandrayaa...\n",
       "2    Video of scuffle between men in khaki uniform ...\n",
       "Name: headline, dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Snapshot of headlines\n",
    "df[\"headline\"][0:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Text cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save headlines in a variable\n",
    "raw_data = df[\"headline\"].values.tolist()\n",
    "\n",
    "# Defining a function to remove accented characters in the headlines  \n",
    "def data_dict(sentences):\n",
    "    return dict((sentence, \", \".join(simple_preprocess(str(sentence), deacc=True, max_len=100))) for sentence in sentences)\n",
    "\n",
    "result = data_dict(raw_data)\n",
    "\n",
    "# Separating non-English headlines using regex\n",
    "pat = re.compile(\"[^\\x00-\\x7F]\") # matches non-English characters\n",
    "non_eng = [k for k,v in result.items() if pat.search(v)]\n",
    "eng = [k for k,v in result.items() if not pat.search(v)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Translating non-English headlines\n",
    "\n",
    "Googletrans is a free library that sends translation requests to the Google Translate API. \n",
    "Random time delays between requests are advised, else Google may (and probably will) block your ip address.\n",
    "\n",
    "Translating the 5200+ non-English headlines took approximately 6.5 hours."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Translating non-English headlines using googletrans library\n",
    "\n",
    "translator = Translator()\n",
    "translations = []\n",
    "for doc in non_eng:\n",
    "    translations.append(translator.translate(doc).text)\n",
    "    time.sleep(uniform(3,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the original and translated headlines for future reference\n",
    "translated_headlines = dict(zip(non_eng, translations))\n",
    "translations_df = pd.DataFrame(translated_headlines.items(), columns = [\"headline\", \"translation\"])\n",
    "translations_df[\"original_english\"] = 0\n",
    "translations_df = translations_df.append(pd.DataFrame(eng, columns=['headline']), ignore_index=True, sort=True)\n",
    "translations_df[\"original_english\"].fillna(value=1, inplace = True)\n",
    "translations_df[\"original_english\"] = translations_df[\"original_english\"].astype(int)\n",
    "translations_df.to_csv(\"headlines_with_translations.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>headline</th>\n",
       "      <th>original_english</th>\n",
       "      <th>translation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Govt’s Lucky Grahak Yojana and Digi-धन Vyapar ...</td>\n",
       "      <td>0</td>\n",
       "      <td>Govt’s Lucky Grahak Yojana and Digi-धन Vyapar ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>पाकिस्तानी सोशल मीडिया में मृत भारतीय सैनिकों ...</td>\n",
       "      <td>0</td>\n",
       "      <td>Share old photos of dead soldiers in Pakistani...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>भारतीय सेना द्वारा सफ़ेद झंडा लहराने के गलत दाव...</td>\n",
       "      <td>0</td>\n",
       "      <td>Old Share Video by Indian forces in Pakistan s...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                                           headline  \\\n",
       "0           0  Govt’s Lucky Grahak Yojana and Digi-धन Vyapar ...   \n",
       "1           1  पाकिस्तानी सोशल मीडिया में मृत भारतीय सैनिकों ...   \n",
       "2           2  भारतीय सेना द्वारा सफ़ेद झंडा लहराने के गलत दाव...   \n",
       "\n",
       "   original_english                                        translation  \n",
       "0                 0  Govt’s Lucky Grahak Yojana and Digi-धन Vyapar ...  \n",
       "1                 0  Share old photos of dead soldiers in Pakistani...  \n",
       "2                 0  Old Share Video by Indian forces in Pakistan s...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans = pd.read_csv(\"/Users/kruttikanadig/Documents/Tattle/data-experiments/headlines_with_translations.csv\")\n",
    "trans.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "translations_df = trans[trans[\"original_english\"] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "translations = list(translations_df[\"translation\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Text preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combining the headlines\n",
    "all_headlines = eng + translations\n",
    "# Tokenizing the headlines\n",
    "def sent_to_words(sentences):\n",
    "    for sentence in sentences:\n",
    "        yield (simple_preprocess(str(sentence), deacc = True))\n",
    "        \n",
    "all_tokens = list(sent_to_words(all_headlines))\n",
    "# Creating stop words list\n",
    "stop_words = stopwords.words(\"english\")\n",
    "# Adding domain words \n",
    "stop_words.extend([\"fake\", \"fact\", \"check\", \"checked\", \"factcheck\", \"news\", \"false\", \n",
    "                   \"falsely\", \"true\", \"truth\", \"viral\", \"video\", \"image\", \"picture\", \n",
    "                   \"photo\", \"claim\", \"claiming\", \"share\", \"clip\", \"misleading\"])\n",
    "# Stop word removal\n",
    "data_stopped = [[word for word in doc if word not in stop_words] for doc in all_tokens]\n",
    "# Creating bigrams\n",
    "bigram = gensim.models.Phrases(data_stopped, min_count=10)\n",
    "for idx in range(len(data_stopped)):\n",
    "    for token in bigram[data_stopped[idx]]:\n",
    "        if '_' in token:\n",
    "            # If token is bigram, add it to document\n",
    "            data_stopped[idx].append(token)\n",
    "            \n",
    "data_with_bigrams = data_stopped\n",
    "# Lemmatizing i.e. reducing words to their root form\n",
    "# Including only nouns as this improves both topic interpretability and coherence scores\n",
    "def lemmatization(docs, allowed_postags=[\"NOUN\", \"PROPN\"]):\n",
    "    nlp = spacy.load(\"en_core_web_sm\")\n",
    "    docs_out = []\n",
    "    for sent in docs:\n",
    "        doc = nlp(\" \".join(sent))\n",
    "        docs_out.append([token.lemma_ for token in doc if token.pos_ in allowed_postags]) \n",
    "    return docs_out\n",
    "\n",
    "data_lemmatized = lemmatization(data_with_bigrams, allowed_postags=[\"NOUN\", \"PROPN\"])\n",
    "\n",
    "# Removing any stopwords created because of lemmatization\n",
    "data_cleaned = [[word for word in doc if word not in stop_words] for doc in data_lemmatized]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Text transformation: creating a corpus of vectors\n",
    "\n",
    "Topic modelling with the Gensim library involves documents, corpus, vectors and bag of words. These are explained here - https://radimrehurek.com/gensim/auto_examples/core/run_core_concepts.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique tokens: 7561\n"
     ]
    }
   ],
   "source": [
    "# Creating a dictionary\n",
    "id2word = corpora.Dictionary(data_cleaned)\n",
    "# Creating a document-term matrix\n",
    "print('Number of unique tokens: %d' % len(id2word))\n",
    "id2word.filter_extremes(no_below = 20)\n",
    "# Creating a document-term matrix\n",
    "corpus = [id2word.doc2bow(doc) for doc in data_cleaned]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Selecting the number of topics \n",
    "\n",
    "Topic coherence is an evaluation metric for topic models that measures the degree of semantic similarity between high scoring words in the topic. The graph below helps find an appropriate 'k' number of topics to model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_coherence_values(dictionary, corpus, texts, limit, start=2, step=3):\n",
    "    \"\"\"\n",
    "    Compute c_v coherence for various number of topics\n",
    "\n",
    "    Parameters:\n",
    "    ----------\n",
    "    dictionary : Gensim dictionary\n",
    "    corpus : Gensim corpus\n",
    "    texts : List of input texts\n",
    "    limit : Max num of topics\n",
    "\n",
    "    Returns:\n",
    "    -------\n",
    "    model_list : List of LDA topic models\n",
    "    coherence_values : Coherence values corresponding to the LDA model with respective number of topics\n",
    "    \"\"\"\n",
    "    coherence_values = []\n",
    "    model_list = []\n",
    "    for num_topics in range(start, limit, step):\n",
    "        model=ldamodel.LdaModel(corpus=corpus, id2word=dictionary, num_topics=num_topics, passes=5, per_word_topics = True, chunksize=100, alpha='auto',\n",
    "    eta='auto', eval_every=1, random_state = 0)\n",
    "        model_list.append(model)\n",
    "        coherencemodel = CoherenceModel(model=model, texts=texts, dictionary=dictionary, coherence='c_v')\n",
    "        coherence_values.append(coherencemodel.get_coherence())\n",
    "\n",
    "    return model_list, coherence_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-07-31 15:33:37,460 [71642] WARNING  gensim.models.ldamodel:145: [JupyterRequire] updated prior is not positive\n",
      "2020-07-31 15:33:37,578 [71642] WARNING  gensim.models.ldamodel:145: [JupyterRequire] updated prior is not positive\n",
      "2020-07-31 15:33:37,677 [71642] WARNING  gensim.models.ldamodel:145: [JupyterRequire] updated prior is not positive\n",
      "2020-07-31 15:33:37,768 [71642] WARNING  gensim.models.ldamodel:145: [JupyterRequire] updated prior is not positive\n",
      "2020-07-31 15:34:02,166 [71642] WARNING  gensim.models.ldamodel:145: [JupyterRequire] updated prior is not positive\n",
      "2020-07-31 15:34:02,258 [71642] WARNING  gensim.models.ldamodel:145: [JupyterRequire] updated prior is not positive\n",
      "2020-07-31 15:34:21,686 [71642] WARNING  gensim.models.ldamodel:145: [JupyterRequire] updated prior is not positive\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_list, coherence_values = compute_coherence_values(dictionary=id2word, corpus=corpus, texts=data_cleaned, start=2, limit=20, step=1)\n",
    "# Show graph\n",
    "limit=20; start=2; step=1;\n",
    "x = range(start, limit, step)\n",
    "plt.plot(x, coherence_values)\n",
    "plt.xlabel(\"Num Topics\")\n",
    "plt.ylabel(\"Coherence score\")\n",
    "plt.legend((\"coherence_values\"), loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Building the topic model\n",
    "\n",
    "We choose k=7 as k=11 produced some topics which were not as meaningful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0,\n",
      "  '0.094*\"bjp\" + 0.057*\"people\" + 0.057*\"medium\" + 0.044*\"modi\" + '\n",
      "  '0.038*\"social_media\" + 0.036*\"temple\" + 0.032*\"verification\" + '\n",
      "  '0.030*\"state\" + 0.027*\"stock\" + 0.026*\"statement\"'),\n",
      " (1,\n",
      "  '0.123*\"virus\" + 0.066*\"box\" + 0.064*\"corona_virus\" + 0.056*\"year\" + '\n",
      "  '0.041*\"coronavirus\" + 0.040*\"student\" + 0.039*\"coro\" + 0.035*\"china\" + '\n",
      "  '0.032*\"message\" + 0.031*\"girl\"'),\n",
      " (2,\n",
      "  '0.101*\"india\" + 0.047*\"post\" + 0.041*\"muslim\" + 0.041*\"country\" + '\n",
      "  '0.041*\"lockdown\" + 0.036*\"rally\" + 0.033*\"riot\" + 0.030*\"president\" + '\n",
      "  '0.027*\"house\" + 0.025*\"shah\"'),\n",
      " (3,\n",
      "  '0.140*\"police\" + 0.089*\"woman\" + 0.081*\"name\" + 0.049*\"man\" + 0.049*\"child\" '\n",
      "  '+ 0.038*\"leader\" + 0.028*\"worker\" + 0.027*\"rumor\" + 0.026*\"khan\" + '\n",
      "  '0.025*\"death\"'),\n",
      " (4,\n",
      "  '0.090*\"gandhi\" + 0.069*\"minister\" + 0.041*\"election\" + 0.041*\"event\" + '\n",
      "  '0.040*\"rahul_gandhi\" + 0.035*\"prime_minister\" + 0.034*\"color\" + '\n",
      "  '0.032*\"body\" + 0.028*\"vote\" + 0.026*\"communal_color\"'),\n",
      " (5,\n",
      "  '0.058*\"rape\" + 0.051*\"time\" + 0.037*\"army\" + 0.036*\"evidence\" + '\n",
      "  '0.036*\"food\" + 0.030*\"mock\" + 0.030*\"mock_drill\" + 0.028*\"drill\" + '\n",
      "  '0.028*\"day\" + 0.026*\"footage\"'),\n",
      " (6,\n",
      "  '0.091*\"delhi\" + 0.081*\"caa\" + 0.055*\"protest\" + 0.038*\"government\" + '\n",
      "  '0.038*\"person\" + 0.035*\"attack\" + 0.032*\"congress\" + 0.030*\"kashmir\" + '\n",
      "  '0.030*\"money\" + 0.028*\"pradesh\"')]\n"
     ]
    }
   ],
   "source": [
    "lda_model = ldamodel.LdaModel(corpus = corpus, num_topics = 7, id2word = id2word, chunksize=100, alpha='auto',\n",
    "    eta='auto', passes = 5, random_state = 0)\n",
    "\n",
    "# Printing the topic-word probabilities\n",
    "pprint(lda_model.print_topics())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Topic interpretation\n",
    "\n",
    "The above topic-word probability distributions can be interpreted as 7 topics or themes that describe the story headlines:\n",
    "\n",
    "- BJP, Modi & the media\n",
    "- Coronavirus\n",
    "- General national politics\n",
    "- Identity / people\n",
    "- Rahul Gandhi, Congress & national politics\n",
    "- Law enforcement & crime\n",
    "- Citizenship act protests & different states "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Interactive model visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-07-31 16:01:26,133 [71642] WARNING  py.warnings:110: [JupyterRequire] /Users/kruttikanadig/anaconda3/lib/python3.7/site-packages/pyLDAvis/_prepare.py:257: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  return pd.concat([default_term_info] + list(topic_dfs))\n",
      "\n"
     ]
    },
    {
     "data": {
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       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el716421126754284168406432706\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el716421126754284168406432706_data = {\"mdsDat\": {\"x\": [-0.10643939197700908, 0.31954374745778824, 0.12478272719129962, -0.1543674630898472, -0.2592951667717977, 0.08504962228562218, -0.009274075096055929], \"y\": [-0.08929579251001356, -0.20818560896247804, 0.4137491856095359, -0.046964304894443566, -0.0032176908115674014, -0.03504854977268655, -0.031037238658346802], \"topics\": [1, 2, 3, 4, 5, 6, 7], \"cluster\": [1, 1, 1, 1, 1, 1, 1], \"Freq\": [18.612733840942383, 17.317588806152344, 15.619277954101562, 13.765599250793457, 13.326069831848145, 11.678223609924316, 9.680509567260742]}, \"tinfo\": {\"Category\": [\"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", 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\"bangladesh\", \"bank\", \"baton\", \"bihar\", \"bill\", \"bird\", \"bjp\", \"bjp_leader\", \"body\", \"bomb\", \"bottle\", \"box\", \"boy\", \"caa\", \"caa_protest\", \"campaign\", \"case\", \"cctv\", \"celebration\", \"chief_minister\", \"child\", \"china\", \"citizenship\", \"color\", \"comment\", \"communal\", \"communal_color\", \"community\", \"company\", \"congress\", \"context\", \"coro\", \"coro\", \"coro\", \"coro\", \"coro\", \"coro\", \"coro\", \"corona_virus\", \"corona_virus\", \"corona_virus\", \"corona_virus\", \"corona_virus\", \"corona_virus\", \"corona_virus\", \"coronavirus\", \"country\", \"crore\", \"czech\", \"czech\", \"data_verification\", \"data_verification\", \"datum\", \"daughter\", \"day\", \"death\", \"delhi\", \"delhi_police\", \"delhi_riot\", \"disease\", \"doctor\", \"drill\", \"drug\", \"election\", \"event\", \"evidence\", \"facebook\", \"family\", \"film\", \"fire\", \"flag\", \"food\", \"foot\", \"footage\", \"force\", \"game\", \"gandhi\", \"girl\", \"government\", \"group\", \"gujarat\", \"hand\", \"health\", \"hindus\", \"home\", \"hospital\", \"hotel\", \"house\", \"husband\", \"incident\", \"india\", \"indian_army\", \"infection\", \"information\", \"injury\", \"islam\", \"italy\", \"jamia\", \"jnu\", \"kashmir\", \"kejriwal\", \"khan\", \"kill\", \"kolkata\", \"kumar\", \"law\", \"leader\", \"lion\", \"list\", \"lockdown\", \"maharashtra\", \"man\", \"medium\", \"member\", \"message\", \"minister\", \"ministry\", \"mla\", \"mob\", \"mock\", \"mock_drill\", \"modi\", \"money\", \"mosque\", \"mother\", \"movie\", \"mumbai\", \"murder\", \"muslim\", \"muslim_man\", \"name\", \"nehru\", \"newspaper\", \"note\", \"nothing\", \"nrc\", \"number\", \"old_photo\", \"old_photos\", \"opposition\", \"order\", \"pack\", \"pack\", \"pack\", \"part\", \"party\", \"patient\", \"people\", \"performance\", \"person\", \"photograph\", \"photoshop\", \"police\", \"policeman\", \"post\", \"poster\", \"pradesh\", \"president\", \"prime_minister\", \"priyanka\", \"protest\", \"protester\", \"rahul_gandhi\", \"rally\", \"ram\", \"rape\", \"report\", \"republic\", \"riot\", \"road\", \"rss\", \"rumor\", \"run\", \"scene\", \"shah\", \"shaheen_bagh\", \"sharing\", \"show\", \"slogan\", \"social_media\", \"soldier\", \"sonia\", \"speech\", \"sri\", \"state\", \"statement\", \"stock\", \"stone\", \"story\", \"strike\", \"student\", \"suicide\", \"support\", \"temple\", \"time\", \"train\", \"trump\", \"tv\", \"tweet\", \"uttar\", \"vaccine\", \"verification\", \"victim\", \"violence\", \"virus\", \"vote\", \"water\", \"way\", \"wife\", \"woman\", \"worker\", \"world\", \"wrong\", \"year\", \"year_old\", \"yogi\", \"youth\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [1, 7, 4, 3, 2, 5, 6]};\n",
       "\n",
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       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
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       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el716421126754284168406432706\", ldavis_el716421126754284168406432706_data);\n",
       "   }(LDAvis);\n",
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       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el716421126754284168406432706\", ldavis_el716421126754284168406432706_data);\n",
       "      });\n",
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       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el716421126754284168406432706\", ldavis_el716421126754284168406432706_data);\n",
       "            })\n",
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       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
       "topic                                                \n",
       "0     -0.106439 -0.089296       1        1  18.612734\n",
       "6      0.319544 -0.208186       2        1  17.317589\n",
       "3      0.124783  0.413749       3        1  15.619278\n",
       "2     -0.154367 -0.046964       4        1  13.765599\n",
       "1     -0.259295 -0.003218       5        1  13.326070\n",
       "4      0.085050 -0.035049       6        1  11.678224\n",
       "5     -0.009274 -0.031037       7        1   9.680510, topic_info=    Category        Freq          Term       Total  loglift  logprob\n",
       "14   Default  869.000000        police  869.000000  30.0000  30.0000\n",
       "344  Default  649.000000         virus  649.000000  29.0000  29.0000\n",
       "70   Default  698.000000           bjp  698.000000  28.0000  28.0000\n",
       "49   Default  553.000000         india  553.000000  27.0000  27.0000\n",
       "199  Default  625.000000         delhi  625.000000  26.0000  26.0000\n",
       "102  Default  553.000000         woman  553.000000  25.0000  25.0000\n",
       "584  Default  556.000000           caa  556.000000  24.0000  24.0000\n",
       "337  Default  500.000000          name  500.000000  23.0000  23.0000\n",
       "2    Default  419.000000        gandhi  419.000000  22.0000  22.0000\n",
       "406  Default  352.000000           box  352.000000  21.0000  21.0000\n",
       "115  Default  421.000000        people  421.000000  20.0000  20.0000\n",
       "6    Default  419.000000        medium  419.000000  19.0000  19.0000\n",
       "94   Default  322.000000      minister  322.000000  18.0000  18.0000\n",
       "79   Default  377.000000       protest  377.000000  17.0000  17.0000\n",
       "605  Default  349.000000  corona_virus  349.000000  16.0000  16.0000\n",
       "53   Default  298.000000          year  298.000000  15.0000  15.0000\n",
       "8    Default  305.000000           man  305.000000  14.0000  14.0000\n",
       "10   Default  304.000000         child  304.000000  13.0000  13.0000\n",
       "55   Default  328.000000          modi  328.000000  12.0000  12.0000\n",
       "276  Default  225.000000          rape  225.000000  11.0000  11.0000\n",
       "30   Default  257.000000          post  257.000000  10.0000  10.0000\n",
       "7    Default  282.000000  social_media  282.000000   9.0000   9.0000\n",
       "155  Default  196.000000          time  196.000000   8.0000   8.0000\n",
       "208  Default  264.000000    government  264.000000   7.0000   7.0000\n",
       "387  Default  260.000000        person  260.000000   6.0000   6.0000\n",
       "187  Default  226.000000        muslim  226.000000   5.0000   5.0000\n",
       "345  Default  225.000000       country  225.000000   4.0000   4.0000\n",
       "166  Default  266.000000        temple  266.000000   3.0000   3.0000\n",
       "580  Default  224.000000      lockdown  224.000000   2.0000   2.0000\n",
       "590  Default  219.000000   coronavirus  219.000000   1.0000   1.0000\n",
       "..       ...         ...           ...         ...      ...      ...\n",
       "276   Topic7  224.796127          rape  225.603607   2.3315  -2.8396\n",
       "155   Topic7  195.884521          time  196.692596   2.3309  -2.9773\n",
       "27    Topic7  140.460602          army  141.266968   2.3293  -3.3099\n",
       "516   Topic7  136.737335          food  137.545776   2.3292  -3.3368\n",
       "540   Topic7  136.784363      evidence  137.594116   2.3292  -3.3364\n",
       "90    Topic7  116.528458          mock  117.335144   2.3282  -3.4967\n",
       "91    Topic7  115.924843    mock_drill  116.731506   2.3281  -3.5019\n",
       "32    Topic7  107.501793         drill  108.308594   2.3276  -3.5773\n",
       "227   Topic7  106.136368           day  106.943466   2.3275  -3.5901\n",
       "137   Topic7  100.688255       footage  101.497696   2.3270  -3.6428\n",
       "295   Topic7   92.291595        slogan   93.098595   2.3263  -3.7299\n",
       "323   Topic7   85.371613       soldier   86.178192   2.3257  -3.8078\n",
       "25    Topic7   83.638802   maharashtra   84.445839   2.3255  -3.8283\n",
       "318   Topic7   80.434387         crore   81.241730   2.3251  -3.8674\n",
       "38    Topic7   78.800468   indian_army   79.606918   2.3249  -3.8879\n",
       "539   Topic7   75.742424       nothing   76.550507   2.3244  -3.9275\n",
       "457   Topic7   70.263252          bomb   71.071678   2.3236  -4.0026\n",
       "19    Topic7   63.239231         kumar   64.046669   2.3224  -4.1079\n",
       "466   Topic7   63.014523          game   63.821720   2.3223  -4.1114\n",
       "207   Topic7   60.761562           run   61.572266   2.3218  -4.1479\n",
       "240   Topic7   58.363277           air   59.169617   2.3213  -4.1881\n",
       "125   Topic7   57.906635          lion   58.715992   2.3212  -4.1960\n",
       "284   Topic7   57.384094        strike   58.190937   2.3211  -4.2050\n",
       "569   Topic7   56.239906         april   57.048744   2.3208  -4.2252\n",
       "242   Topic7   55.588783   celebration   56.397457   2.3206  -4.2368\n",
       "57    Topic7   55.047272       account   55.853729   2.3205  -4.2466\n",
       "89    Topic7   54.277214         force   55.083832   2.3203  -4.2607\n",
       "243   Topic7   54.127289      republic   54.934662   2.3202  -4.2635\n",
       "168   Topic7   52.506882         nehru   53.313641   2.3198  -4.2939\n",
       "433   Topic7   44.133957      facebook   44.940491   2.3169  -4.4676\n",
       "\n",
       "[245 rows x 6 columns], token_table=      Topic      Freq             Term\n",
       "term                                  \n",
       "57        7  0.984715          account\n",
       "240       7  0.980233              air\n",
       "515       2  0.986913          airport\n",
       "84        4  0.989155             amit\n",
       "85        4  0.985122        amit_shah\n",
       "112       3  0.990018            angle\n",
       "566       4  0.993963             anil\n",
       "569       7  0.981617            april\n",
       "27        7  0.991031             army\n",
       "459       1  0.989012  arvind_kejriwal\n",
       "198       3  0.993268            assam\n",
       "22        2  0.995714           attack\n",
       "141       6  0.986512             baby\n",
       "591       2  0.993238             bagh\n",
       "75        2  0.988095       bangladesh\n",
       "48        5  0.990490             bank\n",
       "589       3  0.987032            baton\n",
       "109       3  0.993223            bihar\n",
       "254       6  0.981802             bill\n",
       "286       1  0.981399             bird\n",
       "70        1  0.998644              bjp\n",
       "95        3  0.987139       bjp_leader\n",
       "274       6  0.992159             body\n",
       "457       7  0.984921             bomb\n",
       "572       1  0.980700           bottle\n",
       "406       5  0.996154              box\n",
       "281       2  0.992300              boy\n",
       "584       2  0.998763              caa\n",
       "585       2  0.991705      caa_protest\n",
       "391       6  0.990963         campaign\n",
       "...     ...       ...              ...\n",
       "282       4  0.994741            stone\n",
       "388       1  0.987884            story\n",
       "284       7  0.979534           strike\n",
       "234       5  0.994023          student\n",
       "148       6  0.989632          suicide\n",
       "398       1  0.995086          support\n",
       "166       1  0.996013           temple\n",
       "155       7  0.996479             time\n",
       "293       4  0.980533            train\n",
       "157       6  0.990830            trump\n",
       "246       4  0.996166               tv\n",
       "4         1  0.996665            tweet\n",
       "546       2  0.984192            uttar\n",
       "514       3  0.995766          vaccine\n",
       "456       1  0.995989     verification\n",
       "277       3  0.984652           victim\n",
       "309       3  0.990713         violence\n",
       "344       5  0.999099            virus\n",
       "371       6  0.995525             vote\n",
       "251       4  0.986529            water\n",
       "552       5  0.982798              way\n",
       "56        6  0.994564             wife\n",
       "102       3  0.998326            woman\n",
       "73        3  0.993607           worker\n",
       "52        5  0.990083            world\n",
       "489       5  0.987154            wrong\n",
       "53        5  0.998282             year\n",
       "308       5  0.985774         year_old\n",
       "334       3  0.983392             yogi\n",
       "29        2  0.992696            youth\n",
       "\n",
       "[231 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[1, 7, 4, 3, 2, 5, 6])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyLDAvis.enable_notebook()\n",
    "vis = pyLDAvis.gensim.prepare(lda_model, corpus, id2word, sort_topics=True, n_jobs=1)\n",
    "vis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "lda_model.save(\"lda.model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "lda_model = ldamodel.LdaModel.load(\"lda.model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<gensim.models.ldamodel.LdaModel at 0x1a3acfe0f0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "pyLDAvis.save_html(vis, \"lda_model.html\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "pyLDAvis.save_json(vis, \"lda_model.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10. N-gram analysis of headlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Redoing text processing with adjectives, verbs and adverbs included\n",
    "def lemmatization(docs, allowed_postags=[\"NOUN\", \"PROPN\", \"VERB\", \"ADJ\", \"ADV\"]):\n",
    "    nlp = spacy.load(\"en_core_web_sm\")\n",
    "    docs_out = []\n",
    "    for sent in docs:\n",
    "        doc = nlp(\" \".join(sent))\n",
    "        docs_out.append([token.lemma_ for token in doc if token.pos_ in allowed_postags]) \n",
    "    return docs_out\n",
    "\n",
    "data_lemmatized = lemmatization(data_with_bigrams, allowed_postags=[\"NOUN\", \"PROPN\", \"VERB\", \"ADJ\", \"ADV\"])\n",
    "\n",
    "# Removing any stopwords created because of lemmatization\n",
    "data_cleaned = [[word for word in doc if word not in stop_words] for doc in data_lemmatized]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot frequency distribution of most common words in the headlines\n",
    "all_sents = [\" \".join(doc) for doc in data_cleaned]\n",
    "tokenizer = regextoken(\"[a-zA-Z0-9]+\")\n",
    "all_words = tokenizer.tokenize(str(all_sents))\n",
    "fd = FreqDist()\n",
    "for word in all_words:\n",
    "    fd[word] += 1\n",
    "    \n",
    "# Plotting the top 50 most frequent words\n",
    "# plt.figure(figsize = (15, 10))\n",
    "# fd.plot(50)\n",
    "# plt.show()\n",
    "fd_sorted={k: v for k, v in sorted(fd.items(), key=lambda item: item[1], reverse=True)}\n",
    "fd_sorted=dict(list(fd_sorted.items())[0: 100])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15, 10))\n",
    "plt.suptitle(\"Top words in 13,539 story headlines on Indian factchecking sites (as of May 2020)\", fontsize= 15)\n",
    "plt.title(\"source: Tattle archive\", fontdict = {'fontsize': 12})\n",
    "plt.xlabel(xlabel = \"Word\", fontdict = {'fontsize': 13})\n",
    "plt.ylabel(ylabel = \"Count\", fontdict = {'fontsize': 13})\n",
    "plt.xticks(rotation=90, size =11)\n",
    "plt.yticks(np.arange(0, 2000, 100))\n",
    "plt.plot(fd_sorted.keys(), fd_sorted.values())\n",
    "plt.grid()\n",
    "plt.tight_layout(pad=4)\n",
    "plt.savefig(\"word_plot.png\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 516,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Plot frequency distribution of most common word-pairs in the headlines\n",
    "bigrams_list=[]\n",
    "for idx in range(len(data_cleaned)):\n",
    "    for token in bigram[data_cleaned[idx]]:\n",
    "        if '_' in token:\n",
    "            # If token is bigram, add it to document\n",
    "            bigrams_list.append(token)\n",
    "\n",
    "fd = FreqDist()\n",
    "for b in bigrams_list:\n",
    "    fd[b] += 1\n",
    "    \n",
    "fd_sorted={k: v for k, v in sorted(fd.items(), key=lambda item: item[1], reverse=True)}\n",
    "fd_sorted=dict(list(fd_sorted.items())[0: 100])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 518,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15, 10))\n",
    "plt.suptitle(\"Top bigrams in 13,539 story headlines on Indian factchecking sites (as of May 2020)\", fontsize= 15)\n",
    "plt.title(\"source: Tattle archive\", fontdict = {'fontsize': 12})\n",
    "plt.xlabel(xlabel = \"Bigram\", fontdict = {'fontsize': 13})\n",
    "plt.ylabel(ylabel = \"Count\", fontdict = {'fontsize': 13})\n",
    "plt.xticks(rotation=90, size =9)\n",
    "plt.yticks(np.arange(0, 800, 50))\n",
    "plt.plot(fd_sorted.keys(), fd_sorted.values())\n",
    "plt.grid()\n",
    "plt.tight_layout(pad=4)\n",
    "plt.savefig(\"bigram_plot.png\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part Two: Topic Modelling on body text\n",
    "## 11. Text preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract body text from fact checked stories\n",
    "\n",
    "def get_story_content(docs):\n",
    "    if len(eval(docs)) > 0:\n",
    "        return eval(docs)[0][\"content\"]\n",
    "    else:\n",
    "        return docs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"content\"] = df[\"docs\"].map(get_story_content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    “बिना कुछ कहे, सब कुछ कह दिया। (Says everythin...\n",
       "1    A still of what appears to be a promo for an A...\n",
       "2    A video showing two men in khaki uniform invol...\n",
       "Name: content, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"content\"][:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save stories in a variable\n",
    "raw_stories = df[\"content\"].values.tolist()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unlike non-English story headlines which had to be translated, the non-English bits in body text are already translated within the text, so we can drop those bits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping non-English lines\n",
    "def remove_noneng(doc):\n",
    "    return re.sub(re.compile(\"[^\\x00-\\x7F]\"), \" \", str(doc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_stories = list(map(remove_noneng, raw_stories))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tokenizing the stories\n",
    "def sent_to_words(sentences):\n",
    "    for sentence in sentences:\n",
    "        yield (simple_preprocess(str(sentence), deacc = True))\n",
    "        \n",
    "all_tokens = list(sent_to_words(raw_stories))\n",
    "# Creating stop words list\n",
    "stop_words = stopwords.words(\"english\")\n",
    "# Adding domain words \n",
    "stop_words.extend([\"fake\", \"fact\", \"check\", \"checked\", \"factcheck\", \"news\", \"false\", \n",
    "                   \"falsely\", \"true\", \"truth\", \"viral\", \"video\", \"image\", \"picture\", \n",
    "                   \"photo\", \"claim\", \"claiming\", \"share\", \"clip\", \"misleading\"])\n",
    "# noise words - \"adsbygoogle\",\"window\", \"ad\", \"data\", \"td_screen_width\", \"push\", \"article\", \"style\",\n",
    "#                    \"width\",\"height\",\n",
    "#                   \"client\", \"slot\", \"inline\", \"display\", \"class\", \"block\", \"ins_class\", \"block_px\",\"ca_pub\"]\n",
    "# Stop word removal\n",
    "data_stopped = [[word for word in doc if word not in stop_words] for doc in all_tokens]\n",
    "# Creating bigrams\n",
    "bigram = gensim.models.Phrases(data_stopped, min_count=10)\n",
    "for idx in range(len(data_stopped)):\n",
    "    for token in bigram[data_stopped[idx]]:\n",
    "        if '_' in token:\n",
    "            # If token is bigram, add it to document\n",
    "            data_stopped[idx].append(token)\n",
    "            \n",
    "data_with_bigrams = data_stopped\n",
    "# Lemmatizing i.e. reducing words to their root form\n",
    "# Including only nouns as this improves both topic interpretability and coherence scores\n",
    "def lemmatization(docs, allowed_postags=[\"NOUN\", \"PROPN\"]):\n",
    "    nlp = spacy.load(\"en_core_web_sm\")\n",
    "    docs_out = []\n",
    "    for sent in docs:\n",
    "        doc = nlp(\" \".join(sent))\n",
    "        docs_out.append([token.lemma_ for token in doc if token.pos_ in allowed_postags]) \n",
    "    return docs_out\n",
    "\n",
    "data_lemmatized = lemmatization(data_with_bigrams, allowed_postags=[\"NOUN\", \"PROPN\"])\n",
    "\n",
    "# Removing any stopwords created because of lemmatization\n",
    "data_cleaned = [[word for word in doc if word not in stop_words] for doc in data_lemmatized]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 12. Text Transformation: Creating a corpus of vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique tokens: 42992\n"
     ]
    }
   ],
   "source": [
    "# Creating a dictionary\n",
    "id2word = corpora.Dictionary(data_cleaned)\n",
    "# Creating a document-term matrix\n",
    "print('Number of unique tokens: %d' % len(id2word))\n",
    "id2word.filter_extremes(no_below = 20)\n",
    "# Creating a document-term matrix\n",
    "corpus = [id2word.doc2bow(doc) for doc in data_cleaned]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 13. Selecting the number of topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-18 01:50:56,983 [10356] WARNING  gensim.models.ldamodel:145: [JupyterRequire] updated prior is not positive\n",
      "2020-05-18 01:50:57,267 [10356] WARNING  gensim.models.ldamodel:145: [JupyterRequire] updated prior is not positive\n",
      "2020-05-18 01:50:57,545 [10356] WARNING  gensim.models.ldamodel:145: [JupyterRequire] updated prior is not positive\n",
      "2020-05-18 01:50:57,826 [10356] WARNING  gensim.models.ldamodel:145: [JupyterRequire] updated prior is not positive\n",
      "2020-05-18 01:50:58,115 [10356] WARNING  gensim.models.ldamodel:145: [JupyterRequire] updated prior is not positive\n",
      "2020-05-18 01:50:58,363 [10356] WARNING  gensim.models.ldamodel:145: [JupyterRequire] updated prior is not positive\n",
      "2020-05-18 01:50:58,609 [10356] WARNING  gensim.models.ldamodel:145: [JupyterRequire] updated prior is not positive\n",
      "2020-05-18 01:52:35,939 [10356] WARNING  gensim.models.ldamodel:145: [JupyterRequire] updated prior is not positive\n",
      "2020-05-18 01:52:36,298 [10356] WARNING  gensim.models.ldamodel:145: [JupyterRequire] updated prior is not positive\n",
      "2020-05-18 01:52:36,713 [10356] WARNING  gensim.models.ldamodel:145: [JupyterRequire] updated prior is not positive\n",
      "2020-05-18 01:54:28,330 [10356] WARNING  gensim.models.ldamodel:145: [JupyterRequire] updated prior is not positive\n",
      "2020-05-18 01:54:29,045 [10356] WARNING  gensim.models.ldamodel:145: [JupyterRequire] updated prior is not positive\n",
      "2020-05-18 01:56:42,558 [10356] WARNING  gensim.models.ldamodel:145: [JupyterRequire] updated prior is not positive\n",
      "2020-05-18 01:58:04,733 [10356] WARNING  gensim.models.ldamodel:145: [JupyterRequire] updated prior is not positive\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_list, coherence_values = compute_coherence_values(dictionary=id2word, corpus=corpus, texts=data_cleaned, start=2, limit=20, step=1)\n",
    "# Show graph\n",
    "limit=20; start=2; step=1;\n",
    "x = range(start, limit, step)\n",
    "plt.plot(x, coherence_values)\n",
    "plt.xlabel(\"Num Topics\")\n",
    "plt.ylabel(\"Coherence score\")\n",
    "plt.legend((\"coherence_values\"), loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14. Building the topic model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-18 02:24:03,780 [10356] WARNING  gensim.models.ldamodel:145: [JupyterRequire] updated prior is not positive\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0,\n",
      "  '0.023*\"medium\" + 0.017*\"post\" + 0.015*\"tweet\" + 0.013*\"facebook\" + '\n",
      "  '0.013*\"time\" + 0.013*\"email\" + 0.012*\"india\" + 0.010*\"user\" + '\n",
      "  '0.010*\"people\" + 0.010*\"article\"'),\n",
      " (1,\n",
      "  '0.026*\"police\" + 0.023*\"woman\" + 0.021*\"man\" + 0.017*\"incident\" + '\n",
      "  '0.016*\"people\" + 0.015*\"report\" + 0.014*\"case\" + 0.013*\"child\" + '\n",
      "  '0.010*\"village\" + 0.010*\"delhi\"'),\n",
      " (2,\n",
      "  '0.031*\"minister\" + 0.023*\"bjp\" + 0.020*\"modi\" + 0.020*\"crore\" + '\n",
      "  '0.014*\"sabha\" + 0.014*\"scheme\" + 0.014*\"government\" + '\n",
      "  '0.014*\"prime_minister\" + 0.013*\"party\" + 0.013*\"rs_crore\"'),\n",
      " (3,\n",
      "  '0.030*\"india\" + 0.021*\"year\" + 0.021*\"government\" + 0.015*\"datum\" + '\n",
      "  '0.014*\"state\" + 0.013*\"coronavirus\" + 0.011*\"health\" + 0.011*\"country\" + '\n",
      "  '0.011*\"ministry\" + 0.009*\"world\"'),\n",
      " (4,\n",
      "  '0.061*\"adsbygoogle\" + 0.044*\"window\" + 0.035*\"ad\" + 0.035*\"data\" + '\n",
      "  '0.031*\"td_screen_width\" + 0.031*\"push\" + 0.019*\"article\" + 0.019*\"style\" + '\n",
      "  '0.018*\"width\" + 0.018*\"height\"')]\n"
     ]
    }
   ],
   "source": [
    "lda_model = ldamodel.LdaModel(corpus = corpus, num_topics = 5, id2word = id2word, chunksize=100, alpha='auto',\n",
    "    eta='auto', passes = 5, random_state = 0)\n",
    "\n",
    "# Printing the topic-word probabilities\n",
    "pprint(lda_model.print_topics())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15. Topic interpretation\n",
    "\n",
    "Without noise stopwords:\n",
    "\n",
    "1. Mediums of information / communication\n",
    "2. Identities\n",
    "3. National politics & government\n",
    "4. National-international politics & health / coronavirus\n",
    "5. Article metadata (noise)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "NM FACT CHECKERTrending Now\n",
      "\n",
      "Fact Checked: Abhinandan didn t say  BJP orchestrated Pulwama attack \n",
      "\n",
      "By News Mobile Fact Check Bureau -   May 13, 2019 \n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "ShareTweetGoogleWhatsappPrintMail\n",
      "\n",
      "\n",
      "Ever since Wing Commander Abhinandan came back from Pakistan after he was held captive, a range of claims surfaced on the internet.\n",
      "Another quote attributed to Abhinandan is making rounds on social media, according to which Abhinandan accused that the Pulwama attack was a BJP s well-planned strategy and attack on Pakistan was also fake so that PM Modi could win the elections.\n",
      "The text read:                                                                           ,                                                                                                  :         \n",
      "\n",
      "ALSO READ: Here s the truth about the picture of Narendra Modi with Indira \n",
      "\n",
      "\n",
      "\n",
      "\n",
      "(adsbygoogle = window.adsbygoogle || []).push({});\n",
      "At the time of filing this report, the above post was shared by over 26,000 Facebook users.\n",
      "ALSO READ: Here s the truth about the picture of Narendra Modi with Indira \n",
      "FACT CHECK\n",
      "Team of fact-checkers at NewsMobile checked the above post and found it to be FAKE.\n",
      "When we searched the above text, we didn t find any reliable source quoting Abhinandan with the above statement.\n",
      "\n",
      "Moreover, ever since Abhinandan returned from Pakistan, he has not given any interview or released any public statement.\n",
      "Hence, the viral post is FAKE.\n",
      "\n",
      "If you want to fact-check any story, WhatsApp it now on +91 88268 00707\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "FAKE NEWS BUSTER\n",
      "Name \n",
      "\n",
      "\n",
      "Email \n",
      "Phone \n",
      "\n",
      "\n",
      "Picture/video \n",
      "\n",
      "\n",
      "Picture/video url\n",
      "\n",
      "\n",
      "Description \n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Click here for Latest News updates and viral videos on our AI-powered smart news \n",
      "window._taboola = window._taboola || [];\n",
      "_taboola.push({mode:'thumbnails-b', container:'taboola-below-article', placement:'below-article', target_type: 'mix'});\n",
      "window._taboola = window._taboola || [];\n",
      "_taboola.push({mode:'thumbnails-b', container:'taboola-below-article-second', placement:'below-article-2nd', target_type: 'mix'});ShareTweetGoogleWhatsappPrintMail\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "var td_screen_width = window.innerWidth;\n",
      "\n",
      "                    if ( td_screen_width >= 1140 ) {\n",
      "                        /* large monitors */\n",
      "                        document.write('<ins class=\"adsbygoogle\" style=\"display:inline-block;width:728px;height:90px\" data-ad-client=\"ca-pub-9419436853807952\" data-ad-slot=\"1494347719\"></ins>');\n",
      "                        (adsbygoogle = window.adsbygoogle || []).push({});\n",
      "                    }\n",
      "            \n",
      "\t                    if ( td_screen_width >= 1019  && td_screen_width < 1140 ) {\n",
      "\t                        /* landscape tablets */\n",
      "                        document.write('<ins class=\"adsbygoogle\" style=\"display:inline-block;width:468px;height:60px\" data-ad-client=\"ca-pub-9419436853807952\" data-ad-slot=\"1494347719\"></ins>');\n",
      "\t                        (adsbygoogle = window.adsbygoogle || []).push({});\n",
      "\t                    }\n",
      "\t                \n",
      "                    if ( td_screen_width >= 768  && td_screen_width < 1019 ) {\n",
      "                        /* portrait tablets */\n",
      "                        document.write('<ins class=\"adsbygoogle\" style=\"display:inline-block;width:468px;height:60px\" data-ad-client=\"ca-pub-9419436853807952\" data-ad-slot=\"1494347719\"></ins>');\n",
      "                        (adsbygoogle = window.adsbygoogle || []).push({});\n",
      "                    }\n",
      "                \n",
      "                    if ( td_screen_width < 768 ) {\n",
      "                        /* Phones */\n",
      "                        document.write('<ins class=\"adsbygoogle\" style=\"display:inline-block;width:300px;height:250px\" data-ad-client=\"ca-pub-9419436853807952\" data-ad-slot=\"1494347719\"></ins>');\n",
      "                        (adsbygoogle = window.adsbygoogle || []).push({});\n",
      "                    }\n",
      "                \n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "TAGSAbhinandanairstrikebalakotBJPFact checkFact CheckerFakeImran KhanIndiamobilenewmobilenewsPakistanPulwama Attackwing commander \n",
      "Previous articleHome Ministry cancels registration of Infosys Foundation over FCRA violationNext articleHappy birthday sultry Sunny Leone, who came in and conquered the audiences News Mobile Fact Check Bureau  \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in raw_stories:\n",
    "    if \"adsbygoogle\" in i:\n",
    "        print(i)\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 16. Interactive model visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-17 21:33:41,337 [10356] WARNING  py.warnings:110: [JupyterRequire] /Users/kruttikanadig/anaconda3/lib/python3.7/site-packages/pyLDAvis/_prepare.py:257: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  return pd.concat([default_term_info] + list(topic_dfs))\n",
      "\n"
     ]
    },
    {
     "data": {
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\"journalist\", \"journalist\", \"kashmir\", \"kashmir\", \"kejriwal\", \"khan\", \"kumar\", \"lakh\", \"lakh\", \"lakh\", \"landscape\", \"level\", \"loan\", \"lockdown\", \"lok\", \"lok_sabha\", \"lpg\", \"man\", \"man\", \"man\", \"march\", \"march\", \"march\", \"march\", \"march\", \"mask\", \"medicine\", \"medium\", \"medium\", \"medium\", \"medium\", \"medium\", \"message\", \"message\", \"message\", \"message\", \"minister\", \"minister\", \"minister\", \"ministry\", \"mishra\", \"mob\", \"modi\", \"modi\", \"money\", \"money\", \"money\", \"mosque\", \"mumbai\", \"mumbai\", \"muslim\", \"muslim\", \"nadu\", \"name\", \"name\", \"name\", \"name\", \"name\", \"name_email\", \"narendra\", \"ncrb\", \"number\", \"number\", \"number\", \"outbreak\", \"outbreak\", \"outbreak\", \"page\", \"page\", \"page\", \"page\", \"party\", \"party\", \"patient\", \"people\", \"people\", \"people\", \"people\", \"people\", \"percentage\", \"period\", \"person\", \"person\", \"person\", \"photograph\", \"pic\", \"pic_twitter\", \"place\", \"place\", \"placement\", \"pm\", \"pm\", \"pm\", \"police\", \"policeman\", \"policy\", \"population\", \"post\", \"post\", \"post\", \"post\", \"post\", \"pradesh\", \"pradesh\", \"pradhan\", \"prayer\", \"price\", \"price\", \"prime_minister\", \"prime_minister\", \"product\", \"programme\", \"programme\", \"project\", \"project\", \"protest\", \"protest\", \"pub_data\", \"pub_data\", \"pub_data\", \"push\", \"push\", \"push_td_screen_width\", \"push_td_screen_width\", \"push_td_screen_width\", \"push_td_screen_width\", \"rahul_gandhi\", \"railway\", \"rajya\", \"rajya\", \"ram\", \"rape\", \"rate\", \"rbi\", \"receive_notification\", \"report\", \"report\", \"report\", \"report\", \"research\", \"reserve\", \"reserve\", \"resident\", \"rs\", \"rs_crore\", \"rs_lakh\", \"rss\", \"sabha\", \"scheme\", \"school\", \"school\", \"shri\", \"slot\", \"slot\", \"social_media\", \"social_media\", \"social_media\", \"social_media\", \"social_media\", \"source\", \"source\", \"source\", \"sri\", \"state\", \"state\", \"state\", \"state\", \"statement\", \"statement\", \"statement\", \"station\", \"story\", \"story\", \"story\", \"story\", \"story\", \"student\", \"study\", \"style\", \"style_display\", \"style_display\", \"style_display\", \"subscribe\", \"suicide\", \"survey\", \"system\", \"table\", \"tablet\", \"tablighi\", \"taboola\", \"taboola\", \"taboola\", \"taboola\", \"taboola_article\", \"taboola_article\", \"taboola_article\", \"taboola_article\", \"tamil_nadu\", \"target_type\", \"target_type\", \"target_type\", \"target_type\", \"td_screen_width\", \"td_screen_width\", \"technology\", \"temple\", \"thumbnail\", \"time\", \"time\", \"time\", \"time\", \"today\", \"today\", \"today\", \"today\", \"toilet\", \"treatment\", \"tv\", \"tweet\", \"tweet\", \"tweet\", \"twitter\", \"twitter\", \"twitter\", \"twitter\", \"twitter_handle\", \"url\", \"user\", \"user\", \"user\", \"user\", \"vehicle\", \"victim\", \"village\", \"village\", \"virus\", \"water\", \"water\", \"website\", \"website\", \"website\", \"website\", \"website\", \"width\", \"width\", \"width_px\", \"width_px\", \"width_px\", \"window\", \"window_adsbygoogle\", \"window_adsbygoogle\", \"window_adsbygoogle\", \"woman\", \"woman\", \"world\", \"year\", \"year\", \"year\", \"year\", \"yojana\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [1, 2, 5, 4, 3]};\n",
       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el103561127191730968809545510\", ldavis_el103561127191730968809545510_data);\n",
       "   }(LDAvis);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el103561127191730968809545510\", ldavis_el103561127191730968809545510_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el103561127191730968809545510\", ldavis_el103561127191730968809545510_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
       "topic                                                \n",
       "0      0.083221 -0.201960       1        1  29.357302\n",
       "1      0.125706 -0.202702       2        1  19.653313\n",
       "4     -0.441303  0.013160       3        1  18.976234\n",
       "3      0.092586  0.140499       4        1  17.807255\n",
       "2      0.139790  0.251002       5        1  14.205893, topic_info=     Category          Freq             Term         Total  loglift  logprob\n",
       "4765  Default  15460.000000      adsbygoogle  15460.000000  30.0000  30.0000\n",
       "422   Default  11120.000000           window  11120.000000  29.0000  29.0000\n",
       "0     Default   8922.000000               ad   8922.000000  28.0000  28.0000\n",
       "1142  Default   9366.000000             data   9366.000000  27.0000  27.0000\n",
       "4795  Default   7765.000000  td_screen_width   7765.000000  26.0000  26.0000\n",
       "2782  Default   7768.000000             push   7768.000000  25.0000  25.0000\n",
       "151   Default   6761.000000           police   6761.000000  24.0000  24.0000\n",
       "649   Default   6357.000000         minister   6357.000000  23.0000  23.0000\n",
       "599   Default   6187.000000            woman   6187.000000  22.0000  22.0000\n",
       "39    Default  13414.000000            india  13414.000000  21.0000  21.0000\n",
       "306   Default   7671.000000       government   7671.000000  20.0000  20.0000\n",
       "521   Default   4832.000000              bjp   4832.000000  19.0000  19.0000\n",
       "140   Default   6843.000000              man   6843.000000  18.0000  18.0000\n",
       "2270  Default   4668.000000            style   4668.000000  17.0000  17.0000\n",
       "4672  Default   4612.000000            width   4612.000000  16.0000  16.0000\n",
       "2047  Default   4565.000000           height   4565.000000  15.0000  15.0000\n",
       "3338  Default   4446.000000           client   4446.000000  14.0000  14.0000\n",
       "4438  Default   4442.000000             slot   4442.000000  13.0000  13.0000\n",
       "137   Default   4530.000000         incident   4530.000000  12.0000  12.0000\n",
       "4774  Default   4431.000000           inline   4431.000000  11.0000  11.0000\n",
       "4773  Default   4431.000000          data_ad   4431.000000  10.0000  10.0000\n",
       "1062  Default   3683.000000            crore   3683.000000   9.0000   9.0000\n",
       "2407  Default   4524.000000          display   4524.000000   8.0000   8.0000\n",
       "493   Default   3797.000000             modi   3797.000000   7.0000   7.0000\n",
       "67    Default   6024.000000            tweet   6024.000000   6.0000   6.0000\n",
       "2971  Default   4698.000000            class   4698.000000   5.0000   5.0000\n",
       "4739  Default   4075.000000        height_px   4075.000000   4.0000   4.0000\n",
       "4711  Default   4076.000000         width_px   4076.000000   3.0000   3.0000\n",
       "4236  Default   4677.000000            block   4677.000000   2.0000   2.0000\n",
       "4777  Default   3958.000000  ins_adsbygoogle   3958.000000   1.0000   1.0000\n",
       "...       ...           ...              ...           ...      ...      ...\n",
       "949    Topic5    659.770142          pradhan    660.356934   1.9506  -5.6542\n",
       "743    Topic5    657.762817          rs_lakh    658.348877   1.9506  -5.6573\n",
       "4171   Topic5    679.746765              lpg    680.365479   1.9506  -5.6244\n",
       "3630   Topic5    647.709900         kejriwal    648.306458   1.9506  -5.6727\n",
       "56     Topic5    626.100220     rahul_gandhi    626.702148   1.9506  -5.7066\n",
       "396    Topic5    602.558472        candidate    603.138794   1.9506  -5.7449\n",
       "2754   Topic5    601.703674             card    602.289185   1.9505  -5.7464\n",
       "716    Topic5    596.086182          finance    596.668213   1.9505  -5.7557\n",
       "575    Topic5    575.281799              rbi    575.862610   1.9505  -5.7913\n",
       "3641   Topic5    573.916199           income    574.503174   1.9505  -5.7936\n",
       "103    Topic5   2418.693604            party   2425.089600   1.9489  -4.3551\n",
       "658    Topic5   2592.607178   prime_minister   2604.036377   1.9471  -4.2857\n",
       "493    Topic5   3725.968018             modi   3797.546875   1.9325  -3.9230\n",
       "649    Topic5   5878.945312         minister   6357.256348   1.8733  -3.4670\n",
       "660    Topic5    888.938599          reserve    904.624023   1.9340  -5.3561\n",
       "521    Topic5   4241.005371              bjp   4832.003418   1.8211  -3.7936\n",
       "1310   Topic5    745.935547            rajya    761.585754   1.9307  -5.5315\n",
       "2948   Topic5   1838.735962        household   2188.830078   1.7772  -4.6293\n",
       "2097   Topic5    792.461731            price    822.311340   1.9145  -5.4710\n",
       "154    Topic5   2290.540771          pradesh   4256.163086   1.3319  -4.4096\n",
       "727    Topic5   1077.577515             lakh   1453.380981   1.6523  -5.1636\n",
       "306    Topic5   2593.107178       government   7671.018555   0.8669  -4.2855\n",
       "693    Topic5   1247.207764               pm   2044.886230   1.4571  -5.0175\n",
       "143    Topic5    947.333557            money   1190.189209   1.7233  -5.2925\n",
       "328    Topic5   1934.634399            state   6140.765625   0.7965  -4.5784\n",
       "309    Topic5   1060.270020            house   2348.702637   1.1562  -5.1798\n",
       "39     Topic5   1393.117432            india  13414.896484  -0.3133  -4.9068\n",
       "994    Topic5   1200.455322            delhi   5773.058594   0.3810  -5.0557\n",
       "73     Topic5   1011.386963          account   2344.045898   1.1110  -5.2270\n",
       "289    Topic5   1040.812866          village   3729.136230   0.6753  -5.1984\n",
       "\n",
       "[294 rows x 6 columns], token_table=      Topic      Freq                Term\n",
       "term                                     \n",
       "1958      5  0.999053                 aap\n",
       "73        1  0.548624             account\n",
       "73        4  0.019624             account\n",
       "73        5  0.431306             account\n",
       "829       1  0.999824               actor\n",
       "0         3  0.999038                  ad\n",
       "0         4  0.000897                  ad\n",
       "4764      1  0.000758             ad_slot\n",
       "4764      3  0.998929             ad_slot\n",
       "4764      5  0.000253             ad_slot\n",
       "4765      1  0.000194         adsbygoogle\n",
       "4765      3  0.999731         adsbygoogle\n",
       "4591      1  0.990114                afwa\n",
       "4591      2  0.008305                afwa\n",
       "4591      4  0.001846                afwa\n",
       "601       4  0.999577                 air\n",
       "5         1  0.999762                 alt\n",
       "1115      1  0.441684               april\n",
       "1115      2  0.142562               april\n",
       "1115      3  0.005384               april\n",
       "1115      4  0.195108               april\n",
       "1115      5  0.215135               april\n",
       "861       2  0.927898                area\n",
       "861       4  0.071945                area\n",
       "368       1  0.999993                army\n",
       "122       1  0.378085             article\n",
       "122       2  0.083305             article\n",
       "122       3  0.476015             article\n",
       "122       4  0.062652             article\n",
       "567       5  0.999559                bank\n",
       "...     ...       ...                 ...\n",
       "116       5  0.001997                user\n",
       "164       2  0.999059             vehicle\n",
       "190       2  0.998983              victim\n",
       "289       2  0.720810             village\n",
       "289       5  0.279153             village\n",
       "3114      2  0.999838               virus\n",
       "1521      2  0.077321               water\n",
       "1521      4  0.922522               water\n",
       "421       1  0.500608             website\n",
       "421       2  0.008699             website\n",
       "421       3  0.053778             website\n",
       "421       4  0.429036             website\n",
       "421       5  0.008304             website\n",
       "4672      3  0.999604               width\n",
       "4672      5  0.000217               width\n",
       "4711      3  0.999219            width_px\n",
       "4711      4  0.000245            width_px\n",
       "4711      5  0.000245            width_px\n",
       "422       3  0.999961              window\n",
       "4803      1  0.000759  window_adsbygoogle\n",
       "4803      3  0.998954  window_adsbygoogle\n",
       "4803      5  0.000253  window_adsbygoogle\n",
       "599       1  0.031998               woman\n",
       "599       2  0.968012               woman\n",
       "677       4  0.999637               world\n",
       "388       1  0.103351                year\n",
       "388       2  0.179576                year\n",
       "388       4  0.623053                year\n",
       "388       5  0.094023                year\n",
       "3307      5  0.998935              yojana\n",
       "\n",
       "[470 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[1, 2, 5, 4, 3])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyLDAvis.enable_notebook()\n",
    "vis = pyLDAvis.gensim.prepare(lda_model, corpus, id2word, sort_topics=True, n_jobs=1)\n",
    "vis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pyLDAvis' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-42ad2f0887b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpyLDAvis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_html\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'lda.html'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'pyLDAvis' is not defined"
     ]
    }
   ],
   "source": [
    "pyLDAvis.save_html(vis, 'lda.html')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 17. N-gram analysis of the complete text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Redoing text processing with adjectives, verbs and adverbs included\n",
    "def lemmatization(docs, allowed_postags=[\"NOUN\", \"PROPN\", \"VERB\", \"ADJ\", \"ADV\"]):\n",
    "    nlp = spacy.load(\"en_core_web_sm\")\n",
    "    docs_out = []\n",
    "    for sent in docs:\n",
    "        doc = nlp(\" \".join(sent))\n",
    "        docs_out.append([token.lemma_ for token in doc if token.pos_ in allowed_postags]) \n",
    "    return docs_out\n",
    "\n",
    "data_lemmatized = lemmatization(data_with_bigrams, allowed_postags=[\"NOUN\", \"PROPN\", \"VERB\", \"ADJ\", \"ADV\"])\n",
    "\n",
    "# Removing any stopwords created because of lemmatization\n",
    "data_cleaned = [[word for word in doc if word not in stop_words] for doc in data_lemmatized]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot frequency distribution of most common words in the headlines\n",
    "all_sents = [\" \".join(doc) for doc in data_cleaned]\n",
    "tokenizer = regextoken(\"[a-zA-Z0-9]+\")\n",
    "all_words = tokenizer.tokenize(str(all_sents))\n",
    "fd = FreqDist()\n",
    "for word in all_words:\n",
    "    fd[word] += 1\n",
    "    \n",
    "# Plotting the top 50 most frequent words\n",
    "# plt.figure(figsize = (15, 10))\n",
    "# fd.plot(50)\n",
    "# plt.show()\n",
    "fd_sorted={k: v for k, v in sorted(fd.items(), key=lambda item: item[1], reverse=True)}\n",
    "fd_sorted=dict(list(fd_sorted.items())[0: 100])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15, 10))\n",
    "plt.suptitle(\"Top words in 13,539 story headlines on Indian factchecking sites (as of May 2020)\", fontsize= 15)\n",
    "plt.title(\"source: Tattle archive\", fontdict = {'fontsize': 12})\n",
    "plt.xlabel(xlabel = \"Word\", fontdict = {'fontsize': 13})\n",
    "plt.ylabel(ylabel = \"Count\", fontdict = {'fontsize': 13})\n",
    "plt.xticks(rotation=90, size =11)\n",
    "plt.yticks(np.arange(0, 2000, 100))\n",
    "plt.plot(fd_sorted.keys(), fd_sorted.values())\n",
    "plt.grid()\n",
    "plt.tight_layout(pad=4)\n",
    "plt.savefig(\"word_plot_stories.png\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Plot frequency distribution of most common word-pairs in the headlines\n",
    "bigrams_list=[]\n",
    "for idx in range(len(data_cleaned)):\n",
    "    for token in bigram[data_cleaned[idx]]:\n",
    "        if '_' in token:\n",
    "            # If token is bigram, add it to document\n",
    "            bigrams_list.append(token)\n",
    "\n",
    "fd = FreqDist()\n",
    "for b in bigrams_list:\n",
    "    fd[b] += 1\n",
    "    \n",
    "fd_sorted={k: v for k, v in sorted(fd.items(), key=lambda item: item[1], reverse=True)}\n",
    "fd_sorted=dict(list(fd_sorted.items())[0: 100])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15, 10))\n",
    "plt.suptitle(\"Top bigrams in 13,539 story headlines on Indian factchecking sites (as of May 2020)\", fontsize= 15)\n",
    "plt.title(\"source: Tattle archive\", fontdict = {'fontsize': 12})\n",
    "plt.xlabel(xlabel = \"Bigram\", fontdict = {'fontsize': 13})\n",
    "plt.ylabel(ylabel = \"Count\", fontdict = {'fontsize': 13})\n",
    "plt.xticks(rotation=90, size =9)\n",
    "plt.yticks(np.arange(0, 800, 50))\n",
    "plt.plot(fd_sorted.keys(), fd_sorted.values())\n",
    "plt.grid()\n",
    "plt.tight_layout(pad=4)\n",
    "plt.savefig(\"bigram_plot_stories.png\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 18. Possible applications\n",
    "\n",
    "We have performed topic modelling and ngram analysis on the headlines and body text of 13,000+ fact-checking stories, including headlines that were originally in Indian languages. Some possible applications of this analysis are:\n",
    "\n",
    "- **Content selection**: Targeting content categories aligned with the produced topics / words while running social media scrapers\n",
    "- **Content filtering**: Using top words from topic model and ngram analysis along with image text-extraction tool to triage new content for the archive\n",
    "- **Content enrichment**: Offering some of these topics as tags / labels in the annotation UI\n",
    "- **Communications** around Tattle's archive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part Three: Modelling complete story text (headlines + body)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "def combine_text(df):\n",
    "    pat = re.compile(\"[^\\x00-\\x7F]\") # matches non-English characters\n",
    "    translator = Translator()\n",
    "    res = []\n",
    "    for h in df[\"headline\"]:\n",
    "        if pat.search(h):\n",
    "            res.append(translator.translate(h).text)\n",
    "            time.sleep(uniform(3,5))\n",
    "        else:\n",
    "            res.append(h)\n",
    "    df[\"eng_headline\"] = np.array(res)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = combine_text(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>{'name': 'Jignesh Patel', 'link': 'https://www...</td>\n",
       "      <td>October 23, 2019</td>\n",
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       "      <td>https://www.altnews.in/madhu-kishwar-tweets-ph...</td>\n",
       "      <td>“बिना कुछ कहे, सब कुछ कह दिया। (Says everythin...</td>\n",
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       "      <td>Madhu Kishwar tweets photoshopped image of Amu...</td>\n",
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       "      <td>{'name': 'Arjun Sidharth', 'link': 'https://ww...</td>\n",
       "      <td>October 23, 2019</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Photoshopped promo of ABP News over Chandrayaa...</td>\n",
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       "      <td>{'name': 'Arjun Sidharth', 'link': 'https://ww...</td>\n",
       "      <td>October 23, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>September 09, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': '7dedd34af16d42d7aa960f0f23b5dff8'...</td>\n",
       "      <td>altnews.in</td>\n",
       "      <td>Video of scuffle between men in khaki uniform ...</td>\n",
       "      <td>801429d1458a4802b65fd4b165ef9068</td>\n",
       "      <td>https://www.altnews.in/video-of-scuffle-betwee...</td>\n",
       "      <td>A video showing two men in khaki uniform invol...</td>\n",
       "      <td>A video showing two men in khaki uniform invol...</td>\n",
       "      <td>Video of scuffle between men in khaki uniform ...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>5db02a4d88ab2b22f0a59431</td>\n",
       "      <td>{'name': 'Pooja Chaudhuri', 'link': 'https://w...</td>\n",
       "      <td>October 23, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>September 08, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': 'dc281d88dc1648d49968e42cc06e729a'...</td>\n",
       "      <td>altnews.in</td>\n",
       "      <td>Mentally unstable man beaten by mob in Himacha...</td>\n",
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       "      <td>https://www.altnews.in/mentally-unstable-man-b...</td>\n",
       "      <td>A video of a man hounded by a mob over suspici...</td>\n",
       "      <td>A video of a man hounded by a mob over suspici...</td>\n",
       "      <td>Mentally unstable man beaten by mob in Himacha...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5db02a4e88ab2b22f0a59432</td>\n",
       "      <td>{'name': 'Pooja Chaudhuri', 'link': 'https://w...</td>\n",
       "      <td>October 23, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>September 07, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': '6f74e996f91546d5a19c6a7507b93021'...</td>\n",
       "      <td>altnews.in</td>\n",
       "      <td>Digitally created artwork shared as Hurricane ...</td>\n",
       "      <td>2c2eeccffec54c68a996aee315bfbac2</td>\n",
       "      <td>https://www.altnews.in/digitally-created-artwo...</td>\n",
       "      <td>“A view of Hurricane Dorian from the coasts of...</td>\n",
       "      <td>A view of Hurricane Dorian from the coasts of...</td>\n",
       "      <td>Digitally created artwork shared as Hurricane ...</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>5db02a4e88ab2b22f0a59433</td>\n",
       "      <td>{'name': 'Arjun Sidharth', 'link': 'https://ww...</td>\n",
       "      <td>October 23, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>September 07, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': '989767dcc82549119ca29a76117b7d8e'...</td>\n",
       "      <td>altnews.in</td>\n",
       "      <td>Unrelated video from Rajasthan shared as polic...</td>\n",
       "      <td>64f86cb1debf41119260d3cd3d650694</td>\n",
       "      <td>https://www.altnews.in/unrelated-video-from-ra...</td>\n",
       "      <td>Penalties for traffic violations have risen st...</td>\n",
       "      <td>Penalties for traffic violations have risen st...</td>\n",
       "      <td>Unrelated video from Rajasthan shared as polic...</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>5db02a4f88ab2b22f0a59434</td>\n",
       "      <td>{'name': 'Jignesh Patel', 'link': 'https://www...</td>\n",
       "      <td>October 23, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>September 07, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': '783d336547a842d0826015bf0e90566e'...</td>\n",
       "      <td>altnews.in</td>\n",
       "      <td>Physically-challenged man beaten to death in U...</td>\n",
       "      <td>34f8e7d512a24bc88de866d730219f1e</td>\n",
       "      <td>https://www.altnews.in/physically-challenged-m...</td>\n",
       "      <td>On September 2, Facebook user Deepak Bagi post...</td>\n",
       "      <td>On September 2, Facebook user Deepak Bagi post...</td>\n",
       "      <td>Physically-challenged man beaten to death in U...</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>5db02a4f88ab2b22f0a59435</td>\n",
       "      <td>{'name': 'Alt News Desk', 'link': 'https://www...</td>\n",
       "      <td>October 23, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>September 06, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': 'ac5f228eec354ef8b7b5a7b095f22199'...</td>\n",
       "      <td>altnews.in</td>\n",
       "      <td>Clipped video of Ravish Kumar’s 2013 program c...</td>\n",
       "      <td>54046bda02ef4d9c8633f095893bcde5</td>\n",
       "      <td>https://www.altnews.in/clipped-video-of-ravish...</td>\n",
       "      <td>On the day Ravish Kumar travelled to Manila to...</td>\n",
       "      <td>On the day Ravish Kumar travelled to Manila to...</td>\n",
       "      <td>Clipped video of Ravish Kumar’s 2013 program c...</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>5db02a5088ab2b22f0a59436</td>\n",
       "      <td>{'name': 'Arjun Sidharth', 'link': 'https://ww...</td>\n",
       "      <td>October 23, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>September 06, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': 'd1df1572f23e4a1d93f012877d6535bf'...</td>\n",
       "      <td>altnews.in</td>\n",
       "      <td>Video of scorpions from Ahmedabad, Gujarat sha...</td>\n",
       "      <td>838bd6575b8049609be2d0a7575e9db8</td>\n",
       "      <td>https://www.altnews.in/video-of-scorpions-from...</td>\n",
       "      <td>“राजस्थान के एक गांव में बिच्छू की बरसात हुई द...</td>\n",
       "      <td>...</td>\n",
       "      <td>Video of scorpions from Ahmedabad, Gujarat sha...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>5db02a5088ab2b22f0a59437</td>\n",
       "      <td>{'name': 'Jignesh Patel', 'link': 'https://www...</td>\n",
       "      <td>October 23, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>September 06, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': 'ceffa34b0dc9474ca297da0be833a257'...</td>\n",
       "      <td>altnews.in</td>\n",
       "      <td>Old images of leopard attack in Maharashtra sh...</td>\n",
       "      <td>ac2a0f7a9d9e4215aac36852ebbe74d6</td>\n",
       "      <td>https://www.altnews.in/old-images-of-leopard-a...</td>\n",
       "      <td>Facebook user Jitesh Jadeja posted a set of im...</td>\n",
       "      <td>Facebook user Jitesh Jadeja posted a set of im...</td>\n",
       "      <td>Old images of leopard attack in Maharashtra sh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>5db02a5188ab2b22f0a59438</td>\n",
       "      <td>{'name': 'Arjun Sidharth', 'link': 'https://ww...</td>\n",
       "      <td>October 23, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>September 05, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': 'd3c4ac6fea5e48cda66c8791627d3384'...</td>\n",
       "      <td>altnews.in</td>\n",
       "      <td>Theatrical video shared as youth uprising in P...</td>\n",
       "      <td>3e3ccb9a150e4bb4977938ad485853e3</td>\n",
       "      <td>https://www.altnews.in/theatrical-video-shared...</td>\n",
       "      <td>A video has been circulated on social media wi...</td>\n",
       "      <td>A video has been circulated on social media wi...</td>\n",
       "      <td>Theatrical video shared as youth uprising in P...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>5db0332e88ab2b22f0a5943b</td>\n",
       "      <td>{'name': 'Priyanka Jha', 'link': 'https://www....</td>\n",
       "      <td>October 23, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>October 22, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': '54aa9d44ba844797babd5054f577a30f'...</td>\n",
       "      <td>altnews.in</td>\n",
       "      <td>No, RRB has not reduced NTPC posts from 35,277...</td>\n",
       "      <td>0485ee53dd494e229fead53b69011faa</td>\n",
       "      <td>https://www.altnews.in/did-railway-decrease-nt...</td>\n",
       "      <td>An image of what is purportedly a notice by th...</td>\n",
       "      <td>An image of what is purportedly a notice by th...</td>\n",
       "      <td>No, RRB has not reduced NTPC posts from 35,277...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>5db0333188ab2b22f0a5943c</td>\n",
       "      <td>{'name': 'Arjun Sidharth', 'link': 'https://ww...</td>\n",
       "      <td>October 23, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>October 22, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': '80843e552580465bb69c400b6a0ea44e'...</td>\n",
       "      <td>altnews.in</td>\n",
       "      <td>Old video shared as cross-border firing betwee...</td>\n",
       "      <td>6b0616540e92449db26a6ffca31d7be5</td>\n",
       "      <td>https://www.altnews.in/old-video-shared-as-cro...</td>\n",
       "      <td>Cross-border firing broke out between the Indi...</td>\n",
       "      <td>Cross-border firing broke out between the Indi...</td>\n",
       "      <td>Old video shared as cross-border firing betwee...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>5db0333488ab2b22f0a5943d</td>\n",
       "      <td>{'name': 'Jignesh Patel', 'link': 'https://www...</td>\n",
       "      <td>October 23, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>October 22, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': '034cfeae92c5413e9f50e1b824bf33a4'...</td>\n",
       "      <td>altnews.in</td>\n",
       "      <td>Video of an aircraft hijacking drill in Mexico...</td>\n",
       "      <td>a2089474892d4ff5a680419e0fe4d495</td>\n",
       "      <td>https://www.altnews.in/video-of-an-aircraft-hi...</td>\n",
       "      <td>A video of an aircraft supposedly hijacked in ...</td>\n",
       "      <td>A video of an aircraft supposedly hijacked in ...</td>\n",
       "      <td>Video of an aircraft hijacking drill in Mexico...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>5db0333888ab2b22f0a5943e</td>\n",
       "      <td>{'name': 'Pooja Chaudhuri', 'link': 'https://w...</td>\n",
       "      <td>October 23, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>October 22, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': 'a5b69e8e7aba47ec92ef8633f3f24659'...</td>\n",
       "      <td>altnews.in</td>\n",
       "      <td>Sudarshan News runs fake news claiming Asadudd...</td>\n",
       "      <td>75d7c8ece1f04105a0d27aba33731d85</td>\n",
       "      <td>https://www.altnews.in/sudarshan-news-runs-fak...</td>\n",
       "      <td>In an impassioned October 20 broadcast, Sudars...</td>\n",
       "      <td>In an impassioned October 20 broadcast, Sudars...</td>\n",
       "      <td>Sudarshan News Runs Fake News climbing Asadudd...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>5db0333b88ab2b22f0a5943f</td>\n",
       "      <td>{'name': 'Kinjal', 'link': 'https://www.altnew...</td>\n",
       "      <td>October 23, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>October 21, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': '7d40e7aea7b6455b9baa190174a38e8a'...</td>\n",
       "      <td>altnews.in</td>\n",
       "      <td>Fake quote of Ravish Kumar over Murshidabad tr...</td>\n",
       "      <td>048f8e9e6d8b4dfea0c4186bf1bd54a6</td>\n",
       "      <td>https://www.altnews.in/fake-quote-of-ravish-ku...</td>\n",
       "      <td>Following the Murshidabad triple murder case, ...</td>\n",
       "      <td>Following the Murshidabad triple murder case, ...</td>\n",
       "      <td>Fake quote of Ravish Kumar over Murshidabad tr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>5db0333e88ab2b22f0a59440</td>\n",
       "      <td>{'name': 'Pooja Chaudhuri', 'link': 'https://w...</td>\n",
       "      <td>October 23, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>October 21, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': 'f4a49d48d66643e790d4f1efa6a66f53'...</td>\n",
       "      <td>altnews.in</td>\n",
       "      <td>Old video shared by Pak social media users to ...</td>\n",
       "      <td>1c6eba34859045e5aa7f20c6497efa9c</td>\n",
       "      <td>https://www.altnews.in/old-video-shared-by-pak...</td>\n",
       "      <td>On October 20, India and Pakistan traded fire ...</td>\n",
       "      <td>On October 20, India and Pakistan traded fire ...</td>\n",
       "      <td>Old video shared by Pak social media users to ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>5db0334288ab2b22f0a59441</td>\n",
       "      <td>{'name': 'Pooja Chaudhuri', 'link': 'https://w...</td>\n",
       "      <td>October 23, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>October 21, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': 'b9c3d9a9fb4141c4b1414810b39d13e3'...</td>\n",
       "      <td>altnews.in</td>\n",
       "      <td>Old photo used by Pak social media users to fa...</td>\n",
       "      <td>1fd3d5aeff10482c94eb94d15a04219f</td>\n",
       "      <td>https://www.altnews.in/old-photo-used-by-pak-s...</td>\n",
       "      <td>In the backdrop of recent India-Pakistan cross...</td>\n",
       "      <td>In the backdrop of recent India-Pakistan cross...</td>\n",
       "      <td>Old photo used by Pak social media users to fa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>5db0334588ab2b22f0a59442</td>\n",
       "      <td>{'name': 'Arjun Sidharth', 'link': 'https://ww...</td>\n",
       "      <td>October 23, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>October 21, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': 'c78a4f56f8dc4b289514acd3ee7907e4'...</td>\n",
       "      <td>altnews.in</td>\n",
       "      <td>Screenshots of Aaj Tak fact-check shared to cl...</td>\n",
       "      <td>a8cfbf42abff453dbec97047f3713c74</td>\n",
       "      <td>https://www.altnews.in/screenshots-of-aaj-tak-...</td>\n",
       "      <td>Screenshots of a broadcast by the Hindi news c...</td>\n",
       "      <td>Screenshots of a broadcast by the Hindi news c...</td>\n",
       "      <td>Screenshots of Aaj Tak fact-check shared to cl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>5db0334888ab2b22f0a59443</td>\n",
       "      <td>{'name': 'Arjun Sidharth', 'link': 'https://ww...</td>\n",
       "      <td>October 23, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>October 19, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': 'bda787f15d254891ae48fc8688e83dd4'...</td>\n",
       "      <td>altnews.in</td>\n",
       "      <td>Video shared with false claim that man sets hi...</td>\n",
       "      <td>18cfba105fb64c7f8a7087182c24962c</td>\n",
       "      <td>https://www.altnews.in/video-shared-with-false...</td>\n",
       "      <td>“35000\\- का चालान करने पर लडके ने अपनी कार में...</td>\n",
       "      <td>35000\\-                                      ...</td>\n",
       "      <td>Video shared with false claim that man sets hi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>5db0334c88ab2b22f0a59444</td>\n",
       "      <td>{'name': 'Jignesh Patel', 'link': 'https://www...</td>\n",
       "      <td>October 23, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>October 19, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': '1d5191f6c27e48d6bc754d968daa37a8'...</td>\n",
       "      <td>altnews.in</td>\n",
       "      <td>Video of biker knocked by speeding car in Braz...</td>\n",
       "      <td>099429c05ede4a839f187b3d35b40376</td>\n",
       "      <td>https://www.altnews.in/video-of-biker-knocked-...</td>\n",
       "      <td>A video of a biker who while performing a stun...</td>\n",
       "      <td>A video of a biker who while performing a stun...</td>\n",
       "      <td>Video of biker knocked by speeding car in Braz...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>5db0334f88ab2b22f0a59445</td>\n",
       "      <td>{'name': 'Pooja Chaudhuri', 'link': 'https://w...</td>\n",
       "      <td>October 23, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>October 18, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': 'bd151892e6ea4469b37daf1231c406c1'...</td>\n",
       "      <td>altnews.in</td>\n",
       "      <td>No, India has not repaid all its World Bank lo...</td>\n",
       "      <td>4231b7d4a4e448ceb96c6414a9d04e1c</td>\n",
       "      <td>https://www.altnews.in/no-india-has-not-repaid...</td>\n",
       "      <td>A viral claim on social media asserts that the...</td>\n",
       "      <td>A viral claim on social media asserts that the...</td>\n",
       "      <td>No, India has not repaid all its World Bank lo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>5db0335288ab2b22f0a59446</td>\n",
       "      <td>{'name': 'Jignesh Patel', 'link': 'https://www...</td>\n",
       "      <td>October 23, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>October 18, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': 'b630ace0622543cc89edf1263c9a7c69'...</td>\n",
       "      <td>altnews.in</td>\n",
       "      <td>No, PM Modi isn’t bowing to Gautam Adani’s wif...</td>\n",
       "      <td>82731785dbe641dc9d877db29518ac42</td>\n",
       "      <td>https://www.altnews.in/no-pm-modi-isnt-bowing-...</td>\n",
       "      <td>A photograph of PM Modi bowing to a woman is b...</td>\n",
       "      <td>A photograph of PM Modi bowing to a woman is b...</td>\n",
       "      <td>No, PM Modi isn’t bowing to Gautam Adani’s wif...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>5db0335688ab2b22f0a59447</td>\n",
       "      <td>{'name': 'Jignesh Patel', 'link': 'https://www...</td>\n",
       "      <td>October 23, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>October 18, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': 'bf27119877a1409ab105ef54ec7ae78c'...</td>\n",
       "      <td>altnews.in</td>\n",
       "      <td>Fact-check: HDFC Bank insures Rs 1 lakh deposi...</td>\n",
       "      <td>e5631803922a44bf8c2fbb517cf64a01</td>\n",
       "      <td>https://www.altnews.in/fact-check-hdfc-bank-in...</td>\n",
       "      <td>“RBI…पूरी तैयारी में, banks डूबने की कगार पर, ...</td>\n",
       "      <td>RBI                , banks                 , ...</td>\n",
       "      <td>Fact-check: HDFC Bank insures Rs 1 lakh deposi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>5db0335988ab2b22f0a59448</td>\n",
       "      <td>{'name': 'Arjun Sidharth', 'link': 'https://ww...</td>\n",
       "      <td>October 23, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>October 18, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': '6b20d30371a44a509a8ac2acaec999db'...</td>\n",
       "      <td>altnews.in</td>\n",
       "      <td>Did Abu Dhabi Crown Prince chant ‘Jai Siya Ram...</td>\n",
       "      <td>c39f3e46505a4620b2708679377d91de</td>\n",
       "      <td>https://www.altnews.in/did-abu-dhabi-crown-pri...</td>\n",
       "      <td>“Abu Dhabi Crown Prince chanting Jai Siya Ram ...</td>\n",
       "      <td>Abu Dhabi Crown Prince chanting Jai Siya Ram ...</td>\n",
       "      <td>Did Abu Dhabi Crown Prince chant ‘Jai Siya Ram...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>5db0335d88ab2b22f0a59449</td>\n",
       "      <td>{'name': 'Arjun Sidharth', 'link': 'https://ww...</td>\n",
       "      <td>October 23, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>October 17, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': '7a977ba8066c4d01b094b1e2ec5d0280'...</td>\n",
       "      <td>altnews.in</td>\n",
       "      <td>Viral message falsely claims RBI has introduce...</td>\n",
       "      <td>bc05709ec8824b30914288f6fbde6b4c</td>\n",
       "      <td>https://www.altnews.in/viral-message-falsely-c...</td>\n",
       "      <td>Images of what appears to be a rupee note of 1...</td>\n",
       "      <td>Images of what appears to be a rupee note of 1...</td>\n",
       "      <td>Viral message falsely claims RBI has introduce...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>5db033dc88ab2b22f0a5944b</td>\n",
       "      <td>{'name': 'Arjun Sidharth', 'link': 'https://ww...</td>\n",
       "      <td>October 23, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>October 17, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': '1ff20d77ab4c43de8efb00a63fa6d906'...</td>\n",
       "      <td>altnews.in</td>\n",
       "      <td>Has India’s Global Hunger Index ranking fallen...</td>\n",
       "      <td>98254dfeaf0f411784ece0053da492e4</td>\n",
       "      <td>https://www.altnews.in/has-indias-global-hunge...</td>\n",
       "      <td>According to the latest Global Hunger Index (2...</td>\n",
       "      <td>According to the latest Global Hunger Index (2...</td>\n",
       "      <td>Has India’s Global Hunger Index ranking fallen...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>5db033e088ab2b22f0a5944c</td>\n",
       "      <td>{'name': 'Pooja Chaudhuri', 'link': 'https://w...</td>\n",
       "      <td>October 23, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>October 16, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': '1b3a4154ce1e4af6886f39a15ea61109'...</td>\n",
       "      <td>altnews.in</td>\n",
       "      <td>No, this is not TMC MP Nusrat Jahan performing...</td>\n",
       "      <td>ba8f42538fd44802ba4740df226a2df1</td>\n",
       "      <td>https://www.altnews.in/no-this-is-not-tmc-mp-n...</td>\n",
       "      <td>Two videos of women performing the traditional...</td>\n",
       "      <td>Two videos of women performing the traditional...</td>\n",
       "      <td>No, this is not TMC MP Nusrat Jahan performing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>5db033e388ab2b22f0a5944d</td>\n",
       "      <td>{'name': 'Pooja Chaudhuri', 'link': 'https://w...</td>\n",
       "      <td>October 23, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>October 16, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': '5b6a146b27cf4e9f87d6bccf3f55554a'...</td>\n",
       "      <td>altnews.in</td>\n",
       "      <td>How the Murshidabad triple murder was given a ...</td>\n",
       "      <td>dd289968a49449c1a0b359f7116de337</td>\n",
       "      <td>https://www.altnews.in/how-the-murshidabad-tri...</td>\n",
       "      <td>On October 8, 2019, a family of three – Bandhu...</td>\n",
       "      <td>On October 8, 2019, a family of three   Bandhu...</td>\n",
       "      <td>How the Murshidabad triple murder was given a ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>5db033e688ab2b22f0a5944e</td>\n",
       "      <td>{'name': 'Jignesh Patel', 'link': 'https://www...</td>\n",
       "      <td>October 23, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>October 15, 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': '2ed2aea3affc46aa893a4999ab0b76f1'...</td>\n",
       "      <td>altnews.in</td>\n",
       "      <td>Old video of earthquake in Mexico shared as re...</td>\n",
       "      <td>79f11f845e2245ce9e2290f0a5f8b3e9</td>\n",
       "      <td>https://www.altnews.in/old-video-of-earthquake...</td>\n",
       "      <td>A video which shows horrific visuals of buildi...</td>\n",
       "      <td>A video which shows horrific visuals of buildi...</td>\n",
       "      <td>Old video of earthquake in Mexico shared as re...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13644</th>\n",
       "      <td>13644</td>\n",
       "      <td>5ea6df0a5d72edccfee51dbc</td>\n",
       "      <td>{'name': 'Akhil Reddy', 'link': 'https://factl...</td>\n",
       "      <td>April 27, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>April 25, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': '6739859f0eda48ad8edfbda86e200e6a'...</td>\n",
       "      <td>factly.in</td>\n",
       "      <td>‘కొరోనా వైరస్ ను ల్యాబ్ లోనే సృష్టించారు’ అని...</td>\n",
       "      <td>3a7594e47f1247b98f3ca36ee09b329e</td>\n",
       "      <td>https://factly.in/telugu-nobel-laureate-tasuku...</td>\n",
       "      <td>కొరోనా వైరస్ సహజంగా వచ్చింది కాదని, చైనాయే సృష...</td>\n",
       "      <td>,           ...</td>\n",
       "      <td>\"Coro virus was created in a lab,\" I did not s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13645</th>\n",
       "      <td>13645</td>\n",
       "      <td>5ea6df0e5d72edccfee51dbd</td>\n",
       "      <td>{'name': 'Akhil Reddy', 'link': 'https://factl...</td>\n",
       "      <td>April 27, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>April 25, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': '9fb08d37ecc948fd8c162067db9fad35'...</td>\n",
       "      <td>factly.in</td>\n",
       "      <td>‘దిల్‌సుఖ్‌నగర్‌ పీ అండ్ టీ కాలనీ లో కొందరు ర...</td>\n",
       "      <td>feb41be250544069a8ee680264236234</td>\n",
       "      <td>https://factly.in/telugu-police-have-clarified...</td>\n",
       "      <td>దిల్‌సుఖ్‌నగర్‌ పీ అండ్ టీ కాలనీ లో కొందరు రాత...</td>\n",
       "      <td>...</td>\n",
       "      <td>\"P &amp; T Colony, Dilsukhnagar some of the curren...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13646</th>\n",
       "      <td>13646</td>\n",
       "      <td>5ea6df115d72edccfee51dbe</td>\n",
       "      <td>{'name': 'Akhil Reddy', 'link': 'https://factl...</td>\n",
       "      <td>April 27, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>April 26, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': 'f7b6b4140eed4286984e197f6e551737'...</td>\n",
       "      <td>factly.in</td>\n",
       "      <td>ఫేక్ న్యూస్ స్క్రోల్ చేసిన టీవీ9: కొరోనా టీకా...</td>\n",
       "      <td>930dde038f4a429abe1c733578d373ee</td>\n",
       "      <td>https://factly.in/telugu-tv9-scrolls-fake-news...</td>\n",
       "      <td>‘కొరోనా టీకా వేయించుకున్న మొదటి మహిళ 32 ఏళ్ల ఎ...</td>\n",
       "      <td>32       ...</td>\n",
       "      <td>Scroll back to the TV 9 News: Coro veyincukunn...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13647</th>\n",
       "      <td>13647</td>\n",
       "      <td>5ea6df165d72edccfee51dbf</td>\n",
       "      <td>{'name': 'Akhil Reddy', 'link': 'https://factl...</td>\n",
       "      <td>April 27, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>April 27, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': 'f09e42d0d034464684f65d6986ad251b'...</td>\n",
       "      <td>factly.in</td>\n",
       "      <td>These photos of social distancing in a market...</td>\n",
       "      <td>2a9e85f24fc14b96b4fc9a728ca47c0e</td>\n",
       "      <td>https://factly.in/these-photos-of-social-dista...</td>\n",
       "      <td>A photo of vendors maintaining social distanci...</td>\n",
       "      <td>A photo of vendors maintaining social distanci...</td>\n",
       "      <td>These photos of social distancing in a market...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13648</th>\n",
       "      <td>13648</td>\n",
       "      <td>5ea6df175d72edccfee51dc0</td>\n",
       "      <td>{'name': 'Rakesh Vuppu', 'link': 'https://fact...</td>\n",
       "      <td>April 27, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>April 27, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': 'a226762225e540899424a7397b7fd26c'...</td>\n",
       "      <td>factly.in</td>\n",
       "      <td>పాత వీడియో పెట్టి, ‘హైడ్రాక్సీక్లోరోక్విన్ మం...</td>\n",
       "      <td>6db9ac2349a349269d8684f3e4303b0d</td>\n",
       "      <td>https://factly.in/telugu-an-old-video-is-being...</td>\n",
       "      <td>ఒక వీడియో ని ఫేస్బుక్ లో పోస్టు చేసి, హైడ్రాక్...</td>\n",
       "      <td>,         ...</td>\n",
       "      <td>The old video box, sending haidraksiklorokvin ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13649</th>\n",
       "      <td>13649</td>\n",
       "      <td>5ea6df1c5d72edccfee51dc1</td>\n",
       "      <td>{'name': 'Rakesh Vuppu', 'link': 'https://fact...</td>\n",
       "      <td>April 27, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>April 27, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': '4bcca36432ad423f98dabefe949d50d1'...</td>\n",
       "      <td>factly.in</td>\n",
       "      <td>This Nita Ambani’s tweet regarding the Palgha...</td>\n",
       "      <td>0f586d59058f46eea8dc9c7d40986199</td>\n",
       "      <td>https://factly.in/this-nita-ambanis-tweet-rega...</td>\n",
       "      <td>A post with the screenshot of a tweet purporte...</td>\n",
       "      <td>A post with the screenshot of a tweet purporte...</td>\n",
       "      <td>This Nita Ambani’s tweet regarding the Palghar...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13650</th>\n",
       "      <td>13650</td>\n",
       "      <td>5ea6df1f5d72edccfee51dc2</td>\n",
       "      <td>{'name': 'Rakesh Vuppu', 'link': 'https://fact...</td>\n",
       "      <td>April 27, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>April 27, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': '83bf1c40a8bc4937b064af772691b9e2'...</td>\n",
       "      <td>factly.in</td>\n",
       "      <td>A video clip from a web series is being false...</td>\n",
       "      <td>19228ed66ec041c2958be1b2fbcb3890</td>\n",
       "      <td>https://factly.in/a-video-clip-from-a-web-seri...</td>\n",
       "      <td>A video clip is being widely shared on social ...</td>\n",
       "      <td>A video clip is being widely shared on social ...</td>\n",
       "      <td>A video clip from a web series is being falsel...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13651</th>\n",
       "      <td>13651</td>\n",
       "      <td>5ea6df225d72edccfee51dc3</td>\n",
       "      <td>{'name': 'Amanpreet Kaur', 'link': 'https://ww...</td>\n",
       "      <td>April 27, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>April 16, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': '670e3de509f34f5ca929afe730e52afa'...</td>\n",
       "      <td>indiatoday.in</td>\n",
       "      <td>Fact Check: Video of mufti challenging lockdow...</td>\n",
       "      <td>6f92b31a9a4946d9823b65e42a2a93ef</td>\n",
       "      <td>https://www.indiatoday.in/fact-check/story/fac...</td>\n",
       "      <td>A video of a man addressing a bunch of people ...</td>\n",
       "      <td>A video of a man addressing a bunch of people ...</td>\n",
       "      <td>Fact Check: Video of mufti challenging lockdow...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13652</th>\n",
       "      <td>13652</td>\n",
       "      <td>5ea6df245d72edccfee51dc4</td>\n",
       "      <td>{'name': 'Amanpreet Kaur', 'link': 'https://ww...</td>\n",
       "      <td>April 27, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>April 15, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': 'b6500d202ca6483a8cf5a10cc62ff458'...</td>\n",
       "      <td>indiatoday.in</td>\n",
       "      <td>Fact Check: No, this woman was not raped insid...</td>\n",
       "      <td>61ea52a9dc3d4a4b9fc38bd87fb026ba</td>\n",
       "      <td>https://www.indiatoday.in/fact-check/story/fac...</td>\n",
       "      <td>A picture of a woman lying on a hospital bed i...</td>\n",
       "      <td>A picture of a woman lying on a hospital bed i...</td>\n",
       "      <td>Fact Check: No, this woman was not raped insid...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13653</th>\n",
       "      <td>13653</td>\n",
       "      <td>5ea6df255d72edccfee51dc5</td>\n",
       "      <td>{'name': ' Chayan Kundu', 'link': None}</td>\n",
       "      <td>April 27, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>April 16, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': '62616eac4add49ba97c6ee9505c46c52'...</td>\n",
       "      <td>indiatoday.in</td>\n",
       "      <td>Fact Check: Baseless rumours about slashing pe...</td>\n",
       "      <td>652d6338460941aeb1c64649b51c7ba2</td>\n",
       "      <td>https://www.indiatoday.in/fact-check/story/fac...</td>\n",
       "      <td>Many people are in panic about job loss and sa...</td>\n",
       "      <td>Many people are in panic about job loss and sa...</td>\n",
       "      <td>Fact Check: Baseless rumours about slashing pe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13654</th>\n",
       "      <td>13654</td>\n",
       "      <td>5ea6df265d72edccfee51dc6</td>\n",
       "      <td>{'name': ' Chayan Kundu', 'link': None}</td>\n",
       "      <td>April 27, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>April 18, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': '330d85c6dde7492583a0ffcc272661a1'...</td>\n",
       "      <td>indiatoday.in</td>\n",
       "      <td>Fact Check: Viral post appealing for Facebook ...</td>\n",
       "      <td>644809e1467049bbaeaab4199633ca69</td>\n",
       "      <td>https://www.indiatoday.in/fact-check/story/fac...</td>\n",
       "      <td>An emotional appeal by a child to save her mot...</td>\n",
       "      <td>An emotional appeal by a child to save her mot...</td>\n",
       "      <td>Fact Check: Viral post appealing for Facebook ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13655</th>\n",
       "      <td>13655</td>\n",
       "      <td>5ea6df2b5d72edccfee51dc7</td>\n",
       "      <td>{'name': ' Arjun Deodia ', 'link': None}</td>\n",
       "      <td>April 27, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>April 18, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': 'fc1e2c9e94d7444c93a1cd57d42e8f2c'...</td>\n",
       "      <td>indiatoday.in</td>\n",
       "      <td>Fact Check: Yediyurappa did not attend Kumaras...</td>\n",
       "      <td>e4de2ceaeef845068526b1823d9eb7bd</td>\n",
       "      <td>https://www.indiatoday.in/fact-check/story/fac...</td>\n",
       "      <td>Former Karnataka chief minister HD Kumaraswamy...</td>\n",
       "      <td>Former Karnataka chief minister HD Kumaraswamy...</td>\n",
       "      <td>Fact Check: Yediyurappa did not attend Kumaras...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13656</th>\n",
       "      <td>13656</td>\n",
       "      <td>5ea6df825d72edccfee51dc8</td>\n",
       "      <td>{'name': 'Priyanka Yadav', 'link': 'http://new...</td>\n",
       "      <td>April 27, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>April 23, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': '03715be8ad534ee6b847f2933a3ddfa7'...</td>\n",
       "      <td>newsmobile.in</td>\n",
       "      <td>This video is not of Mount Kailash near Manasa...</td>\n",
       "      <td>1d98768d50e242beb54a13b43132b5fb</td>\n",
       "      <td>http://newsmobile.in/articles/2020/04/23/this-...</td>\n",
       "      <td>\\n\\nNM FACT CHECKERTrending Now\\n\\nThis video ...</td>\n",
       "      <td>\\n\\nNM FACT CHECKERTrending Now\\n\\nThis video ...</td>\n",
       "      <td>This video is not of Mount Kailash near Manasa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13657</th>\n",
       "      <td>13657</td>\n",
       "      <td>5ea6df895d72edccfee51dc9</td>\n",
       "      <td>{'name': 'Rashi Harsha', 'link': 'http://newsm...</td>\n",
       "      <td>April 27, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>April 23, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': '547514875027417e87be7ba5d993f683'...</td>\n",
       "      <td>newsmobile.in</td>\n",
       "      <td>‘Say Namaste’ video conferencing app has not b...</td>\n",
       "      <td>06d0e9ef53c94ac2a6cd4d6f54926265</td>\n",
       "      <td>http://newsmobile.in/articles/2020/04/23/say-n...</td>\n",
       "      <td>\\n\\nNM FACT CHECKER\\n\\n‘Say Namaste’ video con...</td>\n",
       "      <td>\\n\\nNM FACT CHECKER\\n\\n Say Namaste  video con...</td>\n",
       "      <td>‘Say Namaste’ video conferencing app has not b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13658</th>\n",
       "      <td>13658</td>\n",
       "      <td>5ea6df905d72edccfee51dca</td>\n",
       "      <td>{'name': 'Priyanka Yadav', 'link': 'http://new...</td>\n",
       "      <td>April 27, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>April 23, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': '0f11348855064662be21edebb5873e07'...</td>\n",
       "      <td>newsmobile.in</td>\n",
       "      <td>Fact Check: This picture is NOT from Indonesia...</td>\n",
       "      <td>0952a322a3594820a3c1e0abe61e3712</td>\n",
       "      <td>http://newsmobile.in/articles/2020/04/23/fact-...</td>\n",
       "      <td>\\n\\nCoronavirus Fact CheckNM FACT CHECKERTrend...</td>\n",
       "      <td>\\n\\nCoronavirus Fact CheckNM FACT CHECKERTrend...</td>\n",
       "      <td>Fact Check: This picture is NOT from Indonesia...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13659</th>\n",
       "      <td>13659</td>\n",
       "      <td>5ea6df975d72edccfee51dcb</td>\n",
       "      <td>{'name': 'Priyanka Yadav', 'link': 'http://new...</td>\n",
       "      <td>April 27, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>April 23, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': '3386971c12ef45438da864aebacb26af'...</td>\n",
       "      <td>newsmobile.in</td>\n",
       "      <td>Fact Check: Old video being shared with false ...</td>\n",
       "      <td>bb080c50396946cf923a9eb19cdfbf90</td>\n",
       "      <td>http://newsmobile.in/articles/2020/04/23/fact-...</td>\n",
       "      <td>\\n\\nCoronavirus Fact CheckNM FACT CHECKERTrend...</td>\n",
       "      <td>\\n\\nCoronavirus Fact CheckNM FACT CHECKERTrend...</td>\n",
       "      <td>Fact Check: Old video being shared with false ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13660</th>\n",
       "      <td>13660</td>\n",
       "      <td>5ea6df9c5d72edccfee51dcc</td>\n",
       "      <td>{'name': 'Rashi Harsha', 'link': 'http://newsm...</td>\n",
       "      <td>April 27, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>April 24, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': 'fcc7cc09249c4a64a19580579ec6fa77'...</td>\n",
       "      <td>newsmobile.in</td>\n",
       "      <td>Pakistani leader killed in celebratory firing ...</td>\n",
       "      <td>c9f0eb818a41481ab33af83ae0360cd4</td>\n",
       "      <td>http://newsmobile.in/articles/2020/04/24/pakis...</td>\n",
       "      <td>\\n\\nCoronavirus Fact CheckNM FACT CHECKER\\n\\nP...</td>\n",
       "      <td>\\n\\nCoronavirus Fact CheckNM FACT CHECKER\\n\\nP...</td>\n",
       "      <td>Pakistani leader killed in celebratory firing ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13661</th>\n",
       "      <td>13661</td>\n",
       "      <td>5ea6dfa45d72edccfee51dcd</td>\n",
       "      <td>{'name': 'News Mobile Fact Check Bureau', 'lin...</td>\n",
       "      <td>April 27, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>April 24, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': '29061efae23b4e83a2b489550b2b4507'...</td>\n",
       "      <td>newsmobile.in</td>\n",
       "      <td>Fact Check: Maharashtra Home Minister did not ...</td>\n",
       "      <td>969987537b8d47c3925cd2d98b3f5850</td>\n",
       "      <td>http://newsmobile.in/articles/2020/04/24/fact-...</td>\n",
       "      <td>\\n\\nNM FACT CHECKER\\n\\nFact Check: Maharashtra...</td>\n",
       "      <td>\\n\\nNM FACT CHECKER\\n\\nFact Check: Maharashtra...</td>\n",
       "      <td>Fact Check: Maharashtra Home Minister did not ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13662</th>\n",
       "      <td>13662</td>\n",
       "      <td>5ea6dfaa5d72edccfee51dce</td>\n",
       "      <td>{'name': 'News Mobile Fact Check Bureau', 'lin...</td>\n",
       "      <td>April 27, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>April 24, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': 'dceb699207fb463c85caba98bb24708e'...</td>\n",
       "      <td>newsmobile.in</td>\n",
       "      <td>Old picture of birds in the middle of road sha...</td>\n",
       "      <td>9ff1abe84398426bb00d80800a23cf20</td>\n",
       "      <td>http://newsmobile.in/articles/2020/04/24/old-p...</td>\n",
       "      <td>\\n\\nCoronavirus Fact CheckNM FACT CHECKERTrend...</td>\n",
       "      <td>\\n\\nCoronavirus Fact CheckNM FACT CHECKERTrend...</td>\n",
       "      <td>Old picture of birds in the middle of road sha...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13663</th>\n",
       "      <td>13663</td>\n",
       "      <td>5ea6dfb25d72edccfee51dcf</td>\n",
       "      <td>{'name': 'News Mobile Fact Check Bureau', 'lin...</td>\n",
       "      <td>April 27, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>April 25, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': 'e3d29eb4725e4145aebaffa104ea24aa'...</td>\n",
       "      <td>newsmobile.in</td>\n",
       "      <td>Fact Check: Disturbing news from 2015 being sh...</td>\n",
       "      <td>3302842cc9724163b70c4b2de008e21a</td>\n",
       "      <td>http://newsmobile.in/articles/2020/04/25/fact-...</td>\n",
       "      <td>\\n\\nNM FACT CHECKER\\n\\nFact Check: Disturbing ...</td>\n",
       "      <td>\\n\\nNM FACT CHECKER\\n\\nFact Check: Disturbing ...</td>\n",
       "      <td>Fact Check: Disturbing news from 2015 being sh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13664</th>\n",
       "      <td>13664</td>\n",
       "      <td>5ea6dfb55d72edccfee51dd0</td>\n",
       "      <td>{'name': 'News Mobile Fact Check Bureau', 'lin...</td>\n",
       "      <td>April 27, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>April 25, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': 'abcc3f85a1e443daaf0f2ac141d3e589'...</td>\n",
       "      <td>newsmobile.in</td>\n",
       "      <td>Did Japanese Noble Prize Winner, Tasuku Honjo,...</td>\n",
       "      <td>df023766c3154b349939337e69cae63b</td>\n",
       "      <td>http://newsmobile.in/articles/2020/04/25/did-j...</td>\n",
       "      <td>\\n\\nCoronavirus Fact CheckNM FACT CHECKER\\n\\nD...</td>\n",
       "      <td>\\n\\nCoronavirus Fact CheckNM FACT CHECKER\\n\\nD...</td>\n",
       "      <td>Did Japanese Noble Prize Winner, Tasuku Honjo,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13665</th>\n",
       "      <td>13665</td>\n",
       "      <td>5ea6dfbf5d72edccfee51dd1</td>\n",
       "      <td>{'name': 'News Mobile Fact Check Bureau', 'lin...</td>\n",
       "      <td>April 27, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>April 25, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': '320465b42662452c9a93252428040a83'...</td>\n",
       "      <td>newsmobile.in</td>\n",
       "      <td>Fact Check: Wrong number being shared as helpl...</td>\n",
       "      <td>75ea711f661842aca16d5466ff286ee1</td>\n",
       "      <td>http://newsmobile.in/articles/2020/04/25/fact-...</td>\n",
       "      <td>\\n\\nCoronavirus Fact CheckNM FACT CHECKERUncat...</td>\n",
       "      <td>\\n\\nCoronavirus Fact CheckNM FACT CHECKERUncat...</td>\n",
       "      <td>Fact Check: Wrong number being shared as helpl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13666</th>\n",
       "      <td>13666</td>\n",
       "      <td>5ea6dfcb5d72edccfee51dd2</td>\n",
       "      <td>{'name': 'Priyanka Yadav', 'link': 'http://new...</td>\n",
       "      <td>April 27, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>April 25, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': '7d794fe9e62544499ef573b782817e71'...</td>\n",
       "      <td>newsmobile.in</td>\n",
       "      <td>Fact check: Did BJP men torture youth during l...</td>\n",
       "      <td>01f82ade2fe7452cabf5b0c96a5ea3b4</td>\n",
       "      <td>http://newsmobile.in/articles/2020/04/25/fact-...</td>\n",
       "      <td>\\n\\nCoronavirus Fact CheckNM FACT CHECKERTrend...</td>\n",
       "      <td>\\n\\nCoronavirus Fact CheckNM FACT CHECKERTrend...</td>\n",
       "      <td>Fact check: Did BJP men torture youth during l...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13667</th>\n",
       "      <td>13667</td>\n",
       "      <td>5ea6dfd65d72edccfee51dd3</td>\n",
       "      <td>{'name': 'Priyanka Yadav', 'link': 'http://new...</td>\n",
       "      <td>April 27, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>April 25, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': 'f7922a7c03fd4447a4395ebc09cabad2'...</td>\n",
       "      <td>newsmobile.in</td>\n",
       "      <td>Fact Check: Are these sadhus marching towards ...</td>\n",
       "      <td>0590cfdb320e4703a527d312bb79fa9e</td>\n",
       "      <td>http://newsmobile.in/articles/2020/04/25/fact-...</td>\n",
       "      <td>\\n\\nNM FACT CHECKERTrending Now\\n\\nFact Check:...</td>\n",
       "      <td>\\n\\nNM FACT CHECKERTrending Now\\n\\nFact Check:...</td>\n",
       "      <td>Fact Check: Are these sadhus marching towards ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13668</th>\n",
       "      <td>13668</td>\n",
       "      <td>5ea6dfe05d72edccfee51dd4</td>\n",
       "      <td>{'name': 'Abheet Raghav', 'link': 'http://news...</td>\n",
       "      <td>April 27, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>April 25, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': '71d2376c93c746db8982a025c549a292'...</td>\n",
       "      <td>newsmobile.in</td>\n",
       "      <td>Fact Check: American students did not sing Ind...</td>\n",
       "      <td>2a5da450382143f3b6c3297c6e50736e</td>\n",
       "      <td>http://newsmobile.in/articles/2020/04/25/fact-...</td>\n",
       "      <td>\\n\\nCoronavirus Fact CheckHome SliderNM FACT C...</td>\n",
       "      <td>\\n\\nCoronavirus Fact CheckHome SliderNM FACT C...</td>\n",
       "      <td>Fact Check: American students did not sing Ind...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13669</th>\n",
       "      <td>13669</td>\n",
       "      <td>5ea6dfe85d72edccfee51dd5</td>\n",
       "      <td>{'name': 'Shweta Mishra', 'link': 'http://news...</td>\n",
       "      <td>April 27, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>April 26, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': 'b9bb099ab4b44ea4970e5e857c6dbb98'...</td>\n",
       "      <td>newsmobile.in</td>\n",
       "      <td>Fact Check: Former CJI Ranjan Gogoi did not ma...</td>\n",
       "      <td>f7da995db1b744599492861bd5230efb</td>\n",
       "      <td>http://newsmobile.in/articles/2020/04/26/fact-...</td>\n",
       "      <td>\\n\\nCoronavirus Fact CheckNM FACT CHECKERTrend...</td>\n",
       "      <td>\\n\\nCoronavirus Fact CheckNM FACT CHECKERTrend...</td>\n",
       "      <td>Fact Check: Former CJI Ranjan Gogoi did not ma...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13670</th>\n",
       "      <td>13670</td>\n",
       "      <td>5ea6dfeb5d72edccfee51dd6</td>\n",
       "      <td>{'name': 'News Mobile Fact Check Bureau', 'lin...</td>\n",
       "      <td>April 27, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>April 27, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': '976af9ee1c9b4ff298170ed378fb8724'...</td>\n",
       "      <td>newsmobile.in</td>\n",
       "      <td>Fact Check: Delhi’s LG has not banned Azaan; h...</td>\n",
       "      <td>4a5f90f5a83f4ebfbf328a1f13c739c4</td>\n",
       "      <td>http://newsmobile.in/articles/2020/04/27/fact-...</td>\n",
       "      <td>\\n\\nNM FACT CHECKER\\n\\nFact Check: Delhi’s LG ...</td>\n",
       "      <td>\\n\\nNM FACT CHECKER\\n\\nFact Check: Delhi s LG ...</td>\n",
       "      <td>Fact Check: Delhi’s LG has not banned Azaan; h...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13671</th>\n",
       "      <td>13671</td>\n",
       "      <td>5ea6dff75d72edccfee51dd7</td>\n",
       "      <td>{'name': 'News Mobile Fact Check Bureau', 'lin...</td>\n",
       "      <td>April 27, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>April 27, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': '7e92aba157464587aec1923a66ad36b2'...</td>\n",
       "      <td>newsmobile.in</td>\n",
       "      <td>Fact Check: Picture of nurse tending to a musl...</td>\n",
       "      <td>ffda794ffd874d0aa7dc7f5e90ac4247</td>\n",
       "      <td>http://newsmobile.in/articles/2020/04/27/11046...</td>\n",
       "      <td>\\n\\nNM FACT CHECKER\\n\\nFact Check: Picture of ...</td>\n",
       "      <td>\\n\\nNM FACT CHECKER\\n\\nFact Check: Picture of ...</td>\n",
       "      <td>Fact Check: Picture of nurse tending to a musl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13672</th>\n",
       "      <td>13672</td>\n",
       "      <td>5ea6dfff5d72edccfee51dd8</td>\n",
       "      <td>{'name': 'Priyanka Yadav', 'link': 'http://new...</td>\n",
       "      <td>April 27, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>April 27, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': '8170942f80004f8a870a423577f74a1f'...</td>\n",
       "      <td>newsmobile.in</td>\n",
       "      <td>Fact Check: Did food scarcity due to lockdown ...</td>\n",
       "      <td>298becaebcd04ea0a9de27d723c7b071</td>\n",
       "      <td>http://newsmobile.in/articles/2020/04/27/fact-...</td>\n",
       "      <td>\\n\\nCoronavirus Fact CheckNM FACT CHECKERTrend...</td>\n",
       "      <td>\\n\\nCoronavirus Fact CheckNM FACT CHECKERTrend...</td>\n",
       "      <td>Fact Check: Did food scarcity due to lockdown ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13673</th>\n",
       "      <td>13673</td>\n",
       "      <td>5ea6e0065d72edccfee51dd9</td>\n",
       "      <td>{'name': 'Priyanka Yadav', 'link': 'http://new...</td>\n",
       "      <td>April 27, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>April 27, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'doc_id': '218ce54a6df44a539bd4bfaccb9642f5'...</td>\n",
       "      <td>newsmobile.in</td>\n",
       "      <td>Fact Check: Is this video of airport staff man...</td>\n",
       "      <td>6db87eff28b0415a9ac123eb4da8111c</td>\n",
       "      <td>http://newsmobile.in/articles/2020/04/27/fact-...</td>\n",
       "      <td>\\n\\nNM FACT CHECKERTrending Now\\n\\nFact Check:...</td>\n",
       "      <td>\\n\\nNM FACT CHECKERTrending Now\\n\\nFact Check:...</td>\n",
       "      <td>Fact Check: Is this video of airport staff man...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13674 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0                       _id  \\\n",
       "0               0  5db02a1888ab2b22f0a5942e   \n",
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       "28             28  5db033e388ab2b22f0a5944d   \n",
       "29             29  5db033e688ab2b22f0a5944e   \n",
       "...           ...                       ...   \n",
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       "13645       13645  5ea6df0e5d72edccfee51dbd   \n",
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       "13655       13655  5ea6df2b5d72edccfee51dc7   \n",
       "13656       13656  5ea6df825d72edccfee51dc8   \n",
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       "13658       13658  5ea6df905d72edccfee51dca   \n",
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       "13660       13660  5ea6df9c5d72edccfee51dcc   \n",
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       "13662       13662  5ea6dfaa5d72edccfee51dce   \n",
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       "13665       13665  5ea6dfbf5d72edccfee51dd1   \n",
       "13666       13666  5ea6dfcb5d72edccfee51dd2   \n",
       "13667       13667  5ea6dfd65d72edccfee51dd3   \n",
       "13668       13668  5ea6dfe05d72edccfee51dd4   \n",
       "13669       13669  5ea6dfe85d72edccfee51dd5   \n",
       "13670       13670  5ea6dfeb5d72edccfee51dd6   \n",
       "13671       13671  5ea6dff75d72edccfee51dd7   \n",
       "13672       13672  5ea6dfff5d72edccfee51dd8   \n",
       "13673       13673  5ea6e0065d72edccfee51dd9   \n",
       "\n",
       "                                                  author     date_accessed  \\\n",
       "0      {'name': 'Jignesh Patel', 'link': 'https://www...  October 23, 2019   \n",
       "1      {'name': 'Arjun Sidharth', 'link': 'https://ww...  October 23, 2019   \n",
       "2      {'name': 'Arjun Sidharth', 'link': 'https://ww...  October 23, 2019   \n",
       "3      {'name': 'Pooja Chaudhuri', 'link': 'https://w...  October 23, 2019   \n",
       "4      {'name': 'Pooja Chaudhuri', 'link': 'https://w...  October 23, 2019   \n",
       "5      {'name': 'Arjun Sidharth', 'link': 'https://ww...  October 23, 2019   \n",
       "6      {'name': 'Jignesh Patel', 'link': 'https://www...  October 23, 2019   \n",
       "7      {'name': 'Alt News Desk', 'link': 'https://www...  October 23, 2019   \n",
       "8      {'name': 'Arjun Sidharth', 'link': 'https://ww...  October 23, 2019   \n",
       "9      {'name': 'Jignesh Patel', 'link': 'https://www...  October 23, 2019   \n",
       "10     {'name': 'Arjun Sidharth', 'link': 'https://ww...  October 23, 2019   \n",
       "11     {'name': 'Priyanka Jha', 'link': 'https://www....  October 23, 2019   \n",
       "12     {'name': 'Arjun Sidharth', 'link': 'https://ww...  October 23, 2019   \n",
       "13     {'name': 'Jignesh Patel', 'link': 'https://www...  October 23, 2019   \n",
       "14     {'name': 'Pooja Chaudhuri', 'link': 'https://w...  October 23, 2019   \n",
       "15     {'name': 'Kinjal', 'link': 'https://www.altnew...  October 23, 2019   \n",
       "16     {'name': 'Pooja Chaudhuri', 'link': 'https://w...  October 23, 2019   \n",
       "17     {'name': 'Pooja Chaudhuri', 'link': 'https://w...  October 23, 2019   \n",
       "18     {'name': 'Arjun Sidharth', 'link': 'https://ww...  October 23, 2019   \n",
       "19     {'name': 'Arjun Sidharth', 'link': 'https://ww...  October 23, 2019   \n",
       "20     {'name': 'Jignesh Patel', 'link': 'https://www...  October 23, 2019   \n",
       "21     {'name': 'Pooja Chaudhuri', 'link': 'https://w...  October 23, 2019   \n",
       "22     {'name': 'Jignesh Patel', 'link': 'https://www...  October 23, 2019   \n",
       "23     {'name': 'Jignesh Patel', 'link': 'https://www...  October 23, 2019   \n",
       "24     {'name': 'Arjun Sidharth', 'link': 'https://ww...  October 23, 2019   \n",
       "25     {'name': 'Arjun Sidharth', 'link': 'https://ww...  October 23, 2019   \n",
       "26     {'name': 'Arjun Sidharth', 'link': 'https://ww...  October 23, 2019   \n",
       "27     {'name': 'Pooja Chaudhuri', 'link': 'https://w...  October 23, 2019   \n",
       "28     {'name': 'Pooja Chaudhuri', 'link': 'https://w...  October 23, 2019   \n",
       "29     {'name': 'Jignesh Patel', 'link': 'https://www...  October 23, 2019   \n",
       "...                                                  ...               ...   \n",
       "13644  {'name': 'Akhil Reddy', 'link': 'https://factl...    April 27, 2020   \n",
       "13645  {'name': 'Akhil Reddy', 'link': 'https://factl...    April 27, 2020   \n",
       "13646  {'name': 'Akhil Reddy', 'link': 'https://factl...    April 27, 2020   \n",
       "13647  {'name': 'Akhil Reddy', 'link': 'https://factl...    April 27, 2020   \n",
       "13648  {'name': 'Rakesh Vuppu', 'link': 'https://fact...    April 27, 2020   \n",
       "13649  {'name': 'Rakesh Vuppu', 'link': 'https://fact...    April 27, 2020   \n",
       "13650  {'name': 'Rakesh Vuppu', 'link': 'https://fact...    April 27, 2020   \n",
       "13651  {'name': 'Amanpreet Kaur', 'link': 'https://ww...    April 27, 2020   \n",
       "13652  {'name': 'Amanpreet Kaur', 'link': 'https://ww...    April 27, 2020   \n",
       "13653            {'name': ' Chayan Kundu', 'link': None}    April 27, 2020   \n",
       "13654            {'name': ' Chayan Kundu', 'link': None}    April 27, 2020   \n",
       "13655           {'name': ' Arjun Deodia ', 'link': None}    April 27, 2020   \n",
       "13656  {'name': 'Priyanka Yadav', 'link': 'http://new...    April 27, 2020   \n",
       "13657  {'name': 'Rashi Harsha', 'link': 'http://newsm...    April 27, 2020   \n",
       "13658  {'name': 'Priyanka Yadav', 'link': 'http://new...    April 27, 2020   \n",
       "13659  {'name': 'Priyanka Yadav', 'link': 'http://new...    April 27, 2020   \n",
       "13660  {'name': 'Rashi Harsha', 'link': 'http://newsm...    April 27, 2020   \n",
       "13661  {'name': 'News Mobile Fact Check Bureau', 'lin...    April 27, 2020   \n",
       "13662  {'name': 'News Mobile Fact Check Bureau', 'lin...    April 27, 2020   \n",
       "13663  {'name': 'News Mobile Fact Check Bureau', 'lin...    April 27, 2020   \n",
       "13664  {'name': 'News Mobile Fact Check Bureau', 'lin...    April 27, 2020   \n",
       "13665  {'name': 'News Mobile Fact Check Bureau', 'lin...    April 27, 2020   \n",
       "13666  {'name': 'Priyanka Yadav', 'link': 'http://new...    April 27, 2020   \n",
       "13667  {'name': 'Priyanka Yadav', 'link': 'http://new...    April 27, 2020   \n",
       "13668  {'name': 'Abheet Raghav', 'link': 'http://news...    April 27, 2020   \n",
       "13669  {'name': 'Shweta Mishra', 'link': 'http://news...    April 27, 2020   \n",
       "13670  {'name': 'News Mobile Fact Check Bureau', 'lin...    April 27, 2020   \n",
       "13671  {'name': 'News Mobile Fact Check Bureau', 'lin...    April 27, 2020   \n",
       "13672  {'name': 'Priyanka Yadav', 'link': 'http://new...    April 27, 2020   \n",
       "13673  {'name': 'Priyanka Yadav', 'link': 'http://new...    April 27, 2020   \n",
       "\n",
       "      date_accessed_UTC        date_updated date_updated_UTC  \\\n",
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       "1                   NaN  September 09, 2019              NaN   \n",
       "2                   NaN  September 09, 2019              NaN   \n",
       "3                   NaN  September 08, 2019              NaN   \n",
       "4                   NaN  September 07, 2019              NaN   \n",
       "5                   NaN  September 07, 2019              NaN   \n",
       "6                   NaN  September 07, 2019              NaN   \n",
       "7                   NaN  September 06, 2019              NaN   \n",
       "8                   NaN  September 06, 2019              NaN   \n",
       "9                   NaN  September 06, 2019              NaN   \n",
       "10                  NaN  September 05, 2019              NaN   \n",
       "11                  NaN    October 22, 2019              NaN   \n",
       "12                  NaN    October 22, 2019              NaN   \n",
       "13                  NaN    October 22, 2019              NaN   \n",
       "14                  NaN    October 22, 2019              NaN   \n",
       "15                  NaN    October 21, 2019              NaN   \n",
       "16                  NaN    October 21, 2019              NaN   \n",
       "17                  NaN    October 21, 2019              NaN   \n",
       "18                  NaN    October 21, 2019              NaN   \n",
       "19                  NaN    October 19, 2019              NaN   \n",
       "20                  NaN    October 19, 2019              NaN   \n",
       "21                  NaN    October 18, 2019              NaN   \n",
       "22                  NaN    October 18, 2019              NaN   \n",
       "23                  NaN    October 18, 2019              NaN   \n",
       "24                  NaN    October 18, 2019              NaN   \n",
       "25                  NaN    October 17, 2019              NaN   \n",
       "26                  NaN    October 17, 2019              NaN   \n",
       "27                  NaN    October 16, 2019              NaN   \n",
       "28                  NaN    October 16, 2019              NaN   \n",
       "29                  NaN    October 15, 2019              NaN   \n",
       "...                 ...                 ...              ...   \n",
       "13644               NaN      April 25, 2020              NaN   \n",
       "13645               NaN      April 25, 2020              NaN   \n",
       "13646               NaN      April 26, 2020              NaN   \n",
       "13647               NaN      April 27, 2020              NaN   \n",
       "13648               NaN      April 27, 2020              NaN   \n",
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       "13650               NaN      April 27, 2020              NaN   \n",
       "13651               NaN      April 16, 2020              NaN   \n",
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       "13654               NaN      April 18, 2020              NaN   \n",
       "13655               NaN      April 18, 2020              NaN   \n",
       "13656               NaN      April 23, 2020              NaN   \n",
       "13657               NaN      April 23, 2020              NaN   \n",
       "13658               NaN      April 23, 2020              NaN   \n",
       "13659               NaN      April 23, 2020              NaN   \n",
       "13660               NaN      April 24, 2020              NaN   \n",
       "13661               NaN      April 24, 2020              NaN   \n",
       "13662               NaN      April 24, 2020              NaN   \n",
       "13663               NaN      April 25, 2020              NaN   \n",
       "13664               NaN      April 25, 2020              NaN   \n",
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       "13666               NaN      April 25, 2020              NaN   \n",
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       "13669               NaN      April 26, 2020              NaN   \n",
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       "13672               NaN      April 27, 2020              NaN   \n",
       "13673               NaN      April 27, 2020              NaN   \n",
       "\n",
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       "13669  [{'doc_id': 'b9bb099ab4b44ea4970e5e857c6dbb98'...  newsmobile.in   \n",
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       "\n",
       "                                                headline  \\\n",
       "0      Madhu Kishwar tweets photoshopped image of Amu...   \n",
       "1      Photoshopped promo of ABP News over Chandrayaa...   \n",
       "2      Video of scuffle between men in khaki uniform ...   \n",
       "3      Mentally unstable man beaten by mob in Himacha...   \n",
       "4      Digitally created artwork shared as Hurricane ...   \n",
       "5      Unrelated video from Rajasthan shared as polic...   \n",
       "6      Physically-challenged man beaten to death in U...   \n",
       "7      Clipped video of Ravish Kumar’s 2013 program c...   \n",
       "8      Video of scorpions from Ahmedabad, Gujarat sha...   \n",
       "9      Old images of leopard attack in Maharashtra sh...   \n",
       "10     Theatrical video shared as youth uprising in P...   \n",
       "11     No, RRB has not reduced NTPC posts from 35,277...   \n",
       "12     Old video shared as cross-border firing betwee...   \n",
       "13     Video of an aircraft hijacking drill in Mexico...   \n",
       "14     Sudarshan News runs fake news claiming Asadudd...   \n",
       "15     Fake quote of Ravish Kumar over Murshidabad tr...   \n",
       "16     Old video shared by Pak social media users to ...   \n",
       "17     Old photo used by Pak social media users to fa...   \n",
       "18     Screenshots of Aaj Tak fact-check shared to cl...   \n",
       "19     Video shared with false claim that man sets hi...   \n",
       "20     Video of biker knocked by speeding car in Braz...   \n",
       "21     No, India has not repaid all its World Bank lo...   \n",
       "22     No, PM Modi isn’t bowing to Gautam Adani’s wif...   \n",
       "23     Fact-check: HDFC Bank insures Rs 1 lakh deposi...   \n",
       "24     Did Abu Dhabi Crown Prince chant ‘Jai Siya Ram...   \n",
       "25     Viral message falsely claims RBI has introduce...   \n",
       "26     Has India’s Global Hunger Index ranking fallen...   \n",
       "27     No, this is not TMC MP Nusrat Jahan performing...   \n",
       "28     How the Murshidabad triple murder was given a ...   \n",
       "29     Old video of earthquake in Mexico shared as re...   \n",
       "...                                                  ...   \n",
       "13644   ‘కొరోనా వైరస్ ను ల్యాబ్ లోనే సృష్టించారు’ అని...   \n",
       "13645   ‘దిల్‌సుఖ్‌నగర్‌ పీ అండ్ టీ కాలనీ లో కొందరు ర...   \n",
       "13646   ఫేక్ న్యూస్ స్క్రోల్ చేసిన టీవీ9: కొరోనా టీకా...   \n",
       "13647   These photos of social distancing in a market...   \n",
       "13648   పాత వీడియో పెట్టి, ‘హైడ్రాక్సీక్లోరోక్విన్ మం...   \n",
       "13649   This Nita Ambani’s tweet regarding the Palgha...   \n",
       "13650   A video clip from a web series is being false...   \n",
       "13651  Fact Check: Video of mufti challenging lockdow...   \n",
       "13652  Fact Check: No, this woman was not raped insid...   \n",
       "13653  Fact Check: Baseless rumours about slashing pe...   \n",
       "13654  Fact Check: Viral post appealing for Facebook ...   \n",
       "13655  Fact Check: Yediyurappa did not attend Kumaras...   \n",
       "13656  This video is not of Mount Kailash near Manasa...   \n",
       "13657  ‘Say Namaste’ video conferencing app has not b...   \n",
       "13658  Fact Check: This picture is NOT from Indonesia...   \n",
       "13659  Fact Check: Old video being shared with false ...   \n",
       "13660  Pakistani leader killed in celebratory firing ...   \n",
       "13661  Fact Check: Maharashtra Home Minister did not ...   \n",
       "13662  Old picture of birds in the middle of road sha...   \n",
       "13663  Fact Check: Disturbing news from 2015 being sh...   \n",
       "13664  Did Japanese Noble Prize Winner, Tasuku Honjo,...   \n",
       "13665  Fact Check: Wrong number being shared as helpl...   \n",
       "13666  Fact check: Did BJP men torture youth during l...   \n",
       "13667  Fact Check: Are these sadhus marching towards ...   \n",
       "13668  Fact Check: American students did not sing Ind...   \n",
       "13669  Fact Check: Former CJI Ranjan Gogoi did not ma...   \n",
       "13670  Fact Check: Delhi’s LG has not banned Azaan; h...   \n",
       "13671  Fact Check: Picture of nurse tending to a musl...   \n",
       "13672  Fact Check: Did food scarcity due to lockdown ...   \n",
       "13673  Fact Check: Is this video of airport staff man...   \n",
       "\n",
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       "\n",
       "                                                 postURL  \\\n",
       "0      https://www.altnews.in/madhu-kishwar-tweets-ph...   \n",
       "1      https://www.altnews.in/photoshopped-promo-of-a...   \n",
       "2      https://www.altnews.in/video-of-scuffle-betwee...   \n",
       "3      https://www.altnews.in/mentally-unstable-man-b...   \n",
       "4      https://www.altnews.in/digitally-created-artwo...   \n",
       "5      https://www.altnews.in/unrelated-video-from-ra...   \n",
       "6      https://www.altnews.in/physically-challenged-m...   \n",
       "7      https://www.altnews.in/clipped-video-of-ravish...   \n",
       "8      https://www.altnews.in/video-of-scorpions-from...   \n",
       "9      https://www.altnews.in/old-images-of-leopard-a...   \n",
       "10     https://www.altnews.in/theatrical-video-shared...   \n",
       "11     https://www.altnews.in/did-railway-decrease-nt...   \n",
       "12     https://www.altnews.in/old-video-shared-as-cro...   \n",
       "13     https://www.altnews.in/video-of-an-aircraft-hi...   \n",
       "14     https://www.altnews.in/sudarshan-news-runs-fak...   \n",
       "15     https://www.altnews.in/fake-quote-of-ravish-ku...   \n",
       "16     https://www.altnews.in/old-video-shared-by-pak...   \n",
       "17     https://www.altnews.in/old-photo-used-by-pak-s...   \n",
       "18     https://www.altnews.in/screenshots-of-aaj-tak-...   \n",
       "19     https://www.altnews.in/video-shared-with-false...   \n",
       "20     https://www.altnews.in/video-of-biker-knocked-...   \n",
       "21     https://www.altnews.in/no-india-has-not-repaid...   \n",
       "22     https://www.altnews.in/no-pm-modi-isnt-bowing-...   \n",
       "23     https://www.altnews.in/fact-check-hdfc-bank-in...   \n",
       "24     https://www.altnews.in/did-abu-dhabi-crown-pri...   \n",
       "25     https://www.altnews.in/viral-message-falsely-c...   \n",
       "26     https://www.altnews.in/has-indias-global-hunge...   \n",
       "27     https://www.altnews.in/no-this-is-not-tmc-mp-n...   \n",
       "28     https://www.altnews.in/how-the-murshidabad-tri...   \n",
       "29     https://www.altnews.in/old-video-of-earthquake...   \n",
       "...                                                  ...   \n",
       "13644  https://factly.in/telugu-nobel-laureate-tasuku...   \n",
       "13645  https://factly.in/telugu-police-have-clarified...   \n",
       "13646  https://factly.in/telugu-tv9-scrolls-fake-news...   \n",
       "13647  https://factly.in/these-photos-of-social-dista...   \n",
       "13648  https://factly.in/telugu-an-old-video-is-being...   \n",
       "13649  https://factly.in/this-nita-ambanis-tweet-rega...   \n",
       "13650  https://factly.in/a-video-clip-from-a-web-seri...   \n",
       "13651  https://www.indiatoday.in/fact-check/story/fac...   \n",
       "13652  https://www.indiatoday.in/fact-check/story/fac...   \n",
       "13653  https://www.indiatoday.in/fact-check/story/fac...   \n",
       "13654  https://www.indiatoday.in/fact-check/story/fac...   \n",
       "13655  https://www.indiatoday.in/fact-check/story/fac...   \n",
       "13656  http://newsmobile.in/articles/2020/04/23/this-...   \n",
       "13657  http://newsmobile.in/articles/2020/04/23/say-n...   \n",
       "13658  http://newsmobile.in/articles/2020/04/23/fact-...   \n",
       "13659  http://newsmobile.in/articles/2020/04/23/fact-...   \n",
       "13660  http://newsmobile.in/articles/2020/04/24/pakis...   \n",
       "13661  http://newsmobile.in/articles/2020/04/24/fact-...   \n",
       "13662  http://newsmobile.in/articles/2020/04/24/old-p...   \n",
       "13663  http://newsmobile.in/articles/2020/04/25/fact-...   \n",
       "13664  http://newsmobile.in/articles/2020/04/25/did-j...   \n",
       "13665  http://newsmobile.in/articles/2020/04/25/fact-...   \n",
       "13666  http://newsmobile.in/articles/2020/04/25/fact-...   \n",
       "13667  http://newsmobile.in/articles/2020/04/25/fact-...   \n",
       "13668  http://newsmobile.in/articles/2020/04/25/fact-...   \n",
       "13669  http://newsmobile.in/articles/2020/04/26/fact-...   \n",
       "13670  http://newsmobile.in/articles/2020/04/27/fact-...   \n",
       "13671  http://newsmobile.in/articles/2020/04/27/11046...   \n",
       "13672  http://newsmobile.in/articles/2020/04/27/fact-...   \n",
       "13673  http://newsmobile.in/articles/2020/04/27/fact-...   \n",
       "\n",
       "                                                 content  \\\n",
       "0      “बिना कुछ कहे, सब कुछ कह दिया। (Says everythin...   \n",
       "1      A still of what appears to be a promo for an A...   \n",
       "2      A video showing two men in khaki uniform invol...   \n",
       "3      A video of a man hounded by a mob over suspici...   \n",
       "4      “A view of Hurricane Dorian from the coasts of...   \n",
       "5      Penalties for traffic violations have risen st...   \n",
       "6      On September 2, Facebook user Deepak Bagi post...   \n",
       "7      On the day Ravish Kumar travelled to Manila to...   \n",
       "8      “राजस्थान के एक गांव में बिच्छू की बरसात हुई द...   \n",
       "9      Facebook user Jitesh Jadeja posted a set of im...   \n",
       "10     A video has been circulated on social media wi...   \n",
       "11     An image of what is purportedly a notice by th...   \n",
       "12     Cross-border firing broke out between the Indi...   \n",
       "13     A video of an aircraft supposedly hijacked in ...   \n",
       "14     In an impassioned October 20 broadcast, Sudars...   \n",
       "15     Following the Murshidabad triple murder case, ...   \n",
       "16     On October 20, India and Pakistan traded fire ...   \n",
       "17     In the backdrop of recent India-Pakistan cross...   \n",
       "18     Screenshots of a broadcast by the Hindi news c...   \n",
       "19     “35000\\- का चालान करने पर लडके ने अपनी कार में...   \n",
       "20     A video of a biker who while performing a stun...   \n",
       "21     A viral claim on social media asserts that the...   \n",
       "22     A photograph of PM Modi bowing to a woman is b...   \n",
       "23     “RBI…पूरी तैयारी में, banks डूबने की कगार पर, ...   \n",
       "24     “Abu Dhabi Crown Prince chanting Jai Siya Ram ...   \n",
       "25     Images of what appears to be a rupee note of 1...   \n",
       "26     According to the latest Global Hunger Index (2...   \n",
       "27     Two videos of women performing the traditional...   \n",
       "28     On October 8, 2019, a family of three – Bandhu...   \n",
       "29     A video which shows horrific visuals of buildi...   \n",
       "...                                                  ...   \n",
       "13644  కొరోనా వైరస్ సహజంగా వచ్చింది కాదని, చైనాయే సృష...   \n",
       "13645  దిల్‌సుఖ్‌నగర్‌ పీ అండ్ టీ కాలనీ లో కొందరు రాత...   \n",
       "13646  ‘కొరోనా టీకా వేయించుకున్న మొదటి మహిళ 32 ఏళ్ల ఎ...   \n",
       "13647  A photo of vendors maintaining social distanci...   \n",
       "13648  ఒక వీడియో ని ఫేస్బుక్ లో పోస్టు చేసి, హైడ్రాక్...   \n",
       "13649  A post with the screenshot of a tweet purporte...   \n",
       "13650  A video clip is being widely shared on social ...   \n",
       "13651  A video of a man addressing a bunch of people ...   \n",
       "13652  A picture of a woman lying on a hospital bed i...   \n",
       "13653  Many people are in panic about job loss and sa...   \n",
       "13654  An emotional appeal by a child to save her mot...   \n",
       "13655  Former Karnataka chief minister HD Kumaraswamy...   \n",
       "13656  \\n\\nNM FACT CHECKERTrending Now\\n\\nThis video ...   \n",
       "13657  \\n\\nNM FACT CHECKER\\n\\n‘Say Namaste’ video con...   \n",
       "13658  \\n\\nCoronavirus Fact CheckNM FACT CHECKERTrend...   \n",
       "13659  \\n\\nCoronavirus Fact CheckNM FACT CHECKERTrend...   \n",
       "13660  \\n\\nCoronavirus Fact CheckNM FACT CHECKER\\n\\nP...   \n",
       "13661  \\n\\nNM FACT CHECKER\\n\\nFact Check: Maharashtra...   \n",
       "13662  \\n\\nCoronavirus Fact CheckNM FACT CHECKERTrend...   \n",
       "13663  \\n\\nNM FACT CHECKER\\n\\nFact Check: Disturbing ...   \n",
       "13664  \\n\\nCoronavirus Fact CheckNM FACT CHECKER\\n\\nD...   \n",
       "13665  \\n\\nCoronavirus Fact CheckNM FACT CHECKERUncat...   \n",
       "13666  \\n\\nCoronavirus Fact CheckNM FACT CHECKERTrend...   \n",
       "13667  \\n\\nNM FACT CHECKERTrending Now\\n\\nFact Check:...   \n",
       "13668  \\n\\nCoronavirus Fact CheckHome SliderNM FACT C...   \n",
       "13669  \\n\\nCoronavirus Fact CheckNM FACT CHECKERTrend...   \n",
       "13670  \\n\\nNM FACT CHECKER\\n\\nFact Check: Delhi’s LG ...   \n",
       "13671  \\n\\nNM FACT CHECKER\\n\\nFact Check: Picture of ...   \n",
       "13672  \\n\\nCoronavirus Fact CheckNM FACT CHECKERTrend...   \n",
       "13673  \\n\\nNM FACT CHECKERTrending Now\\n\\nFact Check:...   \n",
       "\n",
       "                                             content_eng  \\\n",
       "0                   ,                 (Says everythin...   \n",
       "1      A still of what appears to be a promo for an A...   \n",
       "2      A video showing two men in khaki uniform invol...   \n",
       "3      A video of a man hounded by a mob over suspici...   \n",
       "4       A view of Hurricane Dorian from the coasts of...   \n",
       "5      Penalties for traffic violations have risen st...   \n",
       "6      On September 2, Facebook user Deepak Bagi post...   \n",
       "7      On the day Ravish Kumar travelled to Manila to...   \n",
       "8                                                    ...   \n",
       "9      Facebook user Jitesh Jadeja posted a set of im...   \n",
       "10     A video has been circulated on social media wi...   \n",
       "11     An image of what is purportedly a notice by th...   \n",
       "12     Cross-border firing broke out between the Indi...   \n",
       "13     A video of an aircraft supposedly hijacked in ...   \n",
       "14     In an impassioned October 20 broadcast, Sudars...   \n",
       "15     Following the Murshidabad triple murder case, ...   \n",
       "16     On October 20, India and Pakistan traded fire ...   \n",
       "17     In the backdrop of recent India-Pakistan cross...   \n",
       "18     Screenshots of a broadcast by the Hindi news c...   \n",
       "19      35000\\-                                      ...   \n",
       "20     A video of a biker who while performing a stun...   \n",
       "21     A viral claim on social media asserts that the...   \n",
       "22     A photograph of PM Modi bowing to a woman is b...   \n",
       "23      RBI                , banks                 , ...   \n",
       "24      Abu Dhabi Crown Prince chanting Jai Siya Ram ...   \n",
       "25     Images of what appears to be a rupee note of 1...   \n",
       "26     According to the latest Global Hunger Index (2...   \n",
       "27     Two videos of women performing the traditional...   \n",
       "28     On October 8, 2019, a family of three   Bandhu...   \n",
       "29     A video which shows horrific visuals of buildi...   \n",
       "...                                                  ...   \n",
       "13644                                    ,           ...   \n",
       "13645                                                ...   \n",
       "13646                                       32       ...   \n",
       "13647  A photo of vendors maintaining social distanci...   \n",
       "13648                                      ,         ...   \n",
       "13649  A post with the screenshot of a tweet purporte...   \n",
       "13650  A video clip is being widely shared on social ...   \n",
       "13651  A video of a man addressing a bunch of people ...   \n",
       "13652  A picture of a woman lying on a hospital bed i...   \n",
       "13653  Many people are in panic about job loss and sa...   \n",
       "13654  An emotional appeal by a child to save her mot...   \n",
       "13655  Former Karnataka chief minister HD Kumaraswamy...   \n",
       "13656  \\n\\nNM FACT CHECKERTrending Now\\n\\nThis video ...   \n",
       "13657  \\n\\nNM FACT CHECKER\\n\\n Say Namaste  video con...   \n",
       "13658  \\n\\nCoronavirus Fact CheckNM FACT CHECKERTrend...   \n",
       "13659  \\n\\nCoronavirus Fact CheckNM FACT CHECKERTrend...   \n",
       "13660  \\n\\nCoronavirus Fact CheckNM FACT CHECKER\\n\\nP...   \n",
       "13661  \\n\\nNM FACT CHECKER\\n\\nFact Check: Maharashtra...   \n",
       "13662  \\n\\nCoronavirus Fact CheckNM FACT CHECKERTrend...   \n",
       "13663  \\n\\nNM FACT CHECKER\\n\\nFact Check: Disturbing ...   \n",
       "13664  \\n\\nCoronavirus Fact CheckNM FACT CHECKER\\n\\nD...   \n",
       "13665  \\n\\nCoronavirus Fact CheckNM FACT CHECKERUncat...   \n",
       "13666  \\n\\nCoronavirus Fact CheckNM FACT CHECKERTrend...   \n",
       "13667  \\n\\nNM FACT CHECKERTrending Now\\n\\nFact Check:...   \n",
       "13668  \\n\\nCoronavirus Fact CheckHome SliderNM FACT C...   \n",
       "13669  \\n\\nCoronavirus Fact CheckNM FACT CHECKERTrend...   \n",
       "13670  \\n\\nNM FACT CHECKER\\n\\nFact Check: Delhi s LG ...   \n",
       "13671  \\n\\nNM FACT CHECKER\\n\\nFact Check: Picture of ...   \n",
       "13672  \\n\\nCoronavirus Fact CheckNM FACT CHECKERTrend...   \n",
       "13673  \\n\\nNM FACT CHECKERTrending Now\\n\\nFact Check:...   \n",
       "\n",
       "                                            eng_headline  \n",
       "0      Madhu Kishwar tweets photoshopped image of Amu...  \n",
       "1      Photoshopped promo of ABP News over Chandrayaa...  \n",
       "2      Video of scuffle between men in khaki uniform ...  \n",
       "3      Mentally unstable man beaten by mob in Himacha...  \n",
       "4      Digitally created artwork shared as Hurricane ...  \n",
       "5      Unrelated video from Rajasthan shared as polic...  \n",
       "6      Physically-challenged man beaten to death in U...  \n",
       "7      Clipped video of Ravish Kumar’s 2013 program c...  \n",
       "8      Video of scorpions from Ahmedabad, Gujarat sha...  \n",
       "9      Old images of leopard attack in Maharashtra sh...  \n",
       "10     Theatrical video shared as youth uprising in P...  \n",
       "11     No, RRB has not reduced NTPC posts from 35,277...  \n",
       "12     Old video shared as cross-border firing betwee...  \n",
       "13     Video of an aircraft hijacking drill in Mexico...  \n",
       "14     Sudarshan News Runs Fake News climbing Asadudd...  \n",
       "15     Fake quote of Ravish Kumar over Murshidabad tr...  \n",
       "16     Old video shared by Pak social media users to ...  \n",
       "17     Old photo used by Pak social media users to fa...  \n",
       "18     Screenshots of Aaj Tak fact-check shared to cl...  \n",
       "19     Video shared with false claim that man sets hi...  \n",
       "20     Video of biker knocked by speeding car in Braz...  \n",
       "21     No, India has not repaid all its World Bank lo...  \n",
       "22     No, PM Modi isn’t bowing to Gautam Adani’s wif...  \n",
       "23     Fact-check: HDFC Bank insures Rs 1 lakh deposi...  \n",
       "24     Did Abu Dhabi Crown Prince chant ‘Jai Siya Ram...  \n",
       "25     Viral message falsely claims RBI has introduce...  \n",
       "26     Has India’s Global Hunger Index ranking fallen...  \n",
       "27     No, this is not TMC MP Nusrat Jahan performing...  \n",
       "28     How the Murshidabad triple murder was given a ...  \n",
       "29     Old video of earthquake in Mexico shared as re...  \n",
       "...                                                  ...  \n",
       "13644  \"Coro virus was created in a lab,\" I did not s...  \n",
       "13645  \"P & T Colony, Dilsukhnagar some of the curren...  \n",
       "13646  Scroll back to the TV 9 News: Coro veyincukunn...  \n",
       "13647   These photos of social distancing in a market...  \n",
       "13648  The old video box, sending haidraksiklorokvin ...  \n",
       "13649  This Nita Ambani’s tweet regarding the Palghar...  \n",
       "13650  A video clip from a web series is being falsel...  \n",
       "13651  Fact Check: Video of mufti challenging lockdow...  \n",
       "13652  Fact Check: No, this woman was not raped insid...  \n",
       "13653  Fact Check: Baseless rumours about slashing pe...  \n",
       "13654  Fact Check: Viral post appealing for Facebook ...  \n",
       "13655  Fact Check: Yediyurappa did not attend Kumaras...  \n",
       "13656  This video is not of Mount Kailash near Manasa...  \n",
       "13657  ‘Say Namaste’ video conferencing app has not b...  \n",
       "13658  Fact Check: This picture is NOT from Indonesia...  \n",
       "13659  Fact Check: Old video being shared with false ...  \n",
       "13660  Pakistani leader killed in celebratory firing ...  \n",
       "13661  Fact Check: Maharashtra Home Minister did not ...  \n",
       "13662  Old picture of birds in the middle of road sha...  \n",
       "13663  Fact Check: Disturbing news from 2015 being sh...  \n",
       "13664  Did Japanese Noble Prize Winner, Tasuku Honjo,...  \n",
       "13665  Fact Check: Wrong number being shared as helpl...  \n",
       "13666  Fact check: Did BJP men torture youth during l...  \n",
       "13667  Fact Check: Are these sadhus marching towards ...  \n",
       "13668  Fact Check: American students did not sing Ind...  \n",
       "13669  Fact Check: Former CJI Ranjan Gogoi did not ma...  \n",
       "13670  Fact Check: Delhi’s LG has not banned Azaan; h...  \n",
       "13671  Fact Check: Picture of nurse tending to a musl...  \n",
       "13672  Fact Check: Did food scarcity due to lockdown ...  \n",
       "13673  Fact Check: Is this video of airport staff man...  \n",
       "\n",
       "[13674 rows x 15 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"all_text\"] = df[\"eng_headline\"] + \" \" + df[\"content_eng\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Madhu Kishwar tweets photoshopped image of Amul Ad targeting Gandhi family              ,                 (Says everything without saying anything -translated) , reads a tweet by academician and writer Madhu Purnima Kishwar with a photograph of billboard, which showed the trademark Amul girl along with cartoons of Congress leader Rahul Gandhi and Priyanka Gandhi. The billboard had an inscribed message that was targeted at the dynasty politics and allegation of corruptions by the Gandhi family. The message reads,               ,             ,              ,                                                           (Grandfather ate, Grandmother ate, Father ate, Mother ate and Sister you also eat and also call brother-in-law -translation) , reads the Hindi text in the billboard. The word       (ate) , contextually refers to corruption.\\nPhotoshopped image\\nAlt News found that the Ad banner used in the billboard is photoshopped with the Hindi text stated above. A reverse search of the image on Google reveals that there are several images of the same car and billboard with different banners, which goes to suggest that it has been photoshopped.\\nMoreover, the Amul Ad banner, which comprises of the iconic Amul girl, Priyanka Gandhi and Rahul Gandhi has also been photoshopped with the Hindi text and the Amul slogan (The Taste of India). Amul had tweeted the original photo of the Amul Ad dedicated to Priyanka Gandhi s entry into active politics before the 2019 general elections.\\nBoom also spoke to daCunha, the advertisement agency behind the campaign and confirmed,  the viral photo was fake and it didn t come from the agency.  \\nIn conclusion, academician Madhu Kishwar tweeted a photoshopped image of Amul Ad, which targeted the Gandhi family portraying it as the official advertisement from the company. In the past as well, the writer and academician has been found spreading misinformation on several occasions (1, 2, 3, 4). Last December, Kishwar tweeted an old video of a rally with a false claim that Pakistani flags were waved by the Muslim community in celebration of the Congress  electoral victory in three assembly elections. When it was pointed out that she had posted misinformation, she tweeted another misleading video to defend her last tweet.\\nDonate Now\\nEnter your email address to subscribe to Alt News and receive notifications of new posts by email.\\nSend this to a friend'"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"all_text\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop([\"Unnamed: 0\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"factcheckedstories_alltext_withtrans.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save stories in a variable\n",
    "raw_all = df[\"all_text\"].values.tolist()\n",
    "\n",
    "raw_all = list(map(remove_noneng, raw_all))\n",
    "\n",
    "all_tokens = list(sent_to_words(raw_all))\n",
    "# Creating stop words list\n",
    "stop_words = stopwords.words(\"english\")\n",
    "# Adding domain words \n",
    "stop_words.extend([\"fake\", \"fact\", \"check\", \"checked\", \"factcheck\", \"news\", \"false\", \n",
    "                   \"falsely\", \"true\", \"truth\", \"viral\", \"video\", \"image\", \"picture\", \n",
    "                   \"photo\", \"claim\", \"claiming\", \"share\", \"clip\", \"misleading\"])\n",
    "# noise words - \"adsbygoogle\",\"window\", \"ad\", \"data\", \"td_screen_width\", \"push\", \"article\", \"style\",\n",
    "#                    \"width\",\"height\",\n",
    "#                   \"client\", \"slot\", \"inline\", \"display\", \"class\", \"block\", \"ins_class\", \"block_px\",\"ca_pub\"]\n",
    "# Stop word removal\n",
    "data_stopped = [[word for word in doc if word not in stop_words] for doc in all_tokens]\n",
    "# Creating bigrams\n",
    "bigram = gensim.models.Phrases(data_stopped, min_count=10)\n",
    "for idx in range(len(data_stopped)):\n",
    "    for token in bigram[data_stopped[idx]]:\n",
    "        if '_' in token:\n",
    "            # If token is bigram, add it to document\n",
    "            data_stopped[idx].append(token)\n",
    "            \n",
    "data_with_bigrams = data_stopped\n",
    "# Lemmatizing i.e. reducing words to their root form\n",
    "# Including only nouns as this improves both topic interpretability and coherence scores\n",
    "def lemmatization(docs, allowed_postags=[\"NOUN\", \"PROPN\"]):\n",
    "    nlp = spacy.load(\"en_core_web_sm\")\n",
    "    docs_out = []\n",
    "    for sent in docs:\n",
    "        doc = nlp(\" \".join(sent))\n",
    "        docs_out.append([token.lemma_ for token in doc if token.pos_ in allowed_postags]) \n",
    "    return docs_out\n",
    "\n",
    "data_lemmatized = lemmatization(data_with_bigrams, allowed_postags=[\"NOUN\", \"PROPN\"])\n",
    "\n",
    "# Removing any stopwords created because of lemmatization\n",
    "data_cleaned = [[word for word in doc if word not in stop_words] for doc in data_lemmatized]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique tokens: 44433\n"
     ]
    }
   ],
   "source": [
    "# Creating a dictionary\n",
    "id2word = corpora.Dictionary(data_cleaned)\n",
    "# Creating a document-term matrix\n",
    "print('Number of unique tokens: %d' % len(id2word))\n",
    "id2word.filter_extremes(no_below = 20)\n",
    "# Creating a document-term matrix\n",
    "corpus = [id2word.doc2bow(doc) for doc in data_cleaned]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-18 18:00:00,424 [10356] WARNING  gensim.models.ldamodel:145: [JupyterRequire] updated prior is not positive\n",
      "2020-05-18 18:00:00,705 [10356] WARNING  gensim.models.ldamodel:145: [JupyterRequire] updated prior is not positive\n",
      "2020-05-18 18:00:00,949 [10356] WARNING  gensim.models.ldamodel:145: [JupyterRequire] updated prior is not positive\n",
      "2020-05-18 18:00:01,203 [10356] WARNING  gensim.models.ldamodel:145: [JupyterRequire] updated prior is not positive\n",
      "2020-05-18 18:00:01,428 [10356] WARNING  gensim.models.ldamodel:145: [JupyterRequire] updated prior is not positive\n",
      "2020-05-18 18:00:01,635 [10356] WARNING  gensim.models.ldamodel:145: [JupyterRequire] updated prior is not positive\n",
      "2020-05-18 18:00:01,852 [10356] WARNING  gensim.models.ldamodel:145: [JupyterRequire] updated prior is not positive\n",
      "2020-05-18 18:01:18,530 [10356] WARNING  gensim.models.ldamodel:145: [JupyterRequire] updated prior is not positive\n",
      "2020-05-18 18:01:18,796 [10356] WARNING  gensim.models.ldamodel:145: [JupyterRequire] updated prior is not positive\n",
      "2020-05-18 18:01:19,031 [10356] WARNING  gensim.models.ldamodel:145: [JupyterRequire] updated prior is not positive\n",
      "2020-05-18 18:02:35,268 [10356] WARNING  gensim.models.ldamodel:145: [JupyterRequire] updated prior is not positive\n",
      "2020-05-18 18:02:35,533 [10356] WARNING  gensim.models.ldamodel:145: [JupyterRequire] updated prior is not positive\n",
      "2020-05-18 18:03:55,484 [10356] WARNING  gensim.models.ldamodel:145: [JupyterRequire] updated prior is not positive\n",
      "2020-05-18 18:05:15,136 [10356] WARNING  gensim.models.ldamodel:145: [JupyterRequire] updated prior is not positive\n"
     ]
    },
    {
     "data": {
      "image/png": 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1jwzn8iEJzF1zgOPFZY5fzxjTdNX6F0lEQoGHVfUBVR3pfvxKVYv8FJ9x2DWjkjlZUs48a+g2xtSi1mShquXACD/FYgIgNakD/bpE8ZZNLmiMqYU3dR2rRWSeiEwTkSmVD8cjM35ROaJ7/f581tuIbmNMDbxJFjFALnAecLn7cZmTQRn/mpyaSKuwEN5abncXxpjq1dkbSlVv8EcgJnCiW4dz2ZAE5q7ezwOX9qdtK286yRljWhJvRnB3E5H3RCRbRA6LyL9FpJs/gjP+M3VUEidKynl/rTV0G2O+zZtqqFeAeUACkAi8795mmpHhyR3p07mdNXQbY6rlTbKIU9VXVLXM/XgViHM4LuNnlQ3da7Py2XjAGrqNMafzJlkcEZHrRCTU/bgOV4O3aWaucDd0z1q2L9ChGGOCjDfJ4kbge8Ah4CBwlXtbnURkgohsFZEdInJ/NftniEiOiKxxP27y2DddRLa7H9O9++eYxujQJoLvDO7KnNX7OVliI7qNMf9TZ7JQ1b2qOlFV41Q1XlUnq+qeuo5zj/5+FrgEGABcIyIDqin6tqoOcz/+6T42BvgNMArXjLe/EZGO9fh3mQa6ZlQyhcVlfLDuYKBDMcYEEW96Q80UkQ4erzuKyMtenDsd2KGqmapaAswCJnkZ18XAAlXNU9WjwAJggpfHmkZI696RM+KtodsYczpvqqGGqOqxyhfuP96pXhyXCHhWfme5t1V1pYisE5F3RSSpnscaH6ts6F699xibDxYEOhxjTJDwJlmEeFYBuauIvBm1Vd06oFVX2XkfSFHVIcBnwMx6HIuI3CwiK0RkRU5OjhchGW9MSU0kIiyEWXZ3YYxx8yZZ/BlYIiKPisijuNay+KMXx2UBSR6vuwGnjfhS1VxVLXa/fJH/TVpY57Hu419Q1TRVTYuLs968vtKxbQSXDurC7NX7OVVSHuhwjDFBwJsG7teAK4HDQDYwRVVf9+Lcy4HeItJDRCKAq3EN7vuGiHT1eDkR2Ox+/glwkbt9pCNwkXub8ZOr05MpLCrjw/XW0G2M8a6BuxewU1WfAdYDF3g2eNdEVcuAO3D9kd8MvKOqG0XkERGZ6C52l4hsFJG1uFbgm+E+Ng94FFfCWQ484t5m/GRUjxh6xra1hm5jDACi+q2mgNMLiKwB0oAU4GNc7Qx9VfVSx6Orh7S0NF2xYkWgw2hWXlycyWPzN/PJPefSt0tUoMMxxjhARFaqalpd5bxps6hw3yVMAf6iqvcCXes4xjQDV47oRkRoiN1dGGO86tVUKiLXANfjWssCINy5kEywiGkbwcWDuvBGxh6WZubSuX0kndu3onP7SOLbR9I5qhVdoiPp3D6STm0jCPPDuuHGmMDwJlncANwKPKaqu0SkB/AvZ8MyweLHF/ahQ+twDuYXkV1YxJZDBeQUFlNRpfYyRCC2XSt3QvlfUuncvpU7sUSSEtuGNhG2VoYxTVGdbRZNhbVZ+E95hZJ7vJhDBUUcLijmcEER2e7nrm1FZBcWk3ei5LTjukZHsvhn4wm3OxBjgoa3bRb2Nc/UW2iIEO+uiqpNcVk52QXFZBcWsWRHLn9esI0vdxxhfN94P0VqjPEV+4pnHNMqLJSkmDaM6B7DLWN7Ed06nLmr9wc6LGNMA3idLESkrZOBmOYtIiyESwd35dNNh236c2OaIG8G5Y0RkU24R1eLyFAR+bvjkZlmZ/KwBE6WlLNg0+FAh2KMqSdv7iyewjVleC6Aqq4FznUyKNM8jUyJISE6kjlWFWVMk+NVNZSqVl1n02aXM/UWEiJMHJbI4u1HyD1eXPcBxpig4U2y2CciYwAVkQgR+Qn/m/DPmHqZNCyB8gq1CQqNaWK8SRa3Aj/CtfhQFjDM/dqYeuvftT19O0cxd823Zpw3xgQxb6YoP6Kq16pqZ/ca3Nepaq4/gjPN06TUBFbuOcq+vJOBDsUY4yUn1+A2ploThyYAMHeNNXQb01Q4uQa3MdXq1rEN6SkxzFlzgOYy3YwxzZ2Ta3AbU6NJqQnsyD7OxgMFgQ7FGOMFJ9fgNqZGlw7qSnioWFWUMU2Et2twX0X91+A2pkYd20Ywtk8c89YeoLzqfOfGOEBVKSq1IWIN5e3cUFuA2cBc4LiIJDsXkmkpJg1L5HBBMRmZ1rnOOGtf3kmmvbSMEY8usF54DeRNb6g7cd1VLAA+AD50/zSmUS7o35m2EaE25sI4pqJCmblkNxc/vZjVe49SWqH89T/bAx1Wk+TNncXdQF9VHaiqQ1R1sKoOcTow0/y1jgjl4kFdmL/hoFUPGJ/bdeQEV7+wlN/M20haSgyf3jeWaaO78+9VWezMOR7o8Hxm0bYc/rPZ+ck5vZruA8h3OhDTMk0elkhhURlfbM0OdCimmSivUF5cnMmEpxez5VABf7pqCDNvGElih9bcNq4XkeGhPLVgW6DD9IkN+/O5/V8refqz7Y63/XnTBTYT+EJEPgS+mf1NVZ90LCrTYozp1YnYdq2Ys/oAEwZ1DXQ4ponbfriQn767jjX7jnFB/848dsUgOnus6BjbrhU3ntWDZxbu4LZx+QxMiA5gtI2TdfQkN7y6nOjW4fxzehqhIeLo9by5s9iLq70iAojyeBjTaGGhIVw+tCufb8km/1RpoMMxTVRpeQXPLtzBd/76JXtyT/CXq4fx4vUjTksUlX54bk/aR4bx5KdN9+4i/2QpM15ZTlFpOa/emF7tv9PX6ryzUNWHwbVSnqqecDwi0+JMGpbIK1/t5uMNB/n+SOtoZ+pn44F8fvbuOjYeKOA7Q7ry8MSBxLZrVWP56Nbh3DK2F3/6ZCur9h5leHLHGssGo+Kycn74+gr25p5k5o3p9Onsn+/u3vSGOtNWyjNOGtotmpRObZiz2npFGe8Vl5Xz5KdbmfTMVxwuKOa564bz7NThtSaKSjeclUJsuwie+GSrHyL1nYoK5cfvrGXZrjye+N5QzuzVyW/X9qYa6mkauFKeiEwQka0iskNE7q+l3FUioiKS5n6dIiKnRGSN+/GcN9czTZOIMGlYIkt35XIovyjQ4ZgmYO2+Y1z+ty/56+c7mDg0gc/uO7debV5tIsK4fdwZLNmZy5IdRxyM1Lce/3gLH6w7yC8u6ffNhJz+4thKeSISCjwLXAIMAK4RkQHVlIsC7gIyquzaqarD3I9bvYnTNF2TUxNRhffX2t2FqVlRaTm//2gzV/z9KwpOlfHyjDSe/P4wOrSJqPe5po5Kpmt0JH/6dGuTmNDy1a928cLiTK4/szs3n9vT79d3cqW8dGCHqmaqagkwC5hUTblHcc01ZV8pW7AesW0Z2i2aOTZXlKnBit15XPqX//L8oky+PzKJT+87l/P6dW7w+SLDQ7nr/N6s3nuM/2wO7q7bH284xMMfbOLCAZ35zeUDEXG251N1nFwpLxHXGI1KWe5t3xCRVCBJVasbEd5DRFaLyCIROceL65kmbtKwRDYeKGBHdmGgQzFB5GRJGQ/N28h3n/+akvIK/vWDUfx+yhDaR4Y3+txXjehG905teOLTrVQE6RxlK/cc5e5ZqxmW1IG/Xp3qeBfZmtSaLNxVSdMauFJedf+ib34bIhICPAX8uJpyB4FkVU0F7gPeFJH21cR3s4isEJEVOTk5XoRkgtllQ7sSIlhDd4Dlnyrl3rfXMH/9wYBXz3y5/QgXPbWYV5fsZvqZKXxyz7mc3TvWZ+cPDw3h3gv6sOVQYVCuC5+Zc5ybZi6na3Qk/7w+jdYRoQGLpdZkoarlVF915I0sIMnjdTfA869AFDAI14C/3cBoYJ6IpKlqcWVCUtWVwE6gTzXxvaCqaaqaFhcX18AwTbCIj4rkrDNimbt2f8D/SLVkj3+0mfdW7+f2N1Zxw6vL2Zvr/4n38k+V8vN313HdSxmEh4bwzi1n8tDEgbRt5fuldC4fmkDfzlE8tWAbZeUVPj9/Qx05XsyMV5YjIrx6QzqdvOjl5SRvqqG+EpFnROQcERle+fDiuOVAbxHpISIRwNXAvMqdqpqvqrGqmqKqKcBSYKKqrhCROPddDSLSE+iNayS5aeYmDUtkX94pVu09GuhQWqSlmbm8tWwfN57Vg19fNoDlu/K48KlFPLtwByVl/vlDumDTYS56ahHvrsri1rG9+Ojuc0jvEePY9UJDhPsu6kPmkRPMXh0cbWYnS8r4wavLyS4s4qXpaaTEtg10SF5N9zHG/fMRj20KnFfbQapaJiJ3AJ8AocDLqrpRRB4BVqjqvFoOPxd4RETKcPW8ulVV87yI1TRxFw/szAPvhTBn9QFGdHfuD4T5tqLScn45ez1JMa356cV9aR0RyqWDu/DI+5v40ydbeW/1fn47eRCjezrTtz/3eDEPvb+J99ceoF+XKP55/UgGd/PPdBwXDejM0G7R/OWz7UwalkCrsMBV95SVV3DXW6tZvz+f56elkRokgwaludzup6Wl6YoVKwIdhvGBH725iq935pLxy/MJD/V2yRXTWE98spVnFu7g9R+kc07v06t1P99ymAfnbiTr6CmuHN6NX17az2fVIqrKvLUHePj9TRQWlXLneb25dWwvIsL8+7tfvC2H619exiOTBnL9mSl+vXYlVeXXczfwr6V7eXTSQKb5IQ4RWamqaXWV82YEd2cReUlEPnK/HiAiP/BFkMZUZ/KwRPJOlPDl9qYzWKqp23KogOcW7WTK8MRvJQqA8/p1ZsG9Y7l9XC/mrtnP+U8u4u3lexvdg+hQfhE3zVzB3bPWkBzThg/vOoe7zu/t90QBcE7vWNJ7xPC3z3dwqiQwU+b/Y9FO/rV0L7eO7eWXRFEf3vxGXsVVlVQ5XHAbcI9TARkztk8cHdqE25gLPymvUH7+7/VEtw7n19/51rjZb7SOCOVnE/ox/+5z6BMfxc//vZ7vPf81Ww4V1Puaqspby/Zy4ZOL+GrnEX71nf78+7YxfpvnqDoiwk8v7ktOYTEzv97t9+vPWb2fP368lYlDE/jZxX39fv26eJMsYlX1HaACXG0ReDGC25iGiggL4dLBXfl042FOFJcFOpxm77Wvd7N23zEevHwAHdvWPRK6T+co3r5lNH+6agg7c45z2V+/5PcfbeZkiXe/q725J7n2nxn8YvZ6BiVG88k953LTOT0DNn7A08iUGMb2ieO5RTspKPLfLMhLdhzhp++uZXTPGP703SGEBMF7UZU3yeKEiHTCPUZCREZjiyEZh00elsip0nIWbHJ+BbCWbP+xU/zpk62M6xtXr7mGRITvpiXx+Y/HMWV4Is8vyuTCJxfzWS2/r/IK5aUvd3Hx04tZl5XP764YzJs/HEX3ToHv6ePpJxf15djJUl767y6/XG/LoQJueX0lPWLb8vy0tIA2rtfGm2RxH64ur71E5CvgNeBOR6MyLV5a944kREdaVZSDVJVfvbcegN9OHtSgKSQ6to3gj1cN5Z1bzqRtq1Buem0FN7+2gv3HTp1Wbkd2IVc9t4RHP9jEmb06seC+c5k6Kjkg01bUZXC3aC4Z1IWXvtzF0RMljl7rYP4pbnhlOW1ahfLKDelEt278qHSn1JksVHUVMBZXF9pbgIGqus7pwEzLFhIiTByWyH+3H+HI8eK6DzD19v66gyzcmsOPL+pLt45tGnWu9B4xfHDnOfx8Qj8Wb8/hwicX8eLiTIpKy3nm8+1c+pcv2X3EtSjRS9PT6Brd2kf/Cmfcd2EfTpSU8dyinY5do7ColBteWU5hURmvzEgnsUNwvyfedjlIB4YCw3HNHnu9cyEZ4zI5NYHyCmV+EE7D0NQdPVHCw/M2MrRbNDPGpPjknBFhIdw2rhcL7h3L6J6deGz+ZlIfWcATn27jooGdWXDfWCYNSwzKu4mqeneO4ophicz8ejeHC3w/x+nKPXlM+fsSdmQf5x/XDWdAwrdmMwo63nSdfR14AjgbGOl+1Nkn15jG6telPf26RDEnSEbVOunh9zfyRsYev13vsfmbyT9VyuNXDvF5w3JSTBtemp7Gc9eNIDW5A89PG8EzXi5KFEzuuaAPZeXKM5/v8Nk5C4tKeXDuBq567mtOlpTz8oyR1XZVDkbejOBOAwZocxm9Z5qUScMS+cPHW9ibe5LkTo2rKglWB/NP8cpXuwHYl3eKn0/o6+i37y+3H+HdlVmCYIt6AAAX6ElEQVT8aHwv+nd15hutiDBhUBcmDOriyPn9IblTG743MolZy/dy87k9SYpp3OdvwabD/HrOBg4XFjFjTAo/uaivI3NdOcWbaqgNQNP9jZsmbeIwVw+duc24oXvZLtdMNpVdNn/8f2spdWhCu1Ml5fzyvfX0iG3Lnef1duQazcmd552BiPCX/2xv8DmyC4q4/Y2V/PC1FXRoE87s28bwm8udmRTRSTUmCxF5X0TmAbHAJhH5RETmVT78F6JpyRI7tCa9Rwxz1jTfmWiXZuYRFRnGyzNGct+FfZi9aj83zVzhyBiTp/+zjb15J/n9lMFEhgdnF81g0jW6NdeP7s7sVVnsyD5er2NVlVnL9nL+k4v4bHM2P724L+/feXbQzPVUX7Wltif8FoUxtZg0LIEH3tvAxgMFDEr0z8Ry/pSxK5eRKTGEhgh3nd+b+KhW/PK99Ux9cSkvzxjpszmYNuzP55//3cXVI5McmwywObptXC/eWraXpz7bxrNTvZlw27UOxS9mrydjVx6jesTw+ymD6RnXzuFInVXjnYWqLqp8AFtwrT8RBWx2bzPGL74zuCvhodIsG7qzC4vIzDnBKI8puK9OT+b5aWlsOVTIlf9Y4pP1JMrKK7h/9jpi2kbwi0v6N/p8LUmndq248ewefLjuIBsP1D4euaSsgmc+386Ev/yXzQcLeHzKYN764egmnyjAu95Q3wOWAd8FvgdkiMhVTgdmTKUObSIY2yeeeWsPUB6kS182VGV7xagq3/QvHNCZN384iqMnS5nyjyVs2N+4SRNe+Wo3G/YX8PDEgUS3Cd6BX8HqpnN60j4yjD9/uq3GMqv3HuXyv33JE59u48L+nfnsx2O5Oj05KKfuaAhvGrgfAEaq6nRVvR7XmItfOxuWMaebnJpAdmExGZnerOjbdCzblUfbiFAGVdPPfkT3GP5925lEhApXv7C0wbPw7s09yZ8XbOWC/p25pAn3Tgqk6Nbh3DK2F59vyWblntMX5jpe7FojfMo/lpB/qpQXr0/j2WuHEx8VGaBoneFNsghR1WyP17leHmeMz1zQvzPtWoU1u+k/MjLzGJESQ1gN63acER/F7NvPIrFDa254dRnz1tZvfXJV5YE56wkLCeHRyQObxIC4YHXDWSnEtovgiU+2frPt8y2HuejJRcz8ejfTRndnwX3ncuGAzoEL0kHe/NH/2N0TaoaIzAA+BD5yNixjThcZHsrFA7vw0fpDFJU2j0mP806UsPVw4WntFdXpEh3JO7ecSWpSR+56azUvfen9BHezV+3nv9uP8PMJfYN+io1g1yYijNvHncHXmbnMXbOfO99azY2vrqBtqzDevfVMHpk0iKjI5lvF583cUD8FngeG4Jry4wVV/ZnTgRlT1eTUBAqLy1i4Jbvuwk3AN+0VXqwvHd0mnNd+kM6EgV149INN/H7+5joXHso9XsxvP9zEiO4duXZUd5/E3NJNHZVM1+hI7p61hk82HOLeC/rwwV1nt4glgGsbZ3GGiJwFoKqzVfU+Vb0XyBWRXn6L0Bi3M3t2IrZdK95Zsa9ZjLnI2JVLZHgIQ7p18Kp8ZHgoz147nGmju/P84kx+/H9rKSmrefDeox9s4nhxGY9PGdxsGlkDLTI8lN9OHsSEgV2Yf/fZ3H1B76CdUtzXaruzeBoorGb7Sfc+Y/wqLDSEGWO6s3Brjk/n6wmUjMw8hid3rNcSoqEhwiOTBvKTi/rw3ur9/GDm8moH7y3cms2cNQf40fgz6B3A1eeao/P7d+a5aSM4I75lva+1fUpTqpuKXFVXACmORWRMLW4fdwZTUhP584JtvJmxN9DhNFj+qVI2HypgVI/6D44TEe44rzd/uHIwS3bmcs2LS0+bxv1EcRm/em8DZ8S347ZxVglgfKO2ZFFbvy9rKTMBERIi/OGqIYzvG8ev5qzn4w1Nc/ryFbvzUHWtA9FQ3x+ZzAvTRrDtsGvw3p7cEwD8+dNtHMg/xR+uHNxiqkiM82pLFstF5IdVN4rID4CVzoVkTO3CQ0N49trhDE3qwF2z1rC0CY69yNiVR0RoCKnJ3rVX1OT8/p1546bR5J8q5cp/LOGtZXt5dckurhvVvUU0uhr/qS1Z3APcICJfiMif3Y9FwE3A3f4Jz5jqtYkI4+XpI0mOacMPZ66ocxqGYJORmcuwpA4+mcxvRPeOvHvrGFqFhfKL2euJj4rkZxP6+iBKY/6ntrmhDqvqGOBhYLf78bCqnqmqh/wTnjE169g2gtduTKddZBjTX17+TTVMsDteXMaGAwWM6um7b/5nxLdj9u1juGRQF578/tBm3d/fBIY34ywWqurf3I/P/RGUMd5K6NCa13+QTllFBde/vIycwuBfr3vlnqOUV2iDGrdr07l9JP+4bgRjesX69LzGgMPTdojIBBHZKiI7ROT+WspdJSIqImke237hPm6riFzsZJymaTsjPoqXZ4wku6CYGa8so7CoNNAh1SojM5ewEGF498a1VxjjT44lCxEJBZ4FLgEGANeIyIBqykUBdwEZHtsGAFcDA4EJwN/d5zOmWsOTO/L364az9VAhN7+2MqinBMnYlcfgbtG0iWhaK6WZls3JO4t0YIeqZqpqCTALmFRNuUeBPwJFHtsmAbNUtVhVdwE73Oczpkbj+8bzp+8O4evMXO59e01QTmd+qqScdVnHfF4FZYzTnEwWicA+j9dZ7m3fEJFUIElVP6jvscZU54rUbvzqO/35aMMhHpy7IeimBVm19yil5erTxm1j/MHJ++DqJqP55n+uiIQATwEz6nusxzluBm4GSE5OblCQpvm56Zye5Bwv5vlFmcS2a8W9F/YJdEjfyMjMJUQgrXvTXIfZtFxOJossIMnjdTfAczL+KGAQ8IV7jv0uwDwRmejFsQCo6gvACwBpaWnB9RXSBNT9E/qRe7yEv/xnO7FRrZg2OjhmXc3YlcegxGjr2mqaHCeroZYDvUWkh4hE4Gqwnle5U1XzVTVWVVNUNQVYCkx0zz01D7haRFqJSA+gN66lXY3xiojw+JTBnN8vngfnbuDDdYGfFqSotJzV+46RnmJVUKbpcSxZqGoZcAfwCbAZeEdVN4rII+67h9qO3Qi8A2wCPgZ+pKrB273FBKWw0BCemTqcEckdufftNSzZ0bBlSX1l7b5jlJRVfGu9bWOaAkfHWajqfFXto6q9VPUx97YHVXVeNWXHue8qKl8/5j6ur6raynymQVpHhPLS9JGkxLbhh6+tYMP+wE0LkrErDxHszsI0SbaWtmn2otuE89qNo+jQJoIZryxj95HATAuSsSuXfl3aE93G2itM02PJwrQIXaIjmXljOuUVyrSXM8guKKr7IB8qKatg5Z6jXi2hakwwsmRhWowz4tvxyg3p5B4vYforyynw47Qg6/fnU1RawWgbX2GaKEsWpkUZltSB564bwfbDhfz83W8tBOmYjF2uNTdGWnuFaaIsWZgW59w+cdw2rhcfbzzE3tyTfrlmRmYevePb0aldK79czxhfs2RhWqTrRncnVITXvt7t+LXKyitYsTvPpvgwTZolC9MidW4fyYRBXXhnxT5OlpQ5eq2NBwo4UVJukweaJs2ShWmxZoxJoaCojDmrvzWTjE8t25UHYD2hTJNmycK0WCO6d2RA1/bMXLLb0dlpM3bl0iO2LfHtIx27hjFOs2RhWiwRYcaYFLYeLiTD/e3f18orlGW78uyuwjR5lixMizZxWAId2oQzc8luR86/5VABBUVl1rhtmjxLFqZFiwwP5fsjk/h002EOHDvl8/NnZFa2V1jjtmnaLFmYFm/a6O6oKv9ausfn587YlUtSTGsSOrT2+bmN8SdLFqbF69axDRf078ys5fsoKvXdTPiqle0Vdldhmj5LFsbg6kabd6KED3y4SNL27OMcPVlKujVum2bAkoUxwJm9OtE7vp1Pu9FmZLrmgxptdxamGbBkYQyubrTXj0lh/f58Vu875pNzLt2VR9foSJJirL3CNH2WLIxxm5KaSFSrMJ90o1VVMjJd4ytEpPHBGRNgliyMcWvbKoyr0roxf/1BsgsbtzjSriMnOHK82NbbNs2GJQtjPFx/Zgql5cqbGXsbdZ4Mmw/KNDOWLIzx0CO2LeP6xvFGxl5KyioafJ6MzFxi27WiR2xbH0ZnTOBYsjCmiulnppBTWMzHGw816HhVJWOXa/0Ka68wzYUlC2OqGNsnjpRObRrc0L0v7xQH84sYbVVQphmxZGFMFSEhwrQzU1i55ygb9ufX+/il7vW2rXHbNCeWLIypxlUjutE6PLRBdxfLduUR0zaC3vHtfB+YMQFiycKYakS3DmfK8ETmrj1A3omSeh2bsSuXkSkdrb3CNCuOJgsRmSAiW0Vkh4jcX83+W0VkvYisEZEvRWSAe3uKiJxyb18jIs85Gacx1Zk+JoWSsgpmLfe+G+2BY6fYl3fKJg80zY5jyUJEQoFngUuAAcA1lcnAw5uqOlhVhwF/BJ702LdTVYe5H7c6FacxNenTOYoze3bijaV7KSv3rhttxjftFda4bZoXJ+8s0oEdqpqpqiXALGCSZwFVLfB42RZwbiFkYxpg+pgU9h87xWebs70qn5GZR/vIMPp1ae9wZMb4l5PJIhHY5/E6y73tNCLyIxHZievO4i6PXT1EZLWILBKRcxyM05gaXdA/nsQOrb1u6F62K4/0HjGEhlh7hWlenEwW1f1v+dadg6o+q6q9gJ8Dv3JvPggkq2oqcB/wpoh866uaiNwsIitEZEVOTo4PQzfGJSw0hOtGd+frzFy2HS6stWx2QRGZR05Ye4VplpxMFllAksfrbsCBWsrPAiYDqGqxqua6n68EdgJ9qh6gqi+oapqqpsXFxfkscGM8fX9kEhFhIXXeXVTOB2WLHZnmyMlksRzoLSI9RCQCuBqY51lARHp7vPwOsN29Pc7dQI6I9AR6A5kOxmpMjWLaRjBpaAKzV+0n/1RpjeUyduXSrlUYAxOsvcI0P44lC1UtA+4APgE2A++o6kYReUREJrqL3SEiG0VkDa7qpunu7ecC60RkLfAucKuq5jkVqzF1mT4mhVOl5fzfin01lsnIzGNE946EhdrwJdP8hDl5clWdD8yvsu1Bj+d313Dcv4F/OxmbMfUxKDGaEd078vrSPdx4Vg9CqjRg5x4vZnv2ca4Y/q0+HMY0C/YVyBgvTR+Twp7ckyza9u3OFMt3V65fYY3bpnmyZGGMlyYM7EJ8VCteraahe2lmHpHhIQxOjPZ/YMb4gSULY7wUERbC1FHJLNqWw64jJ07bl7HL1V4REWb/pUzzZJ9sY+ph6qhkwkOF177e/c22/JOlbDlUYFVQplmzZGFMPcRHRXLp4K68uyKLE8VlACzbnYeqrbdtmjdLFsbU0/VnplBYXMbsVVmAa73tiLAQhiZ1CHBkxjjHkoUx9TQ8uQODE6OZ+fUeVJVlu/NITepAZHhooEMzxjGWLIypJxFh+pgUdmQf59NNh9mwP9+qoEyzZ8nCmAa4bEhXYtpG8Ks5G6hQW2/bNH+WLIxpgMjwUK4emUROYTHhocLw5I6BDskYR1myMKaBrhvdndAQYUi3DrSOsPYK07w5OjeUMc1ZQofWPDRxIN1j2gQ6FGMcZ8nCmEaYNrp7oEMwxi+sGsoYY0ydLFkYY4ypkyULY4wxdbJkYYwxpk6WLIwxxtTJkoUxxpg6WbIwxhhTJ0sWxhhj6iSqGugYfEJEcoA9Dl8mFjji8DV8qanFCxazvzS1mJtavNB0Yu6uqnF1FWo2ycIfRGSFqqYFOg5vNbV4wWL2l6YWc1OLF5pmzLWxaihjjDF1smRhjDGmTpYs6ueFQAdQT00tXrCY/aWpxdzU4oWmGXONrM3CGGNMnezOwhhjTJ0sWXgQkSQRWSgim0Vko4jcXU2ZcSKSLyJr3I8HAxFrlZh2i8h6dzwrqtkvIvJXEdkhIutEZHgg4vSIp6/H+7dGRApE5J4qZQL+PovIyyKSLSIbPLbFiMgCEdnu/lnteqoiMt1dZruITA9wzH8SkS3u3/17ItKhhmNr/Rz5Md6HRGS/x+/+0hqOnSAiW92f6/v9EW8tMb/tEe9uEVlTw7F+f499RlXt4X4AXYHh7udRwDZgQJUy44APAh1rlZh2A7G17L8U+AgQYDSQEeiYPWILBQ7h6usdVO8zcC4wHNjgse2PwP3u5/cDf6jmuBgg0/2zo/t5xwDGfBEQ5n7+h+pi9uZz5Md4HwJ+4sXnZifQE4gA1lb9v+rPmKvs/zPwYLC8x7562J2FB1U9qKqr3M8Lgc1AYmCj8olJwGvqshToICJdAx2U2/nATlV1ekBlvanqYiCvyuZJwEz385nA5GoOvRhYoKp5qnoUWABMcCxQD9XFrKqfqmqZ++VSoJs/YvFGDe+xN9KBHaqaqaolwCxcvxvH1RaziAjwPeAtf8TiT5YsaiAiKUAqkFHN7jNFZK2IfCQiA/0aWPUU+FREVorIzdXsTwT2ebzOIniS4NXU/B8r2N5ngM6qehBcXy6A+GrKBPP7fSOuu8zq1PU58qc73NVmL9dQ1Res7/E5wGFV3V7D/mB6j+vFkkU1RKQd8G/gHlUtqLJ7Fa4qk6HA34A5/o6vGmep6nDgEuBHInJulf1SzTEB7wYnIhHAROD/qtkdjO+zt4L1/X4AKAPeqKFIXZ8jf/kH0AsYBhzEVa1TVVC+x8A11H5XESzvcb1ZsqhCRMJxJYo3VHV21f2qWqCqx93P5wPhIhLr5zCrxnTA/TMbeA/XLbqnLCDJ43U34IB/oqvVJcAqVT1cdUcwvs9uhyur8Nw/s6spE3Tvt7uR/TLgWnVXnlflxefIL1T1sKqWq2oF8GINcQTjexwGTAHerqlMsLzHDWHJwoO7vvElYLOqPllDmS7ucohIOq73MNd/UX4rnrYiElX5HFdj5oYqxeYB17t7RY0G8iurUgKsxm9hwfY+e5gHVPZumg7MrabMJ8BFItLRXYVykXtbQIjIBODnwERVPVlDGW8+R35RpT3tihriWA70FpEe7jvUq3H9bgLpAmCLqmZVtzOY3uMGCXQLezA9gLNx3cquA9a4H5cCtwK3usvcAWzE1ftiKTAmwDH3dMey1h3XA+7tnjEL8Cyu3iPrgbQgeK/b4PrjH+2xLajeZ1yJ7CBQiuub7A+ATsB/gO3unzHusmnAPz2OvRHY4X7cEOCYd+Cq36/8TD/nLpsAzK/tcxSgeF93f07X4UoAXavG6359Ka4eizv9FW9NMbu3v1r5+fUoG/D32FcPG8FtjDGmTlYNZYwxpk6WLIwxxtTJkoUxxpg6WbIwxhhTJ0sWxhhj6mTJwrRIIqIi8meP1z8RkYd8fI0bPGYiLfGYbfTxBpwrSURqHOxljNOs66xpkUSkCFdf+ZGqekREfgK0U9WHHLreblzjW444cX5jnGZ3FqalKsO17OW9VXeIyKsicpXH6+Pun+NEZJGIvCMi20TkcRG5VkSWue8aenl7cRGJFZF57snylojIIPf234rITHGtq7JdRG50bz+jco0EEQkTkadEZIP7+Nvd2/8kIpvc2/7QmDfHmKrCAh2AMQH0LLBORP5Yj2OGAv1xTVGdiWvUdrq4Fsq6E7intoM9PIprXZGJInIRrtG/ae59g4ExQHtglYh8WOXY23CNDB6qquXiWpCpM64RzQNVVaWGBY6MaSi7szAtlrpmFH4NuKsehy1X17onxbimmfjUvX09kFKP85yNa1oLVPVTIME9XxDAHFUtUtdkc4uBkVWOvQDXlB3l7uPzcCWvCuBFEbkCOFGPWIypkyUL09I9jWs+orYe28pw/99wT2YY4bGv2ON5hcfrCup3p151im3P11UbEqu+lqrbVLUU153JHOBKoOrdiDGNYsnCtGjub+Xv4EoYlXYDI9zPJwHhDlx6MXAtgIhcAGSpauXdwGQRaeWekv0coOpazZ8Ct4lIqPv4GPdspu1V9QNc7TCpDsRsWjBrszDGtbjOHR6vXwTmisgyXDPLOlGl8yDwioisA44DN3jsW45rNbsk4Deqerhyamu354HeuNpbynAtFvQBMFtEWuH6EnifAzGbFsy6zhoTRETkt8ARVX060LEY48mqoYwxxtTJ7iyMMcbUye4sjDHG1MmShTHGmDpZsjDGGFMnSxbGGGPqZMnCGGNMnSxZGGOMqdP/Ayw9yvMU+thQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_list, coherence_values = compute_coherence_values(dictionary=id2word, corpus=corpus, texts=data_cleaned, start=2, limit=20, step=1)\n",
    "# Show graph\n",
    "limit=20; start=2; step=1;\n",
    "x = range(start, limit, step)\n",
    "plt.plot(x, coherence_values)\n",
    "plt.xlabel(\"Num Topics\")\n",
    "plt.ylabel(\"Coherence score\")\n",
    "plt.legend((\"coherence_values\"), loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-18 18:56:46,084 [10356] WARNING  gensim.models.ldamodel:145: [JupyterRequire] updated prior is not positive\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0,\n",
      "  '0.038*\"minister\" + 0.030*\"bjp\" + 0.027*\"modi\" + 0.018*\"toilet\" + '\n",
      "  '0.017*\"sabha\" + 0.016*\"prime_minister\" + 0.015*\"party\" + 0.015*\"pm_modi\" + '\n",
      "  '0.014*\"gandhi\" + 0.013*\"congress\"'),\n",
      " (1,\n",
      "  '0.028*\"police\" + 0.027*\"man\" + 0.026*\"woman\" + 0.018*\"incident\" + '\n",
      "  '0.014*\"child\" + 0.013*\"delhi\" + 0.013*\"people\" + 0.012*\"report\" + '\n",
      "  '0.011*\"protest\" + 0.011*\"village\"'),\n",
      " (2,\n",
      "  '0.023*\"medium\" + 0.022*\"post\" + 0.016*\"india\" + 0.016*\"article\" + '\n",
      "  '0.014*\"time\" + 0.014*\"tweet\" + 0.012*\"report\" + 0.012*\"facebook\" + '\n",
      "  '0.012*\"email\" + 0.011*\"story\"'),\n",
      " (3,\n",
      "  '0.023*\"india\" + 0.022*\"government\" + 0.018*\"year\" + 0.018*\"coronavirus\" + '\n",
      "  '0.013*\"datum\" + 0.013*\"state\" + 0.010*\"health\" + 0.010*\"crore\" + '\n",
      "  '0.010*\"ministry\" + 0.008*\"country\"'),\n",
      " (4,\n",
      "  '0.062*\"adsbygoogle\" + 0.044*\"window\" + 0.036*\"ad\" + 0.035*\"data\" + '\n",
      "  '0.031*\"td_screen_width\" + 0.031*\"push\" + 0.018*\"width\" + 0.018*\"style\" + '\n",
      "  '0.018*\"height\" + 0.018*\"display\"')]\n"
     ]
    }
   ],
   "source": [
    "lda_model = ldamodel.LdaModel(corpus = corpus, num_topics = 5, id2word = id2word, chunksize=100, alpha='auto',\n",
    "    eta='auto', passes = 5, random_state = 0)\n",
    "\n",
    "# Printing the topic-word probabilities\n",
    "pprint(lda_model.print_topics())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pyLDAvis.enable_notebook()\n",
    "vis = pyLDAvis.gensim.prepare(lda_model, corpus, id2word, sort_topics=True, n_jobs=1)\n",
    "vis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Redoing text processing with adjectives, verbs and adverbs included\n",
    "def lemmatization(docs, allowed_postags=[\"NOUN\", \"PROPN\", \"VERB\", \"ADJ\", \"ADV\"]):\n",
    "    nlp = spacy.load(\"en_core_web_sm\")\n",
    "    docs_out = []\n",
    "    for sent in docs:\n",
    "        doc = nlp(\" \".join(sent))\n",
    "        docs_out.append([token.lemma_ for token in doc if token.pos_ in allowed_postags]) \n",
    "    return docs_out\n",
    "\n",
    "data_lemmatized = lemmatization(data_with_bigrams, allowed_postags=[\"NOUN\", \"PROPN\", \"VERB\", \"ADJ\", \"ADV\"])\n",
    "\n",
    "# Removing any stopwords created because of lemmatization\n",
    "data_cleaned = [[word for word in doc if word not in stop_words] for doc in data_lemmatized]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot frequency distribution of most common words in the headlines\n",
    "all_sents = [\" \".join(doc) for doc in data_cleaned]\n",
    "tokenizer = regextoken(\"[a-zA-Z0-9]+\")\n",
    "all_words = tokenizer.tokenize(str(all_sents))\n",
    "fd = FreqDist()\n",
    "for word in all_words:\n",
    "    fd[word] += 1\n",
    "    \n",
    "# Plotting the top 50 most frequent words\n",
    "# plt.figure(figsize = (15, 10))\n",
    "# fd.plot(50)\n",
    "# plt.show()\n",
    "fd_sorted={k: v for k, v in sorted(fd.items(), key=lambda item: item[1], reverse=True)}\n",
    "fd_sorted=dict(list(fd_sorted.items())[0: 100])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize = (15, 10))\n",
    "plt.suptitle(\"Top words in 13,539 story headlines on Indian factchecking sites (as of May 2020)\", fontsize= 15)\n",
    "plt.title(\"source: Tattle archive\", fontdict = {'fontsize': 12})\n",
    "plt.xlabel(xlabel = \"Word\", fontdict = {'fontsize': 13})\n",
    "plt.ylabel(ylabel = \"Count\", fontdict = {'fontsize': 13})\n",
    "plt.xticks(rotation=90, size =11)\n",
    "plt.yticks(np.arange(0, 2000, 100))\n",
    "plt.plot(fd_sorted.keys(), fd_sorted.values())\n",
    "plt.grid()\n",
    "plt.tight_layout(pad=4)\n",
    "plt.savefig(\"word_plot_stories.png\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Plot frequency distribution of most common word-pairs in the headlines\n",
    "bigrams_list=[]\n",
    "for idx in range(len(data_cleaned)):\n",
    "    for token in bigram[data_cleaned[idx]]:\n",
    "        if '_' in token:\n",
    "            # If token is bigram, add it to document\n",
    "            bigrams_list.append(token)\n",
    "\n",
    "fd = FreqDist()\n",
    "for b in bigrams_list:\n",
    "    fd[b] += 1\n",
    "    \n",
    "fd_sorted={k: v for k, v in sorted(fd.items(), key=lambda item: item[1], reverse=True)}\n",
    "fd_sorted=dict(list(fd_sorted.items())[0: 100])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize = (15, 10))\n",
    "plt.suptitle(\"Top bigrams in 13,539 story headlines on Indian factchecking sites (as of May 2020)\", fontsize= 15)\n",
    "plt.title(\"source: Tattle archive\", fontdict = {'fontsize': 12})\n",
    "plt.xlabel(xlabel = \"Bigram\", fontdict = {'fontsize': 13})\n",
    "plt.ylabel(ylabel = \"Count\", fontdict = {'fontsize': 13})\n",
    "plt.xticks(rotation=90, size =9)\n",
    "plt.yticks(np.arange(0, 800, 50))\n",
    "plt.plot(fd_sorted.keys(), fd_sorted.values())\n",
    "plt.grid()\n",
    "plt.tight_layout(pad=4)\n",
    "plt.savefig(\"bigram_plot_stories.png\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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