fake-news-detection.ipynb

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    "<p style=\"font-family: Arials; line-height: 1.3; font-size: 30px; font-weight: bold; letter-spacing: 2px; text-align: center; color: #23527c\">Exploring the news dataset</p>\n"
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    "<span style=\"font-family: Arials; font-size: 20px; font-style: bold; font-weight: bold; letter-spacing: 2px; color: #23527c\">1. INTRODUCTION</span>\n",
    "<hr style=\"height: 0.5px; border: 0; background-color: 'Black'\">\n",
    "\n",
    "Social media is a vast pool of content, and among all the content available for users to access, news is an element that is accessed most frequently. These news can be posted by politicians, news channels, newspaper websites, or even common civilians. These posts have to be checked for their authenticity, since spreading misinformation has been a real concern in today’s times, and many firms are taking steps to make the common people aware of the consequences of spread misinformation. The measure of authenticity of the news posted online cannot be definitively measured, since the manual classification of news is tedious and time-consuming, and is also subject to bias."
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    "![](http://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1508152385/Fake_real-1_gebpwg.png)"
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      "ERROR: Invalid requirement: '#'\n",
      "[nltk_data] Error loading punkt: <urlopen error [WinError 10061] No\n",
      "[nltk_data]     connection could be made because the target machine\n",
      "[nltk_data]     actively refused it>\n"
     ]
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     "ename": "ValueError",
     "evalue": "numpy.ndarray size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject",
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      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_26920/3442829350.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     26\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mnltk\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcorpus\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mstopwords\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     27\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mseaborn\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 28\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mgensim\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     29\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mgensim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0msimple_preprocess\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     30\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mgensim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparsing\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpreprocessing\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mSTOPWORDS\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anacoda\\lib\\site-packages\\gensim\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mlogging\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mgensim\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mparsing\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcorpora\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmatutils\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minterfaces\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msimilarities\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mutils\u001b[0m  \u001b[1;31m# noqa:F401\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anacoda\\lib\\site-packages\\gensim\\corpora\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;31m# bring corpus classes directly into package namespace, to save some typing\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mindexedcorpus\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mIndexedCorpus\u001b[0m  \u001b[1;31m# noqa:F401 must appear before the other classes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mmmcorpus\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mMmCorpus\u001b[0m  \u001b[1;31m# noqa:F401\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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      "\u001b[1;32mD:\\anacoda\\lib\\site-packages\\gensim\\interfaces.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mlogging\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mgensim\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mutils\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmatutils\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
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      "\u001b[1;32mD:\\anacoda\\lib\\site-packages\\gensim\\_matutils.pyx\u001b[0m in \u001b[0;36minit gensim._matutils\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: numpy.ndarray size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject"
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    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session\n",
    "\n",
    "!pip install gensim # Gensim is an open-source library for unsupervised topic modeling and natural language processing\n",
    "import nltk\n",
    "nltk.download('punkt')\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from wordcloud import WordCloud, STOPWORDS\n",
    "import nltk\n",
    "import re\n",
    "from nltk.corpus import stopwords\n",
    "import seaborn as sns \n",
    "import gensim\n",
    "from gensim.utils import simple_preprocess\n",
    "from gensim.parsing.preprocessing import STOPWORDS\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import plotly.express as px\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n"
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    "<p style=\"font-family: Arials; line-height: 1.3; font-size: 27px; font-weight: bold; letter-spacing: 2px; text-align: center; color: #23527c\">Reading the dataset</p>"
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      "(2096, 12)\n"
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       "      <th>author</th>\n",
       "      <th>published</th>\n",
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       "      <th>0</th>\n",
       "      <td>Barracuda Brigade</td>\n",
       "      <td>2016-10-26T21:41:00.000+03:00</td>\n",
       "      <td>muslims busted they stole millions in govt ben...</td>\n",
       "      <td>print they should pay all the back all the mon...</td>\n",
       "      <td>english</td>\n",
       "      <td>100percentfedup.com</td>\n",
       "      <td>http://bb4sp.com/wp-content/uploads/2016/10/Fu...</td>\n",
       "      <td>bias</td>\n",
       "      <td>Real</td>\n",
       "      <td>muslims busted stole millions govt benefits</td>\n",
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       "      <td>1.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>reasoning with facts</td>\n",
       "      <td>2016-10-29T08:47:11.259+03:00</td>\n",
       "      <td>re why did attorney general loretta lynch plea...</td>\n",
       "      <td>why did attorney general loretta lynch plead t...</td>\n",
       "      <td>english</td>\n",
       "      <td>100percentfedup.com</td>\n",
       "      <td>http://bb4sp.com/wp-content/uploads/2016/10/Fu...</td>\n",
       "      <td>bias</td>\n",
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       "      <td>attorney general loretta lynch plead fifth</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Barracuda Brigade</td>\n",
       "      <td>2016-10-31T01:41:49.479+02:00</td>\n",
       "      <td>breaking weiner cooperating with fbi on hillar...</td>\n",
       "      <td>red state  \\nfox news sunday reported this mor...</td>\n",
       "      <td>english</td>\n",
       "      <td>100percentfedup.com</td>\n",
       "      <td>http://bb4sp.com/wp-content/uploads/2016/10/Fu...</td>\n",
       "      <td>bias</td>\n",
       "      <td>Real</td>\n",
       "      <td>breaking weiner cooperating fbi hillary email ...</td>\n",
       "      <td>red state fox news sunday reported morning ant...</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fed Up</td>\n",
       "      <td>2016-11-01T05:22:00.000+02:00</td>\n",
       "      <td>pin drop speech by father of daughter kidnappe...</td>\n",
       "      <td>email kayla mueller was a prisoner and torture...</td>\n",
       "      <td>english</td>\n",
       "      <td>100percentfedup.com</td>\n",
       "      <td>http://100percentfedup.com/wp-content/uploads/...</td>\n",
       "      <td>bias</td>\n",
       "      <td>Real</td>\n",
       "      <td>pin drop speech father daughter kidnapped kill...</td>\n",
       "      <td>email kayla mueller prisoner tortured isis cha...</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fed Up</td>\n",
       "      <td>2016-11-01T21:56:00.000+02:00</td>\n",
       "      <td>fantastic trumps  point plan to reform healthc...</td>\n",
       "      <td>email healthcare reform to make america great ...</td>\n",
       "      <td>english</td>\n",
       "      <td>100percentfedup.com</td>\n",
       "      <td>http://100percentfedup.com/wp-content/uploads/...</td>\n",
       "      <td>bias</td>\n",
       "      <td>Real</td>\n",
       "      <td>fantastic trumps point plan reform healthcare ...</td>\n",
       "      <td>email healthcare reform make america great sin...</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 author                      published  \\\n",
       "0     Barracuda Brigade  2016-10-26T21:41:00.000+03:00   \n",
       "1  reasoning with facts  2016-10-29T08:47:11.259+03:00   \n",
       "2     Barracuda Brigade  2016-10-31T01:41:49.479+02:00   \n",
       "3                Fed Up  2016-11-01T05:22:00.000+02:00   \n",
       "4                Fed Up  2016-11-01T21:56:00.000+02:00   \n",
       "\n",
       "                                               title  \\\n",
       "0  muslims busted they stole millions in govt ben...   \n",
       "1  re why did attorney general loretta lynch plea...   \n",
       "2  breaking weiner cooperating with fbi on hillar...   \n",
       "3  pin drop speech by father of daughter kidnappe...   \n",
       "4  fantastic trumps  point plan to reform healthc...   \n",
       "\n",
       "                                                text language  \\\n",
       "0  print they should pay all the back all the mon...  english   \n",
       "1  why did attorney general loretta lynch plead t...  english   \n",
       "2  red state  \\nfox news sunday reported this mor...  english   \n",
       "3  email kayla mueller was a prisoner and torture...  english   \n",
       "4  email healthcare reform to make america great ...  english   \n",
       "\n",
       "              site_url                                       main_img_url  \\\n",
       "0  100percentfedup.com  http://bb4sp.com/wp-content/uploads/2016/10/Fu...   \n",
       "1  100percentfedup.com  http://bb4sp.com/wp-content/uploads/2016/10/Fu...   \n",
       "2  100percentfedup.com  http://bb4sp.com/wp-content/uploads/2016/10/Fu...   \n",
       "3  100percentfedup.com  http://100percentfedup.com/wp-content/uploads/...   \n",
       "4  100percentfedup.com  http://100percentfedup.com/wp-content/uploads/...   \n",
       "\n",
       "   type label                            title_without_stopwords  \\\n",
       "0  bias  Real        muslims busted stole millions govt benefits   \n",
       "1  bias  Real         attorney general loretta lynch plead fifth   \n",
       "2  bias  Real  breaking weiner cooperating fbi hillary email ...   \n",
       "3  bias  Real  pin drop speech father daughter kidnapped kill...   \n",
       "4  bias  Real  fantastic trumps point plan reform healthcare ...   \n",
       "\n",
       "                              text_without_stopwords  hasImage  \n",
       "0  print pay back money plus interest entire fami...       1.0  \n",
       "1  attorney general loretta lynch plead fifth bar...       1.0  \n",
       "2  red state fox news sunday reported morning ant...       1.0  \n",
       "3  email kayla mueller prisoner tortured isis cha...       1.0  \n",
       "4  email healthcare reform make america great sin...       1.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"../input/source-based-news-classification/news_articles.csv\")\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bb2a0cff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-01T10:27:36.260996Z",
     "iopub.status.busy": "2021-08-01T10:27:36.260222Z",
     "iopub.status.idle": "2021-08-01T10:27:36.264252Z",
     "shell.execute_reply": "2021-08-01T10:27:36.263737Z",
     "shell.execute_reply.started": "2021-08-01T10:16:08.427553Z"
    },
    "papermill": {
     "duration": 0.042779,
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     "exception": false,
     "start_time": "2021-08-01T10:27:36.221618",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "author                      0\n",
       "published                   0\n",
       "title                       0\n",
       "text                       46\n",
       "language                    1\n",
       "site_url                    1\n",
       "main_img_url                1\n",
       "type                        1\n",
       "label                       1\n",
       "title_without_stopwords     2\n",
       "text_without_stopwords     50\n",
       "hasImage                    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for null values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "33698c83",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-01T10:27:36.331463Z",
     "iopub.status.busy": "2021-08-01T10:27:36.330784Z",
     "iopub.status.idle": "2021-08-01T10:27:36.350425Z",
     "shell.execute_reply": "2021-08-01T10:27:36.349694Z",
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    "papermill": {
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     "exception": false,
     "start_time": "2021-08-01T10:27:36.293022",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#dropping null values\n",
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a782959a",
   "metadata": {
    "papermill": {
     "duration": 0.02867,
     "end_time": "2021-08-01T10:27:36.408491",
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "---\n",
    "\n",
    "<p style=\"font-family: Arials; line-height: 1.3; font-size: 27px; font-weight: bold; letter-spacing: 2px; text-align: center; color: #23527c\">Exploratory data analysis</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8cdc6c9",
   "metadata": {
    "papermill": {
     "duration": 0.028654,
     "end_time": "2021-08-01T10:27:36.465785",
     "exception": false,
     "start_time": "2021-08-01T10:27:36.437131",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Let's visualize the proportion of real and fake news!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f8d1f59a",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2021-08-01T10:27:37.777731Z",
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     "exception": false,
     "start_time": "2021-08-01T10:27:36.494673",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#real vs fake\n",
    "fig = px.pie(df,names='label',title='Proportion of Real vs. Fake News')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44f98051",
   "metadata": {
    "papermill": {
     "duration": 0.030842,
     "end_time": "2021-08-01T10:27:37.839388",
     "exception": false,
     "start_time": "2021-08-01T10:27:37.808546",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Languages of News Articles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2ac60e6e",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2021-08-01T10:27:37.908670Z",
     "iopub.status.idle": "2021-08-01T10:27:38.018613Z",
     "shell.execute_reply": "2021-08-01T10:27:38.018084Z",
     "shell.execute_reply.started": "2021-08-01T10:16:08.550002Z"
    },
    "papermill": {
     "duration": 0.148803,
     "end_time": "2021-08-01T10:27:38.018756",
     "exception": false,
     "start_time": "2021-08-01T10:27:37.869953",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sub_tf_df=df.groupby('language').apply(lambda x:x['language'].count()).reset_index(name='Counts')\n",
    "fig = px.bar(sub_tf_df, x=\"language\", y=\"Counts\",\n",
    "             color='Counts', barmode='group',\n",
    "             height=400)\n",
    "fig.show()"
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  },
  {
   "cell_type": "markdown",
   "id": "d7d4fa26",
   "metadata": {
    "papermill": {
     "duration": 0.030297,
     "end_time": "2021-08-01T10:27:38.080603",
     "exception": false,
     "start_time": "2021-08-01T10:27:38.050306",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "As we can see, English is the most common language in which most news articles are written"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a696ca5d",
   "metadata": {
    "papermill": {
     "duration": 0.030382,
     "end_time": "2021-08-01T10:27:38.141973",
     "exception": false,
     "start_time": "2021-08-01T10:27:38.111591",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Visualizing count of news articles by type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0915927a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-01T10:27:38.210620Z",
     "iopub.status.busy": "2021-08-01T10:27:38.209928Z",
     "iopub.status.idle": "2021-08-01T10:27:38.284355Z",
     "shell.execute_reply": "2021-08-01T10:27:38.284824Z",
     "shell.execute_reply.started": "2021-08-01T10:16:08.686934Z"
    },
    "papermill": {
     "duration": 0.111573,
     "end_time": "2021-08-01T10:27:38.285006",
     "exception": false,
     "start_time": "2021-08-01T10:27:38.173433",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sub_check=df.groupby('type').apply(lambda x:x['type'].count()).reset_index(name='Counts')\n",
    "fig=px.bar(sub_check,x='type',y='Counts',color='Counts',title='Count of News Articles by type')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ad6ddb1",
   "metadata": {
    "papermill": {
     "duration": 0.031865,
     "end_time": "2021-08-01T10:27:38.348831",
     "exception": false,
     "start_time": "2021-08-01T10:27:38.316966",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "here we can se that most news are of type bs (i.e. bullshit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "38d7a6db",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-01T10:27:38.716070Z",
     "iopub.status.busy": "2021-08-01T10:27:38.699680Z",
     "iopub.status.idle": "2021-08-01T10:27:46.239791Z",
     "shell.execute_reply": "2021-08-01T10:27:46.240408Z",
     "shell.execute_reply.started": "2021-08-01T10:16:08.755379Z"
    },
    "papermill": {
     "duration": 7.859663,
     "end_time": "2021-08-01T10:27:46.240588",
     "exception": false,
     "start_time": "2021-08-01T10:27:38.380925",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "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": [
    "from wordcloud import WordCloud \n",
    "wc = WordCloud(background_color=\"black\", max_words=100,\n",
    "               max_font_size=256,\n",
    "               random_state=42, width=1000, height=1000)\n",
    "wc.generate(' '.join(df['text_without_stopwords']))\n",
    "plt.imshow(wc)\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "939c2bd0",
   "metadata": {
    "papermill": {
     "duration": 0.033832,
     "end_time": "2021-08-01T10:27:46.309567",
     "exception": false,
     "start_time": "2021-08-01T10:27:46.275735",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Visualizing top 10 unigrams and bigrams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "20066fac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-01T10:27:46.381931Z",
     "iopub.status.busy": "2021-08-01T10:27:46.380963Z",
     "iopub.status.idle": "2021-08-01T10:27:46.389508Z",
     "shell.execute_reply": "2021-08-01T10:27:46.389973Z",
     "shell.execute_reply.started": "2021-08-01T10:16:14.133866Z"
    },
    "papermill": {
     "duration": 0.046295,
     "end_time": "2021-08-01T10:27:46.390170",
     "exception": false,
     "start_time": "2021-08-01T10:27:46.343875",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_top_n_words(corpus, n=None):\n",
    "    vec = CountVectorizer().fit(corpus)\n",
    "    bag_of_words = vec.transform(corpus)\n",
    "    sum_words = bag_of_words.sum(axis=0) \n",
    "    words_freq = [(word, sum_words[0, idx]) for word, idx in     vec.vocabulary_.items()]\n",
    "    words_freq =sorted(words_freq, key = lambda x: x[1], reverse=True)\n",
    "    return words_freq[:n]\n",
    "\n",
    "def get_top_n_bigram(corpus, n=None):\n",
    "    vec = CountVectorizer(ngram_range=(2, 2)).fit(corpus)\n",
    "    bag_of_words = vec.transform(corpus)\n",
    "    sum_words = bag_of_words.sum(axis=0) \n",
    "    words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]\n",
    "    words_freq =sorted(words_freq, key = lambda x: x[1], reverse=True)\n",
    "    return words_freq[:n]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0cf95aa6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-01T10:27:46.480799Z",
     "iopub.status.busy": "2021-08-01T10:27:46.479684Z",
     "iopub.status.idle": "2021-08-01T10:27:48.190210Z",
     "shell.execute_reply": "2021-08-01T10:27:48.190669Z",
     "shell.execute_reply.started": "2021-08-01T10:16:14.145717Z"
    },
    "papermill": {
     "duration": 1.765272,
     "end_time": "2021-08-01T10:27:48.190840",
     "exception": false,
     "start_time": "2021-08-01T10:27:46.425568",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "common_words = get_top_n_words(df['text_without_stopwords'], 10)\n",
    "df2 = pd.DataFrame(common_words,columns=['word','count'])\n",
    "df2.groupby('word').sum()['count'].sort_values(ascending=False)\n",
    "fig=px.bar(df2,x='word',y='count',color='count',title='Top 10 unigrams')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b8459cbd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-01T10:27:48.263518Z",
     "iopub.status.busy": "2021-08-01T10:27:48.262901Z",
     "iopub.status.idle": "2021-08-01T10:27:52.900007Z",
     "shell.execute_reply": "2021-08-01T10:27:52.899305Z",
     "shell.execute_reply.started": "2021-08-01T10:16:16.150823Z"
    },
    "papermill": {
     "duration": 4.674547,
     "end_time": "2021-08-01T10:27:52.900171",
     "exception": false,
     "start_time": "2021-08-01T10:27:48.225624",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "common_words = get_top_n_bigram(df['text_without_stopwords'], 10)\n",
    "df2 = pd.DataFrame(common_words,columns=['word','count'])\n",
    "df2.groupby('word').sum()['count'].sort_values(ascending=False)\n",
    "fig=px.bar(df2,x='word',y='count',color='count',title='Top 10 bigrams')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2f1b56e",
   "metadata": {
    "papermill": {
     "duration": 0.03483,
     "end_time": "2021-08-01T10:27:52.970259",
     "exception": false,
     "start_time": "2021-08-01T10:27:52.935429",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Visualizing top 5 authors "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "73e3a0f6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-01T10:27:53.093168Z",
     "iopub.status.busy": "2021-08-01T10:27:53.092404Z",
     "iopub.status.idle": "2021-08-01T10:27:53.265417Z",
     "shell.execute_reply": "2021-08-01T10:27:53.264839Z",
     "shell.execute_reply.started": "2021-08-01T10:16:19.55966Z"
    },
    "papermill": {
     "duration": 0.259726,
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     "exception": false,
     "start_time": "2021-08-01T10:27:53.005838",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cwUmTJmndunW6dOmSs+sBAAAAADiZQ0cE8+bNq/nz52vw4MF68sknFRgYqFq1aqlWrVry8fFxdo0AAAAAgEzkUBC8fUGYpKQk7d69Wxs3btSIESN07do1HTp0yKkFAgAAAAAyl0NB8OrVq9q5c6e2b9+ubdu26dSpU6pfv74CAwOdXR8AAAAAIJM5FAQDAwNVsmRJ9ejRQxMnTlTZsmWdXRcAAAAAwEkcCoIDBgzQjh079Mknn2jDhg228wOrVaumXLlyObtGAAAAAEAmcigIvvLKK5KklJQUHThwQD/++KP69u0rq9WqXbt2ObVAAAAAAEDmcigIXrx40XZ+YHR0tI4ePaoqVapwjiAAAAAA5EAOBcGGDRuqWrVqCggI0PDhw+Xv7y8vLy9n1wYAAAAAcAKHguD27dvl6enp7FoAAAAAAFnAzZGZCIEAAAAA8OhwKAgCAAAAAB4dBEEAAAAAcDEEQQAAAABwMQ4FwaioKMXExEiSfv/9d3Xr1k09evSwTQMAAAAA5BwOBcH3339fhQoVkiSFh4erWrVqCgwM1Ntvv+3U4gAAAAAAmc+h20fEx8ercOHCSkxM1H//+1/NmDFDHh4eqlOnjrPrAwAAAABkMoeCoI+Pj44dO6bffvtNTz/9tDw9PXX9+nUZY5xdHwAAAAAgkzkUBPv376/27dvL3d1d7733niRp8+bNqlixolOLAwAAAABkPoeCYPv27dW8eXNJUp48eSRJ1atX1/Tp051XGQAAAADAKRy+fcSNGze0du1affrpp5KkmzdvKiUlxWmFAQAAAACcw6EguG3bNgUHB+vbb7/VRx99JEk6duyYxo4d68zaAAAAAABO4FAQfOedd/T+++9r3rx58vC4NZr0mWee0d69e51aHAAAAAAg8zkUBP/880/VrVtXkmSxWCRJuXLlYmgoAAAAAORADgXBsmXLatOmTammbd68WeXLl3dKUQAAAAAA53HoqqFhYWH617/+pUaNGunGjRsaPXq01q9fbztfEAAAAACQczh0RLB69epauXKlypUrpxdeeEGlSpXS0qVLVa1aNWfXBwAAAADIZA4dEZSkYsWKqU+fPs6sBQAAAACQBdINgkOGDLFdGMae8PBwhzrq37+/Tp48KTc3N+XNm1dvvfWWKlWqpKNHjyosLEwXL16Ut7e3pkyZotKlS0uS3TYAAAAAQMakGwSffPLJTO1oypQpKlCggCTpxx9/1IgRIxQREaExY8aoa9euCgkJUWRkpEaPHq0vvvhCkuy2AQAAAAAyJt0gOGDAgEzt6HYIlKQrV67IYrEoLi5OBw8e1Pz58yVJrVq10vjx4xUfHy9jTLptPj4+mVobAAAAALgSh84RnDNnjurUqZPq4jB79+5VdHT0fZ03OHLkSP3yyy8yxmju3Lk6ffq0ihUrJnd3d0mSu7u7ihYtqtOnT8sYk27b/QRBX9/8Ds8LOKpIkQL3ngkAAAB4SDkUBL/44gt179491bSyZcuqf//+9xUEJ06cKElasWKFwsPDNXDgwPsoNWPi4q7IajUZWpYv+0jPuXMJ2V0CAAAAYJebmyXdA2MO3T4iOTlZHh6pM2OuXLmUlJSUoYLatm2r6OhoFS9eXLGxsUpJSZEkpaSk6OzZs/Lz85Ofn1+6bQAAAACAjHMoCFapUkWLFi1KNW3x4sWqXLmyQ51cvXpVp0+ftj1fv369ChUqJF9fX1WqVElRUVGSpKioKFWqVEk+Pj522wAAAAAAGWcxxtxz3OSRI0fUq1cvFS1aVI8//rhOnDihc+fOaf78+SpXrtw9Ozl//rz69++v69evy83NTYUKFdKwYcNUpUoVxcTEKCwsTJcvX1bBggU1ZcoUlSlTRpLstjnqQYeGdh26MEPL4tG1KLwbQ0MBAADw0LM3NNShICjdOqq3YcMGnT59Wn5+fmrUqJHy5cuXqYU6A0EQmY0gCAAAgJzAXhB06GIxkpQvXz61bNky04oCAAAAAGSPdINg7969NW/ePElS165dZbFY0pxv4UKOmAEAAABATpJuEGzbtq3tcceOHbOiFgAAAABAFkg3CLZu3dr2uEyZMnrmmWfummfv3r3OqQoAAAAA4DQO3T6iV69eaU5/+eWXM7UYAAAAAIDz2b1YjNVqlTEm1b/bjh8/Lnd3d6cXCAAAAADIXHaDYOXKlWWxWGSMuevm8W5uburXr59TiwMAAAAAZD67QXDdunUyxqhHjx766quvbNMtFot8fHzk5eXl9AIBAAAAAJnLbhAsWbKkUlJSVKpUKRUpUkSenp5ZVRcAAAAAwEnuebEYd3d3nTx5UlarNSvqAQAAAAA4mUNXDX311Vc1duxY/fnnn0pJSZHVarX9AwAAAADkLHaHht42atQoSVJkZKRtmjFGFotFhw4dck5lAAAAAACncCgIrlu3ztl1AAAAAACyiENBsGTJkndNu3TpkqKiotStW7dMLwoAAAAA4DwOnSN4W0pKitatW6fXXntN9evX1+LFi51VFwAAAADASRw6InjgwAFFRETou+++040bN5SUlKQZM2YoKCjI2fUBAAAAADKZ3SOCc+fOVevWrdW5c2edPHlSI0eO1C+//CJvb28988wzWVUjAAAAACAT2T0iOG3aNHl7e2vKlClq3ry5LBZLVtUFAAAAAHASu0cEP//8czVu3FijRo3S3//+d02ePFn79+/PqtoAAAAAAE5gNwjWrl1bkyZN0i+//KI333xThw8fVqdOnRQXF6fFixfrwoULWVUnAAAAACCTOHSxmDx58qht27Zq27atzpw5oxUrVmjFihWaM2eO9uzZ4+waAQAAAACZyKEgeKfixYurX79+6tevHyEQAAAAAHKg+7qP4F9x5VAAAAAAyHkeKAgCAAAAAHIegiAAAAAAuJh0g2CnTp1sj2fNmpUlxQAAAAAAnC/dIPjHH38oMTFRkvTZZ59lWUEAAAAAAOdK96qhTZo0UbNmzVSyZEklJiaqW7duac63cOFCpxUHAAAAAMh86QbBSZMmaceOHfrzzz+1b98+dejQISvrAgAAAAA4id37CAYEBCggIEDJyclq165dVtUEAAAAAHAih24o36FDB0VHR2vFihU6e/asihYtqpCQENWpU8fZ9QEAAAAAMplDt4/45ptvNGjQIBUpUkTPPfecihYtqjfffFNLlixxdn0AAAAAgEzm0BHBuXPnav78+apYsaJtWvPmzfX666+nus1Eei5cuKChQ4fq+PHj8vT01JNPPqlx48bJx8dHu3fv1ujRo5WYmKiSJUtq6tSp8vX1lSS7bQAAAACAjHHoiODFixdVtmzZVNPKlCmjS5cuOdSJxWLRyy+/rLVr1+rbb7/V448/rmnTpslqtWrIkCEaPXq01q5dq4CAAE2bNk2S7LYBAAAAADLOoSBYo0YNTZ48WdevX5ckXbt2TeHh4fL393eoE29vb9WuXdv2vHr16jp16pT279+v3LlzKyAgQJLUuXNnrVmzRpLstgEAAAAAMs6hoaFvv/223njjDQUEBKhQoUK6dOmS/P399e677953h1arVV9//bWCgoJ0+vRplShRwtbm4+Mjq9Wqixcv2m3z9vZ2uD9f3/z3XSNwL0WKFMjuEgAAAIAMcygIFi1aVAsXLtSZM2dsVw0tXrx4hjocP3688ubNq+7du+uHH37I0DruR1zcFVmtJkPL8mUf6Tl3LiG7SwAAAADscnOzpHtgzKEgeFvx4sUzHAAlacqUKTp27Jhmz54tNzc3+fn56dSpU7b2+Ph4ubm5ydvb224bAAAAACDjHDpHMDNMnz5d+/fv14cffihPT09JUtWqVXXjxg3t2LFDkrR48WIFBwffsw0AAAAAkHH3dUQwo44cOaJPPvlEpUuXVufOnSVJpUqV0ocffqjw8HCNGTMm1S0iJMnNzS3dNgAAAABAxlmMMXZPoLNarYqOjlbNmjVtR/Jykgc9R7Dr0IWZXBFyukXh3ThHEAAAAA89e+cI3nNoqJubm/r3758jQyAAAAAA4G4OnSNYq1Yt7d6928mlAAAAAACygkPnCJYoUUJ9+vRRkyZNVLx4cVksFlvbwIEDnVYcAAAAACDzORQEExMT1bRpU0lSbGysUwsCAAAAADiXQ0Fw0qRJzq4DAAAAAJBFHL59RExMjNasWaO4uDiNHj1av//+u5KSklSxYkVn1gcAAAAAyGQOXSxm9erV6tatm2JjY7VixQpJ0tWrVzV58mRn1gYAAAAAcAKHjgjOmDFDCxYsUMWKFbV69WpJUsWKFfXrr786tTgAAAAAQOZz6IhgfHy8KlSoIEm2K4ZaLJZUVw8FAAAAAOQMDgXBKlWqKDIyMtW0VatWqVq1ak4pCgAAAADgPA4NDR05cqR69+6tpUuX6tq1a+rdu7eOHj2qzz77zNn1AQAAAAAymUNBsGzZslq9erV++uknNWrUSH5+fmrUqJHy5cvn7PoAAAAAAJnM4dtH5MmTRzVr1lSpUqVUrFgxQiAAAAAA5FAOBcFTp04pNDRUe/bsUcGCBXX58mU988wzmjp1qkqWLOnsGgEAAAAAmcihi8UMGzZMVapU0fbt27VlyxZt27ZNVatWVVhYmLPrAwAAAABkMoeOCB44cECfffaZcuXKJUnKly+fQkNDVbt2bacWBwAAAADIfA4dEaxevbr27t2batr+/fvl7+/vlKIAAAAAAM6T7hHBDz74wPb48ccfV9++fdWoUSMVL15cZ86c0caNG9WqVassKRIAAAAAkHnSDYJnzpxJ9fz555+XJMXHx8vT01PPPfecEhMTnVsdAAAAACDTpRsEJ02alJV1AAAAAACyiMP3Ebx+/bqOHTuma9eupZpeo0aNTC8KAAAAAOA8DgXBFStWaNy4ccqVK5e8vLxs0y0WizZs2OCs2gAAAAAATuBQEJw6dapmzpypZ5991tn1AAAAAACczKHbR+TKlUuBgYHOrgUAAAAAkAUcCoIDBw7U5MmTFR8f7+x6AAAAAABO5tDQ0NKlS2vGjBlatGiRbZoxRhaLRYcOHXJacQAAAACAzOdQEBw6dKhCQkLUokWLVBeLAQAAAADkPA4FwYsXL2rgwIGyWCzOrgcAAAAA4GQOnSPYvn17RUZGOrsWAAAAAEAWcOiI4N69e7Vw4UJ9/PHHKly4cKq2hQsXOqUwAAAAAIBzOBQEO3XqpE6dOjm7FgAAAABAFnAoCLZr187ZdQAAAAAAsohDQXDp0qXptnXo0OGey0+ZMkVr167Vn3/+qW+//Vbly5eXJB09elRhYWG6ePGivL29NWXKFJUuXfqebQAAAACAjHMoCP71QjHnz5/XiRMn5O/v71AQbNKkiXr27Klu3bqlmj5mzBh17dpVISEhioyM1OjRo/XFF1/csw0AAAAAkHEOBcEvv/zyrmlLly5VTEyMQ50EBATcNS0uLk4HDx7U/PnzJUmtWrXS+PHjFR8fL2NMum0+Pj4O9QkAAAAASJtDQTAt7du3V506dTRs2LAMLX/69GkVK1ZM7u7ukiR3d3cVLVpUp0+fljEm3bb7DYK+vvkzVB9gT5EiBbK7BAAAACDDHAqCVqs11fPr169r5cqVKlDg4f8yHBd3RVarydCyfNlHes6dS8juEgAAAAC73Nws6R4YcygIVq5cWRaLJdW0YsWKafz48Rkuys/PT7GxsUpJSZG7u7tSUlJ09uxZ+fn5yRiTbhsAAAAA4ME4FATXrVuX6nmePHke+Fw9X19fVapUSVFRUQoJCVFUVJQqVapkW6+9NgAAAABAxlmMMRkbN3kfJkyYoO+//17nz5/XY489Jm9vb61atUoxMTEKCwvT5cuXVbBgQU2ZMkVlypSRJLtt9+NBh4Z2HbowQ8vi0bUovBtDQwEAAPDQszc01G4Q7NGjx11DQlMtbLHo888/f/AKnYggiMxGEAQAAEBOkOFzBNu0aZPm9NjYWH355Ze6cePGg1cHAAAAAMhSdoNgx44dUz2/cOGC5syZoyVLlqhFixZ69dVXnVocAAAAACDzOXSxmCtXrmju3LlauHChGjVqpIiICD3xxBPOrg2AHY8V8pSHZ+7sLgMPmZtJibpwKSm7ywAAAA85u0Hwxo0b+vzzz/XZZ5+pdu3aWrRokf72t79lVW0A7PDwzK3/hr+c3WXgIVNz6FxJBEEAAGCf3SAYFBQkq9Wql19+WVWrVtX58+d1/vz5VPPUrVvXqQUCAAAAADKX3SDo5eUlSfr666/TbLdYLHfdYxAA4NoKFsqt3J6e2V0GHjKJSUm6fCkxu8sAAPwfu0Fw/fr1WVUHAOARkdvTU/+YPzC7y8BDZkGvDyRlfxD0LuCpXF6cX43Ukm8k6mICw+rhWhy6WAwAAMCjIJdXbn3Xs1d2l4GHTIsv5ksEQbgYt+wuAAAAAACQtQiCAAAAAOBiCIIAAAAA4GIIggAAAADgYrhYDAAAAJDNChXMI8/cfDVHakmJN3Xp8nWnrJutDQAAAMhmnrk99M7IpdldBh4yIyZ2cNq6GRoKAAAAAC6GIAgAAAAALoYgCAAAAAAuhiAIAAAAAC6GIAgAAAAALoYgCAAAAAAuhiAIAAAAAC6GIAgAAAAALoYgCAAAAAAuhiAIAAAAAC6GIAgAAAAALoYgCAAAAAAuhiAIAAAAAC6GIAgAAAAALoYgCAAAAAAuhiAIAAAAAC6GIAgAAAAALoYgCAAAAAAu5qEPgkePHtWLL76oZs2a6cUXX9Qff/yR3SUBAAAAQI720AfBMWPGqGvXrlq7dq26du2q0aNHZ3dJAAAAAJCjeWR3AfbExcXp4MGDmj9/viSpVatWGj9+vOLj4+Xj4+PQOtzcLA9UQ+HH8j3Q8ng0Peh2lVk8C/pmdwl4CD0M22fh/I59RsO1PAzbpiTlKcxnJ+72MGyfhbzzZncJeAg9yLZpb1mLMcZkeM1Otn//fg0bNkyrVq2yTWvRooWmTp2qKlWqZGNlAAAAAJBzPfRDQwEAAAAAmeuhDoJ+fn6KjY1VSkqKJCklJUVnz56Vn59fNlcGAAAAADnXQx0EfX19ValSJUVFRUmSoqKiVKlSJYfPDwQAAAAA3O2hPkdQkmJiYhQWFqbLly+rYMGCmjJlisqUKZPdZQEAAABAjvXQB0EAAAAAQOZ6qIeGAgAAAAAyH0EQAAAAAFwMQRAAAAAAXAxBEAAAAABcDEEwhwsKClKrVq1ktVpTTfvtt98ytL6pU6eqatWqiouLc2j+y5cv69NPP001rUePHvrpp58y1D9ypkuXLqlatWqaMGGCQ/P/+OOP2rt3r+35vn379Oabb95zuQ8++EDfffed3XmWL1+uo0ePptveo0cPNWnSRCEhIQoODtbnn3/uUM1/lda2j5wpKChIwcHBCgkJsf07efLkXfNVqFBBV69etS2T0c9ZZI87/373Ehsbq8mTJ9u2h969e+uHH35INc/y5cv1+uuvp7m8vTZXcujQIb3yyiu25xUqVFDr1q3VunVrNWvWTIMHD9b//ve/TOlr5syZqlu3ru2zfcSIEUpKSsrQuhYsWJDqe9DXX3+tBQsWSEr9t42Ojlb79u0fqO6NGzfqrbfeeqB1ONP97t+RsxAEHwHXrl1TZGTkA68nJSVFkZGRqlGjhsPru3z5subOnfvAff/VzZs3M32dcJ6oqCg988wzWrVqlUM73r8GwaefflrvvvvuPZcbOHCgWrRoYXeeiIgI/fHHH3bnGTVqlCIjIzV//nzNmDFDv/766z37/itnbfvIHjNmzFBkZKTtX6lSpbK7JIe42mdlSkqK0/vYu3ev+vXrp6efflpLlixRZGSkJkyYoA0bNuidd95xev+ZKbu3j3fffVd9+/ZNNW3x4sX69ttvtXr1atWqVUtdunTRiRMn7mu96b2utm3bKjIyUitXrlRMTIwWL16cobq/+OKLVEGwS5cu+sc//pGhdd1Lw4YNdeDAAR0/ftwp639Q97t/R87ikd0F4MENGDBAs2bNUsuWLeXp6Zmq7dixYxo9erTi4+Pl4eGhN954Q3//+9/TXM/GjRv1xBNP6PXXX9fYsWP1z3/+U5J08uRJvfDCC4qOjr7r+bhx45SQkKCQkBDlyZPH9qG7bds2zZkzR2fPnlXz5s0VGhp6z3oqVKigAQMGaMOGDWrQoIEGDRrkjLcLTrBs2TINGTJEn3zyidatW6fmzZtLuvWr+oQJE2zBrFWrVqpcubLWr1+vzZs365tvvlGvXr3k5+enKVOmaPny5Ro5cqTKly+vl156SZL022+/6ZVXXtGPP/6o4cOHq2rVqurevbt+/PFHffDBB3Jzc1NKSoreeustnTx5Uvv379eECRP0/vvva9iwYapXr166dfv5+empp57S0aNHVbhwYY0ZM8a2M+7du7fatm0rq9WqcePGaevWrfL09FTevHm1ePHidLd9PDq+//57TZ8+Xblz59bzzz+f7nxnz57VhAkTdOrUKSUmJqply5bq16+fpFtHDkNCQrR582adO3dO//znP9W9e/d7tv3+++965513dOHCBSUnJ+ull17SCy+8ICnnflZWqFBBb7zxhn744QddvHhRQ4cOVbNmzSRJP//8s6ZPn66UlBT5+Pho3LhxevLJJxUdHa0JEyaoatWqOnjwoAYNGqQnn3zS7nvz6quvat26dbpx44YGDx5s6+O21atXKyIiQnPmzJEkJSUlKSgoSEuWLNFjjz2msWPH6pNPPlHRokVty/j5+WnixIl666239J///Ef169eXJCUkJKhfv346fvy4ChcurKlTp6pYsWJ3vfY5c+Zo5cqVkm798DVq1Cjly5dPCQkJGjFihI4cOaJixYqpWLFi8vX11bBhw+y2JSUl6b333tP27duVlJSkChUqaOzYscqXL5/CwsLk7u6uo0eP6urVq6l+2E1ISNA777yj/fv3y2KxKCAgQKNHj9bVq1c1YcIE7du3T5IUEhKiPn36SLo1iqJKlSrau3ev/vzzT/Xs2VPFihXTV199pbNnz2rIkCG2z/w7nTp1SkePHpW/v3+a24Obm5u6dOmibdu2adGiRRo2bJiuXLmiSZMm6fDhw0pMTFTt2rU1fPhwubu7q0ePHqpYsaL27NmjQoUK2R2R4enpqZo1a+ro0aNKSUnRtGnTtGnTJklSgwYNFBoaKnd3d/373//WggUL5OnpKavVqvfff1/ff/+9zp49q9dff125c+fWu+++q9WrV+vatWsaNmxYun1K0ooVKzRv3jxJ0hNPPKFx48bJ19dXy5cvV1RUlAoWLKgjR46oQIECmjlzpooUKSJJCg4O1rJly/TGG2/YXX92SGv/PnPmTB09elQJCQk6ceKEnnjiCX3wwQfKkyfPPbelqlWravfu3am+H+7du1cjRoxQVFSUrd82bdpo7NixSk5O1sSJE1WtWjXt2bNHHh4eCg8P16xZs3TkyBH5+flp5syZyps3r92+g4KCNHv2bJUvXz7V83LlyqW5j3cZBjla48aNzeHDh81rr71mFixYkGqaMcZ06NDBLFmyxBhjzJEjR0xgYKCJi4tLc139+/c333zzjTHGmOeff97s3r3bGGPMiRMnTGBgoG2+O5//tc0YY7p3724GDhxoUlJSzOXLl01gYKA5evToPespX768+eSTTx74PUHWOnTokGncuLGxWq0mMjLS9O7d29bWvXt38+mnn9qe3/5bDxs2zHz55Ze26Vu3bjXt2rUzxhizfft207ZtW1vbpEmTzMyZM+9arnXr1mbnzp3GGGNu3rxpEhISbH2uX78+3XrvbD9y5Ijx9/c3MTExZuDAgea9994zxhgTGxtrnn32WXP48GFz4MABExwcbFJSUowxxly8eNEYk/a2j5ypcePGplmzZqZNmzamTZs2pl27dubcuXMmMDDQxMTEGGOMmTNnjilfvry5cuWKbZnbn7P/+Mc/zLZt24wxxiQmJpouXbqY//znP7b5Jk+ebIy5tc1Ur1491TrSaktOTjbt2rUz//vf/4wxxiQkJJjnn3/e9jynflaWL1/e9v93x44dpn79+sYYY86fP29q165tjhw5YowxZsmSJaZDhw7GmFufDRUrVrT9X3fkvbn9eRETE2MCAwPN+fPnbW23399GjRqZ48ePG2OMiYiIMP379zfGGLN06VLzxRdf2Ppu37696dq1q5k2bZqZO3euOX/+vBkwYIAxxphly5aZp59+2raNzJw507z22mu2ttuPN2zYYFq2bGkSEhKM1Wo1Q4YMMeHh4caYW59vI0aMMMYYc+HChVTbhL22Dz/80Hz44Ye29zY8PNxMnz7dGHPrc7Jdu3bm6tWrd/0NwsLCzLhx42yfZ7c/k8PDw83QoUON1Wo1CQkJpkWLFmbDhg3GmNT79DNnzphq1arZ+tqzZ49p0KBBmn/viIgIM2jQoFTT7vw/dNv8+fPNyy+/bIwxZsSIESYiIsIYY0xKSop54403zL///W9bHf/6179McnJymv3NmDHD9v5cvnzZtGnTxixZssQsXLjQvPTSSyYxMdEkJiaanj17moULFxpjjKlRo4aJjY01xtz6v3vt2jVjTOr/339d951/2zv3XYcPHzbPPvusbX3vvfeeGThwoG2ZgIAAc+rUKWOMMSNHjrS9h8YYs23bNtOxY8c0X1d2Sm//PmPGDPPcc8+ZS5cuGavVanr16mX7Ozm6Lf31+2HHjh1NdHS0MebW94CQkBBjzK33uHLlyubgwYPGGGPGjh1rGjRoYE6fPm2MMebll1+2fa+01/df/6a3n6e3j3cVDA19RAwaNEiffvppqvMfrly5okOHDtl+KS1XrpwqVaqk3bt337V8XFyctm3bpuDgYEm3hlcsW7Ysw/UEBwfLzc1NBQoUUNmyZXX8+HGH6mnXrl2G+0T2WLp0qUJCQmSxWPT8889r7969io2N1dWrV7Vr165Uw2l8fHzuub6AgABdvXpVhw8f1s2bNxUVFZXmdlGnTh1NmjRJc+fOVUxMjPLnz+9wzRMmTFBISIhGjBihcePGqUyZMtqyZYs6d+4sSSpatKgaNmyo6OhoPf7447p586ZGjhypFStWONwHcpY7h4YuX75ce/bsUeXKlVWmTBlJ0osvvpjmcteuXdO2bdts21THjh119uxZxcTE2Oa5PZy5VKlSKliwoM6cOWO37Y8//lBMTIwGDx6skJAQdevWTcnJyfr9999ty+XUz8rbr7d69eo6e/asEhMTtWfPHlWsWFHlypWTJL3wwgs6dOiQrly5Ikl68sknbUeVHHlvOnbsKEkqU6aMKleufNc+z8PDQy+++KLtV/9FixapW7dukqQDBw6oVq1aslqtGjVqlN59910tWLBAv/32m65duyZfX19dvHjRtq6aNWvatpGOHTtq69atd73mLVu2qEWLFsqfP78sFos6deqkLVu2SEp9jpm3t7eaNm1qW85e2/r167Vy5UrbOYzr169PNbQwODhYefPmvauWn376Sb1795ab262vf7c/k7ds2aKOHTvKYrEof/78atmypa3G2+tzc3NTsWLFUtVSpUoVxcbGKjEx8a6+YmNj5evre9f0vzLGpHpd8+bNU0hIiNq1a6cDBw6kOue7devW8vBIfzDbihUrFBISop49e6pRo0Zq3769tmzZonbt2snT01Oenp62adKt/UhYWJi+/PJLxcbGKk+ePPesNz3R0dFq2LCh7Uhy586dU72HNWrUkJ+fnyTpmWeeSfX3Kly4cKrPhYdFevt3Sapfv74KFiwoi8WiatWq2V6Po9vSnd8PpVtHCxctWiRJWrhwoe3/pCQ99dRTqlSpkiSpcuXKqlSpkooXLy7p1jZ47Ngxh/pOi6vv4xka+ogoU6aMGjZsqPnz52do+cjISN28eVNt2rSRdGv8/fXr1zVixAh5eHik+qBO6wP/r3Lnzm177O7u7vB5HWntuPDwSkpKUlRUlDw9PW3Dj5KTk7V8+XL17Nkzw+tt27atIiIiFBgYqLJly6pkyZJ3zTNixAgdPnxYW7du1cCBA9WrVy916tTJofWPGjVKjRs3dmjeAgUKaNWqVYqOjtbmzZs1bdo0RURE3NfrwaPLarXKYrFo6dKlypUrV5rz2Ps8TKvNYrHoscces3uudk79rLz9et3d3SU5dg7bna/VGHPP98YRnTp1Urt27RQUFKTLly+rbt26tjY3NzdduHBB3t7eKl26tKRb53HFxcUpMTHxgcJCZjHGaMyYManqvlNmbx9/3U7T+jveOc/tZRw5p2zfvn3629/+JunW6/roo4/0+OOPpznvvV5X27Zt7zl8806zZs3Svn37tHXrVvXs2VNjx45Vw4YNHV7+ftj7HEhMTJSXl5dT+s0oe/t36e7X48h3w7SWu/0+BAcHa/r06Tp48KCio6NTnY9752lPd25/99O3u7t7qgsr3l4mvX387WG7jzqOCD5CXnvtNS1atMh2VDB//vyqVKmS7UtrTEyMfv31V1WvXv2uZZcvX64PP/xQ69ev1/r16/Xzzz+rWrVqWrNmjQoXLqzk5GTbLy53juHOnz+/bty44dDO/H7qQc6wbt06PfXUU/r5559t285nn32miIgI5cuXT/7+/rYrrUlSfHy8pFvbQkJCQrrrbdu2raKiovTNN9+ke0W233//XRUqVNBLL72kNm3a2M4JuH3ezf2qW7eulixZIkk6d+6cNm7cqDp16ig+Pl7Xr1+3nVdSoEABnThx4r62feQ81atX18GDB23nt37zzTdpzpc/f37VrFnTdr6ZJJ0+fVrnzp3LcN9PPfWUvLy8Uv06HRMTYztC9qipXr26fv31V9tR1IiICFWuXDnNo/yOvDe3R7P88ccfOnjwYJr7GB8fH9WrV0+DBw9W165dZbFYJEmVKlXS9u3b9dhjj+nSpUs6ceKEbt68qZ9//lkpKSmaOXOm7QdTSdq5c6dtG1m2bJnq1KlzV19169bV6tWrdeXKFRljtHTpUtu5y4GBgbYv2ZcvX9a6detsy9lrCwoK0oIFC3Tjxg1Jt0YA3XkUOj2NGzfWvHnzbD/u3v5Mrlu3rpYtWyZjjK5cuaLvvvvO7vnVjqhQoYLdKzhbrVYtWbJEmzZtUpcuXWyva86cObZwEB8ff98XkvmrunXrasWKFUpOTlZycrJWrFihevXq6ebNmzpx4oSqVaumvn376tlnn9WhQ4ckZWw/Urt2bW3cuNH2f3/JkiUOv4e///67KlaseH8vzMns7d/tyei2lCtXLr3wwgt65ZVX1Lp16wz94GKv7yeeeML2PWHLli06f/68JKW7j3cVHBF8hBQvXlwhISH67LPPbNOmTZum0aNHa8GCBbYTbP86PG/Pnj26ePHiXTuw1q1ba9myZWrbtq1GjhypXr16ycfHR40aNbLN4+3tbbsUdKFChe55gq0j9SDnWLZsmVq3bp1qmr+/v6xWq7Zt26Zp06bp7bffVqtWreTm5qZWrVqpb9++atOmjYYPH641a9bYLhZzpxIlSqhcuXLatm2bpk+fnmbf7777ro4dOyZ3d3cVLFhQEydOlHRrCN/kyZM1b968e14s5k6jRo3S6NGjba8nNDRUf/vb33TgwAG99dZbunnzplJSUvT3v/9d1atXl5ub231t+3i43b4wxG0TJkzQ+PHj1a9fP3l5ed11sZg7j4BMmzZNkyZNsm07+fLl08SJEzP8i7KHh4dmz56td955R/PmzZPVapWvr6/ef//9jL24h5yPj4/Cw8MVGhqqmzdvysfHR1OnTk1zXkfem5SUFLVt21bXr1+3XawjLR06dNCaNWtSDbNt3ry5evTooWbNmmncuHEaNGiQvLy8FBAQoOjoaHXo0EGtWrWyzV+jRg1NmTJFx44ds10s5q8aNmyow4cP24aeV61a1XZLhVdffVXDhw9XcHCwihQpoqpVq9oCsL22vn37atasWerQoYMsFossFosGDBigsmXL3tV/nz599Prrr+vpp5/W8OHD9c4776hVq1Zyd3dXYGCgRo0apf79+2v8+PG2bbhNmzbpXljOUTVr1tTJkyeVkJCgAgUK2Kbffh+SkpJUuXJlff3117YjgCNGjNDUqVNtwxFz5cqlESNGpHuE0BEvvviijh8/bvs7169fX506dVJKSorCwsKUkJAgi8UiPz8/222MevbsqREjRsjLy8uhK1pLUvny5RUaGmq70N7jjz+ucePGObTspk2b7F6QKjvca/9etWrVNJd7kG2pY8eOmjVrlu2Hgftlr++BAwcqLCxMX331lerUqaMSJUpIuvXDXVr7eFdhMXeO+QMAAHbdvtrdL7/88tAN53J1FSpU0M6dO5UvX757zvvRRx/p3LlzGjNmTKrpO3bs0DvvvKNBgwapXr168vDwUHx8vNauXatmzZpl6o+XycnJslqtyp07t65cuaIuXbpo+PDhqlevnt22nOKTTz5R7ty5nXbrhUfBhQsX9NJLL2np0qV3Xfnd1URGRmrVqlWpRljAuTgiCACAg7744gvbpe4JgTlXy5Yt5e7ubrvU/50CAgI0a9YszZs3T++//76Sk5Pl4+Ojdu3a6bHHHsvUOi5fvqw+ffooJSVFiYmJatWqlS3o2WvLKXr16vVAF55zBSdOnNDYsWNdPgT27t1bx48f18cff5zdpbgUjggCAAAAgIvhYjEAAAAA4GIIggAAAADgYgiCAAAAAOBiCIIAAAAA4GIIggAAAADgYgiCAAAAAOBiCIIAAAAA4GIIggAAAADgYgiCAAAAAOBiCIIAAAAA4GIIggAAAADgYgiCAAAAAOBiCIIAAAAA4GIIggAAAADgYgiCAAAAAOBiCIIAAAAA4GIIggAAAADgYgiCAAAAAOBiCIIAADjJ8uXL1aVLl+wuAwCAuxAEAQC4gzFGTZo0UYsWLe5ruZMnT6pChQq6efOmkyoDACDzEAQBALjD9u3bFR8frxMnTmjv3r3ZXY4NARMAkJkIggAA3CEiIkJBQUFq2LChVqxYkaotKChImzdvtj2fOXOmQkNDJUndu3eXJNWqVUv+/v7atWuXbb4pU6aoVq1aCgoK0saNG23TY2Nj1a9fPwUGBuq5557TkiVLUq379ddfV2hoqGrUqKGIiAjt3btX7du3V40aNVSvXj1NmjTJGW8BAMAFEAQBAPg/169f19q1a9WmTRu1bt1aq1atUlJSkkPLfvXVV5JuHVHctWuX/P39JUl79+7VU089pa1bt+rll1/WyJEjZYyRJA0ePFjFixfXpk2bNGPGDE2fPl1btmyxrXPdunUKDg7Wjh071Lp1a02cOFE9e/bUzp079cMPP6h58+aZ/A4AAFwFQRAAgP/z/fffy9PTU88++6waNWqkmzdvpjqClxElSpRQp06d5O7urnbt2uncuXM6f/68Tp8+rZ07dyo0NFS5c+dWpUqV1LFjR0VGRtqWrV69upo2bSo3Nzd5eXnJw8NDx48fV3x8vPLly6fq1as/4CsGALgqgiAAAP9nxYoVat68uTw8PJQ7d249//zzioiIeKB1Fi5c2PY4T548kqRr167p7NmzKlSokPLnz29rL1GihGJjY23PixcvnmpdEydO1B9//KHmzZvrhRde0E8//fRAtQEAXJdHdhcAAMDD4MyZM9q6dav27t2r77//XtKtoaJJSUmKj4+Xj4+P8uTJo+vXr9uWOXfunO2xxWK5r/6KFi2qS5cu6cqVK7YwePr0aRUrVizddZYuXVrTp0+X1WrV999/r9dff13R0dHKmzfvfb9eAIBr44ggAACSIiMjVbp0aa1Zs0YrVqzQihUrtHbtWhUrVkyrVq2SJFWsWFHfffedkpOTtW/fPq1du9a2vI+Pj9zc3HTixAmH+vPz85O/v7+mT5+uxMRE/frrr1q6dKnatGljt8b4+Hi5ubmpYMGCkiQ3N3blAID7xxFBAAB062qh3bp1U5EiRVJN79y5syIiItSjRw8NGjRIgwcPVmBgoGrVqqXWrVvr4sWLkm4N++zXr5+6dOmimzdvau7cuffsc/r06RozZowaNGigggUL6rXXXlO9evXSnX/Tpk2aPHmybty4oRIlSui9996Tl5fXA71uAIBrspjbly4DAAAAALgExpMAAAAAgIshCAIAAACAiyEIAgAAAICLIQgCAAAAgIshCAIAAACAiyEIAgAAAICLIQgCAAAAgIshCAIAAACAiyEIAgAAAICL+X8/x1qlAuosiAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = df['author'].value_counts().sort_values(ascending=False).head(5)\n",
    "d = pd.DataFrame(d)\n",
    "d = d.reset_index() # dataframe with top 5 authors\n",
    "\n",
    "# Plotting\n",
    "sns.set()\n",
    "plt.figure(figsize=(15,4))\n",
    "sns.barplot(x='index', y='author', data=d)\n",
    "plt.xlabel(\"\\n Authors\")\n",
    "plt.ylabel(\"Number of Articles written\")\n",
    "plt.title(\"Top 5 authors\\n\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "018cb8e0",
   "metadata": {
    "papermill": {
     "duration": 0.035844,
     "end_time": "2021-08-01T10:27:53.337559",
     "exception": false,
     "start_time": "2021-08-01T10:27:53.301715",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Visualizing top 10 fake news site"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1979e519",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-01T10:27:53.445988Z",
     "iopub.status.busy": "2021-08-01T10:27:53.425611Z",
     "iopub.status.idle": "2021-08-01T10:27:53.735392Z",
     "shell.execute_reply": "2021-08-01T10:27:53.734750Z",
     "shell.execute_reply.started": "2021-08-01T10:16:19.764617Z"
    },
    "papermill": {
     "duration": 0.361492,
     "end_time": "2021-08-01T10:27:53.735557",
     "exception": false,
     "start_time": "2021-08-01T10:27:53.374065",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = df[df['label'] == 'Fake']['site_url'].value_counts().sort_values(ascending=False).head(10)\n",
    "d = pd.DataFrame(d)\n",
    "d = d.reset_index() # dataframe with top 10 fake news site\n",
    "\n",
    "# Plotting\n",
    "sns.set()\n",
    "plt.figure(figsize=(25,7))\n",
    "sns.barplot(x='index', y='site_url', data=d)\n",
    "plt.xlabel(\"\\n site_url\")\n",
    "plt.ylabel(\"Number of Articles written\")\n",
    "plt.title(\"Top 10 Fake news sites\\n\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54a11ecf",
   "metadata": {
    "papermill": {
     "duration": 0.044496,
     "end_time": "2021-08-01T10:27:53.828566",
     "exception": false,
     "start_time": "2021-08-01T10:27:53.784070",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Let's have a look at the top 10 trustworthy news sites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9532bb63",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-01T10:27:53.918804Z",
     "iopub.status.busy": "2021-08-01T10:27:53.918078Z",
     "iopub.status.idle": "2021-08-01T10:27:54.211705Z",
     "shell.execute_reply": "2021-08-01T10:27:54.211109Z",
     "shell.execute_reply.started": "2021-08-01T10:16:19.987224Z"
    },
    "papermill": {
     "duration": 0.341179,
     "end_time": "2021-08-01T10:27:54.211846",
     "exception": false,
     "start_time": "2021-08-01T10:27:53.870667",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = df[df['label'] == 'Real']['site_url'].value_counts().sort_values(ascending=False).head(10)\n",
    "d = pd.DataFrame(d)\n",
    "d = d.reset_index() # dataframe with top 10 Trustworthy news site\n",
    "\n",
    "# Plotting\n",
    "sns.set()\n",
    "plt.figure(figsize=(25,7))\n",
    "sns.barplot(x='index', y='site_url', data=d)\n",
    "plt.xlabel(\"\\n site_url\")\n",
    "plt.ylabel(\"Number of Articles written\")\n",
    "plt.title(\"Top 10 Trustworthy news sites\\n\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "977896bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-01T10:27:54.300794Z",
     "iopub.status.busy": "2021-08-01T10:27:54.298632Z",
     "iopub.status.idle": "2021-08-01T10:27:54.315873Z",
     "shell.execute_reply": "2021-08-01T10:27:54.315276Z",
     "shell.execute_reply.started": "2021-08-01T10:16:20.228538Z"
    },
    "papermill": {
     "duration": 0.064754,
     "end_time": "2021-08-01T10:27:54.315999",
     "exception": false,
     "start_time": "2021-08-01T10:27:54.251245",
     "status": "completed"
    },
    "tags": []
   },
   "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>url_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1847</th>\n",
       "      <td>thefreethoughtproject.com event daily never ch...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1119</th>\n",
       "      <td>frontpagemag.com stalinist monsters bestow ble...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1483</th>\n",
       "      <td>politicususa.com sports file photo shows membe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>abeldanger.net second video bottom one proves ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1100</th>\n",
       "      <td>frontpagemag.com james comey change outcome el...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               url_text\n",
       "1847  thefreethoughtproject.com event daily never ch...\n",
       "1119  frontpagemag.com stalinist monsters bestow ble...\n",
       "1483  politicususa.com sports file photo shows membe...\n",
       "96    abeldanger.net second video bottom one proves ...\n",
       "1100  frontpagemag.com james comey change outcome el..."
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's reshuffle the dataset\n",
    "df = df.sample(frac = 1)\n",
    "\n",
    "#taking the features\n",
    "features = df[['site_url', 'text_without_stopwords']]\n",
    "features.head(5)\n",
    "\n",
    "features['url_text'] = features[\"site_url\"].astype(str) + \" \" + features[\"text_without_stopwords\"]\n",
    "features.drop(['site_url', 'text_without_stopwords'], axis = 1, inplace = True)\n",
    "\n",
    "features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1690fa4c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-01T10:27:54.401086Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X = features\n",
    "y = df['type']\n",
    "y = y.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ece613a7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-01T10:27:54.485662Z",
     "iopub.status.busy": "2021-08-01T10:27:54.485016Z",
     "iopub.status.idle": "2021-08-01T10:27:55.340110Z",
     "shell.execute_reply": "2021-08-01T10:27:55.339538Z",
     "shell.execute_reply.started": "2021-08-01T10:16:20.263485Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Splitting the dataset and using TF-IDF\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)\n",
    "\n",
    "tfidf_vectorizer = TfidfVectorizer(use_idf = True, stop_words = 'english')\n",
    "\n",
    "X_train_tfidf = tfidf_vectorizer.fit_transform(X_train['url_text'])\n",
    "X_test_tfidf = tfidf_vectorizer.transform(X_test['url_text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "579820cc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-01T10:27:55.424850Z",
     "iopub.status.busy": "2021-08-01T10:27:55.424170Z",
     "iopub.status.idle": "2021-08-01T10:27:55.846113Z",
     "shell.execute_reply": "2021-08-01T10:27:55.845567Z",
     "shell.execute_reply.started": "2021-08-01T10:16:21.327439Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "tfidf_train = pd.DataFrame(X_train_tfidf.A, columns = tfidf_vectorizer.get_feature_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2e506cf2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-01T10:27:55.939219Z",
     "iopub.status.busy": "2021-08-01T10:27:55.938090Z",
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     "shell.execute_reply.started": "2021-08-01T10:16:21.512928Z"
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "<p>5 rows × 40411 columns</p>\n",
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      "text/plain": [
       "   100percentfedup  21stcenturywire  ___  _url   aa  aadmi  aafe  aaja  \\\n",
       "0          0.00000              0.0  0.0   0.0  0.0    0.0   0.0   0.0   \n",
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       "4          0.00000              0.0  0.0   0.0  0.0    0.0   0.0   0.0   \n",
       "\n",
       "   aaliya  aaliyas  ...  überreicht  überschwänglich  überteuerten  \\\n",
       "0     0.0      0.0  ...         0.0              0.0           0.0   \n",
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       "\n",
       "   übertrieben  überwacht  überwachungskamera  überwachungskameras  \\\n",
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       "4          0.0        0.0                 0.0                  0.0   \n",
       "\n",
       "   überwältigt     übrig  übte  \n",
       "0          0.0  0.000000   0.0  \n",
       "1          0.0  0.000000   0.0  \n",
       "2          0.0  0.000000   0.0  \n",
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       "4          0.0  0.045969   0.0  \n",
       "\n",
       "[5 rows x 40411 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66b63b08",
   "metadata": {
    "papermill": {
     "duration": 0.040002,
     "end_time": "2021-08-01T10:27:56.058043",
     "exception": false,
     "start_time": "2021-08-01T10:27:56.018041",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Above is the representation of tf-idf matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ab4d876",
   "metadata": {
    "papermill": {
     "duration": 0.039937,
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     "status": "completed"
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   },
   "source": [
    "---\n",
    "<p style=\"font-family: Arials; line-height: 1.3; font-size: 27px; font-weight: bold; letter-spacing: 2px; text-align: center; color: #23527c\">Modelling</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ee377e89",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-01T10:27:56.224525Z",
     "iopub.status.busy": "2021-08-01T10:27:56.223806Z",
     "iopub.status.idle": "2021-08-01T10:28:32.451595Z",
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     "shell.execute_reply.started": "2021-08-01T10:16:21.554365Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.977\n"
     ]
    }
   ],
   "source": [
    "Adab = AdaBoostClassifier(DecisionTreeClassifier(max_depth=10),n_estimators=5,random_state=1)\n",
    "Adab.fit(tfidf_train, y_train)\n",
    "y_pred3 = Adab.predict(X_test_tfidf)\n",
    "ABscore = metrics.accuracy_score(y_test,y_pred3)\n",
    "print(\"accuracy: %0.3f\" %ABscore)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "629668c4",
   "metadata": {
    "papermill": {
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     "start_time": "2021-08-01T10:28:32.494752",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## **We got a testing accuracy of 0.959! Yay!**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7d8e58b",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "\n",
    "<p style=\"font-family: Arials; line-height: 1.3; font-size: 20px; font-weight: bold; letter-spacing: 2px; text-align: left; color: #23527c\">please upvote if you liked my notebook! :D</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9b1b86e",
   "metadata": {
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   "source": []
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