process.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: matplotlib_venn in /home/jujuba/miniconda3/envs/fake/lib/python3.11/site-packages (0.11.9)\n",
            "Requirement already satisfied: gensim in /home/jujuba/miniconda3/envs/fake/lib/python3.11/site-packages (4.3.1)\n",
            "Requirement already satisfied: matplotlib in /home/jujuba/miniconda3/envs/fake/lib/python3.11/site-packages (3.7.2)\n",
            "Requirement already satisfied: pandas in /home/jujuba/miniconda3/envs/fake/lib/python3.11/site-packages (1.5.3)\n",
            "Requirement already satisfied: numpy in /home/jujuba/miniconda3/envs/fake/lib/python3.11/site-packages (1.25.2)\n",
            "Requirement already satisfied: tqdm in /home/jujuba/miniconda3/envs/fake/lib/python3.11/site-packages (4.66.1)\n",
            "Requirement already satisfied: scipy in /home/jujuba/miniconda3/envs/fake/lib/python3.11/site-packages (from matplotlib_venn) (1.11.1)\n",
            "Requirement already satisfied: smart-open>=1.8.1 in /home/jujuba/miniconda3/envs/fake/lib/python3.11/site-packages (from gensim) (6.3.0)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /home/jujuba/miniconda3/envs/fake/lib/python3.11/site-packages (from matplotlib) (1.1.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /home/jujuba/miniconda3/envs/fake/lib/python3.11/site-packages (from matplotlib) (0.11.0)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /home/jujuba/miniconda3/envs/fake/lib/python3.11/site-packages (from matplotlib) (4.42.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /home/jujuba/miniconda3/envs/fake/lib/python3.11/site-packages (from matplotlib) (1.4.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /home/jujuba/miniconda3/envs/fake/lib/python3.11/site-packages (from matplotlib) (23.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /home/jujuba/miniconda3/envs/fake/lib/python3.11/site-packages (from matplotlib) (10.0.0)\n",
            "Requirement already satisfied: pyparsing<3.1,>=2.3.1 in /home/jujuba/miniconda3/envs/fake/lib/python3.11/site-packages (from matplotlib) (3.0.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /home/jujuba/miniconda3/envs/fake/lib/python3.11/site-packages (from matplotlib) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /home/jujuba/miniconda3/envs/fake/lib/python3.11/site-packages (from pandas) (2022.7)\n",
            "Requirement already satisfied: six>=1.5 in /home/jujuba/miniconda3/envs/fake/lib/python3.11/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install matplotlib_venn gensim matplotlib pandas numpy tqdm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "zIUmmW67vs3H"
      },
      "outputs": [],
      "source": [
        "from collections import Counter\n",
        "from functools import reduce\n",
        "import os\n",
        "\n",
        "\n",
        "from matplotlib_venn import venn2_unweighted\n",
        "from gensim.utils import deaccent\n",
        "import matplotlib.pyplot as plt\n",
        "from tqdm.auto import tqdm\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "tqdm.pandas()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "64j4lYuov-a5"
      },
      "outputs": [],
      "source": [
        "datasets = dict()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "RAW_DATA_DIR = \"raw_data\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/mnt/d/Pesquisa/Fact br eval/raw_data\n"
          ]
        }
      ],
      "source": [
        "%cd {RAW_DATA_DIR}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## A - Baixar dados"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [],
      "source": [
        "#!wget https://github.com/kamplus/FakeNewsSet/raw/master/Dataset/News_fake.csv\n",
        "#!wget https://github.com/kamplus/FakeNewsSet/raw/master/Dataset/News_notFake.csv\n",
        "#!wget https://zenodo.org/record/5191798/files/central_de_fatos.csv\n",
        "#!wget https://github.com/Gabriel-Lino-Garcia/FakeRecogna/raw/master/dataset/FakeRecogna.xlsx\n",
        "#!git clone https://github.com/roneysco/Fake.br-Corpus\n",
        "#!gdown --id 1Xx_4bw37cgrEkEhQ1toLsIbAj2pLgvDx -O fact-check_tweet_dataset.zip\n",
        "#!unzip fact-check_tweet_dataset.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## B - Importar dados"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "7ChABOSnvx5X"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "11647\n",
            "10551\n"
          ]
        }
      ],
      "source": [
        "datasets[\"Central de fatos\"] = pd.read_csv(\"central_de_fatos.csv\", sep=\";\")\n",
        "print(datasets[\"Central de fatos\"].shape[0])\n",
        "datasets[\"Central de fatos\"][\"rating\"] = datasets[\"Central de fatos\"][\"rating\"].str.lower().apply(eval).apply(set)\n",
        "datasets[\"Central de fatos\"] = datasets[\"Central de fatos\"][datasets[\"Central de fatos\"][\"rating\"].apply(len) == 1]\n",
        "print(datasets[\"Central de fatos\"].shape[0])\n",
        "datasets[\"Central de fatos\"][\"categories\"] = datasets[\"Central de fatos\"][\"categories\"] \\\n",
        "    .apply(lambda c: eval(c) if isinstance(c, str) and \"[\" in c else c) \\\n",
        "    .apply(lambda c: c[0] if isinstance(c, list) and len(c) > 0 else c) \\\n",
        "    .apply(lambda c: \"\" if isinstance(c, list) and len(c) == 0 else c)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [],
      "source": [
        "fake_news_set_fake = pd.read_csv(\"News_fake.csv\")\n",
        "fake_news_set_fake[\"alternative_name\"] = \"fake\"\n",
        "\n",
        "fake_news_set_not_fake = pd.read_csv(\"News_notFake.csv\")\n",
        "fake_news_set_not_fake[\"alternative_name\"] = \"not_fake\"\n",
        "\n",
        "fake_news_set = pd.concat([fake_news_set_fake, fake_news_set_not_fake])\n",
        "datasets[\"FakeNewsSet\"] = fake_news_set\n",
        "datasets[\"FakeNewsSet\"][\"tweet_ids\"] = datasets[\"FakeNewsSet\"][\"tweet_ids\"].str.split(\"\\t\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "600\n",
            "27059\n"
          ]
        }
      ],
      "source": [
        "print(datasets[\"FakeNewsSet\"].shape[0])\n",
        "print(datasets[\"FakeNewsSet\"][\"tweet_ids\"].apply(len).sum())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "columns = [\n",
        " \"author\",\n",
        " \"link\",\n",
        " \"category\",\n",
        " \"date of publication\",\n",
        " \"number of tokens\",\n",
        " \"number of words without punctuation\",\n",
        " \"number of types\",\n",
        " \"number of links inside the news\",\n",
        " \"number of words in upper case\",\n",
        " \"number of verbs\",\n",
        " \"number of subjuntive and imperative verbs\",\n",
        " \"number of nouns\",\n",
        " \"number of adjectives\",\n",
        " \"number of adverbs\",\n",
        " \"number of modal verbs (mainly auxiliary verbs)\",\n",
        " \"number of singular first and second personal pronouns\",\n",
        " \"number of plural first personal pronouns\",\n",
        " \"number of pronouns\",\n",
        " \"pausality\",\n",
        " \"number of characters\",\n",
        " \"average sentence length\",\n",
        " \"average word length\",\n",
        " \"percentage of news with speeling errors\",\n",
        " \"emotiveness\",\n",
        " \"diversity\",\n",
        "]\n",
        "\n",
        "raw_data = list()\n",
        "\n",
        "\n",
        "for label in [\"fake\", \"true\"]:\n",
        "    text_dir = os.path.join(\"Fake.br-Corpus\", \"full_texts\", label)\n",
        "    metadata_dir = os.path.join(\"Fake.br-Corpus\", \"full_texts\", f\"{label}-meta-information\")\n",
        "    crop_dir = os.path.join(\"Fake.br-Corpus\", \"size_normalized_texts\", label)\n",
        "\n",
        "    for example in tqdm(os.listdir(text_dir)):\n",
        "        text_path = os.path.join(text_dir, example)\n",
        "        metadata_path = os.path.join(metadata_dir, example.replace(\".txt\",\"-meta.txt\"))\n",
        "        crop_path = os.path.join(crop_dir, example)\n",
        "    \n",
        "        with open(text_path, \"r\") as f:\n",
        "            text = f.read()\n",
        "\n",
        "        cropped_text = np.nan\n",
        "        if os.path.exists(crop_path):\n",
        "            with open(crop_path, \"r\") as f:\n",
        "                cropped_text = f.read()\n",
        "\n",
        "        with open(metadata_path, \"r\") as f:\n",
        "            metadata = f.read().split(\"\\n\")\n",
        "\n",
        "        example_data =  dict(zip(columns, metadata))\n",
        "        example_data[\"text\"] = text\n",
        "        example_data[\"size_normalized_text\"] = cropped_text\n",
        "        example_data[\"label\"] = label\n",
        "\n",
        "        raw_data.append(example_data)\n",
        "\n",
        "raw_data = pd.DataFrame(raw_data)\n",
        "prepro_data = pd.read_csv(os.path.join(\"Fake.br-Corpus\", \"preprocessed\", \"pre-processed.csv\"), index_col=\"index\")\n",
        "raw_data[\"preprocessed_news\"] = prepro_data[\"preprocessed_news\"]\n",
        "datasets[\"Fake.br\"] = raw_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "804\n",
            "803\n"
          ]
        }
      ],
      "source": [
        "datasets[\"Fact-check_tweet\"] = pd.read_csv(\"pt_pt.csv\")\n",
        "print(datasets[\"Fact-check_tweet\"].shape[0])\n",
        "datasets[\"Fact-check_tweet\"] = datasets[\"Fact-check_tweet\"][~datasets[\"Fact-check_tweet\"][\"article_url\"].str.startswith(\"https://factly.in\")]\n",
        "datasets[\"Fact-check_tweet\"] = datasets[\"Fact-check_tweet\"].reset_index()\n",
        "print(datasets[\"Fact-check_tweet\"].shape[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "11903\n",
            "11902\n"
          ]
        }
      ],
      "source": [
        "datasets[\"FakeRecogna\"] = pd.read_excel(\"FakeRecogna.xlsx\")\n",
        "print(datasets[\"FakeRecogna\"].shape[0])\n",
        "datasets[\"FakeRecogna\"] = datasets[\"FakeRecogna\"][datasets[\"FakeRecogna\"][\"URL\"].notna()]\n",
        "print(datasets[\"FakeRecogna\"].shape[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## C - Selecionar datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Selecionei somente os datasets que possuem uma url atrelada a alegação e/ou a fonte"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [],
      "source": [
        "claim_datasets = {\n",
        "    \"Fake.br\": 'link',\n",
        "\n",
        "    # ambos são de tweets\n",
        "    \"Fact-check_tweet\": 'tweet_id',\n",
        "    \"FakeNewsSet\": \"tweet_ids\",\n",
        "}\n",
        "\n",
        "\n",
        "review_datasets = {\n",
        "    \"FakeRecogna\": 'URL',\n",
        "    \"Central de fatos\": 'url',\n",
        "    \"Fact-check_tweet\": 'article_url',\n",
        "    \"FakeNewsSet\": 'news_url',\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## D - Unificar"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [],
      "source": [
        "def norm_domain(domain):\n",
        "    for suffix in [\"gazetadigital.com.br\", \"tnh1.com.br\", \"folhavitoria.com.br\", \"jornaldebrasilia.com.br\"]:\n",
        "        if domain in suffix:\n",
        "            return \"r7.com\"\n",
        "\n",
        "    for suffix in [\"globo.com\", \"uol.com.br\", \"estadao.com.br\", \"sapo.pt\", \"afp.com\", \"r7.com\"]:\n",
        "        if suffix in domain:\n",
        "            return suffix\n",
        "\n",
        "    return domain\n",
        "\n",
        "treat_url = lambda url: \"//\".join(url.split(\"//\")[1:]).replace(\"www.\",\"\")\n",
        "find_domain = lambda url: norm_domain(treat_url(url).split(\"/\")[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [],
      "source": [
        "raw_df = datasets[\"FakeRecogna\"]\n",
        "\n",
        "fakeRecogna = pd.DataFrame()\n",
        "\n",
        "fakeRecogna[\"review_text\"] = raw_df[\"Titulo\"] + \"\\n\" + raw_df[\"Subtitulo\"].fillna(\"\") + \"\\n\" + raw_df[\"Noticia\"]\n",
        "fakeRecogna[\"review_text\"] = fakeRecogna[\"review_text\"].str.strip()\n",
        "\n",
        "fakeRecogna[\"review_author\"] = raw_df[\"Autor\"].fillna(\"\").str.strip()\n",
        "fakeRecogna[\"review_author\"] = fakeRecogna[\"review_author\"].apply(lambda a: a if \"/20\" not in a else \"\")\n",
        "fakeRecogna[\"review_author\"] = fakeRecogna[\"review_author\"].fillna(\"\").str.replace(\"Por \", \"\")\n",
        "fakeRecogna[\"review_author\"] = fakeRecogna[\"review_author\"].apply(lambda a: a if len(a.split(\" \")) <= 22 else \"\")\n",
        "\n",
        "fakeRecogna[\"review_url\"] = raw_df[\"URL\"].fillna(\"\").str.strip()\n",
        "fakeRecogna[\"review_domain\"] = fakeRecogna[\"review_url\"].apply(find_domain)\n",
        "\n",
        "fakeRecogna[\"review_date\"] = raw_df[\"Data\"].fillna(\"\").str.strip()\n",
        "fakeRecogna[\"review_date\"] = fakeRecogna[\"review_date\"].apply(lambda d: d if isinstance(d, str) and len(d) >= 10 else \"\")\n",
        "fakeRecogna[\"review_date\"] = fakeRecogna[\"review_date\"].apply(lambda d: d[:10] if isinstance(d, str) and len(d) > 10 else d)\n",
        "fakeRecogna[\"review_date\"] = fakeRecogna[\"review_date\"].fillna(\"\").str.strip()\n",
        "\n",
        "fakeRecogna[\"category\"] = raw_df[\"Categoria\"]\n",
        "fakeRecogna[\"is_fake\"] = raw_df[\"Classe\"].map({0.0: 1, 1.0: -1})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {},
      "outputs": [],
      "source": [
        "FAKE_LABELS = {\"boato\", \"falso\", \"fake\", \"enganoso\", \"fora de contexto\", \"distorcido\", \"exagerado\", \"constraditório\", \"impreciso\", \"insustentável\", \"contexto errado\"}\n",
        "\n",
        "def map_rating(rating: set):\n",
        "    if rating.intersection(FAKE_LABELS):\n",
        "        return 1\n",
        "    elif rating.intersection({\"verdadeiro, mas\", \"ainda é cedo para dizer\"}):\n",
        "        return 0\n",
        "    \n",
        "    return -1\n",
        "\n",
        "raw_df = datasets['Central de fatos']\n",
        "\n",
        "\n",
        "central_de_fatos = pd.DataFrame()\n",
        "\n",
        "central_de_fatos[\"review_text\"] = raw_df[\"title\"] + \"\\n\" + raw_df[\"subtitle\"].fillna(\"\") + \"\\n\" + raw_df[\"text_news\"]\n",
        "central_de_fatos[\"review_text\"] = central_de_fatos[\"review_text\"].str.strip()\n",
        "\n",
        "central_de_fatos[\"review_author\"] = \"\"\n",
        "central_de_fatos[\"review_url\"] = raw_df[\"url\"].str.strip()\n",
        "central_de_fatos[\"review_domain\"] = central_de_fatos[\"review_url\"].apply(find_domain)\n",
        "central_de_fatos[\"review_date\"] = raw_df[\"publication_date\"].fillna(\"\").str.strip()\n",
        "central_de_fatos[\"category\"] = raw_df[\"categories\"].str.lower()\n",
        "central_de_fatos[\"is_fake\"] = raw_df[\"rating\"].map(map_rating)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {},
      "outputs": [],
      "source": [
        "fact_check_tt_pt = pd.DataFrame()\n",
        "raw_df = datasets['Fact-check_tweet']\n",
        "fact_check_tt_pt[\"review_url\"] = raw_df[\"article_url\"]\n",
        "fact_check_tt_pt[\"review_domain\"] = fact_check_tt_pt[\"review_url\"].apply(find_domain)\n",
        "fact_check_tt_pt[\"claim_ids\"] = raw_df[\"tweet_id\"].apply(lambda x: [x])\n",
        "fact_check_tt_pt[\"is_fake\"] = raw_df[\"label\"].map({0: -1, 1:1})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [],
      "source": [
        "fakeNewsSet = pd.DataFrame()\n",
        "raw_df = datasets[\"FakeNewsSet\"]\n",
        "fakeNewsSet[\"review_url\"] = raw_df[\"news_url\"]\n",
        "fakeNewsSet[\"review_domain\"] = fakeNewsSet[\"review_url\"].apply(find_domain)\n",
        "fakeNewsSet[\"claim_ids\"] = raw_df[\"tweet_ids\"]\n",
        "fakeNewsSet[\"is_fake\"] = raw_df[\"alternative_name\"].map({\"fake\": 1, \"not_fake\":-1})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {},
      "outputs": [],
      "source": [
        "fakebr = pd.DataFrame()\n",
        "raw_df = datasets['Fake.br']\n",
        "fakebr[\"claim_text\"] = raw_df[\"text\"]\n",
        "fakebr[\"claim_author\"] = raw_df[\"author\"]\n",
        "fakebr[\"claim_url\"] = raw_df[\"link\"]\n",
        "fakebr[\"claim_date\"] = raw_df[\"date of publication\"]\n",
        "fakebr[\"category\"] = raw_df[\"category\"]\n",
        "fakebr[\"is_fake\"] = raw_df[\"label\"].map({\"fake\":  1, \"true\": -1})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {},
      "outputs": [],
      "source": [
        "final_dataset = {\n",
        "    \"fact_check_tweet_pt\": fact_check_tt_pt,\n",
        "    \"FakeNewsSet\": fakeNewsSet,\n",
        "    \"FakeRecogna\": fakeRecogna,\n",
        "    \"central_de_fatos\": central_de_fatos,\n",
        "    \"fakebr\": fakebr,\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {},
      "outputs": [],
      "source": [
        "review_urls = {dataset: set(final_dataset[dataset][\"review_url\"]) for dataset  in final_dataset if dataset != \"fakebr\"}\n",
        "review_urls = {d: {treat_url(url) for url in urls} for d, urls in review_urls.items()}\n",
        "\n",
        "all_review_urls = reduce(lambda a, b: a.union(b), review_urls.values())\n",
        "\n",
        "review_urls_count = list()\n",
        "for review_url in all_review_urls:\n",
        "    item = {\"review_url\": review_url}\n",
        "    item.update({dataset: review_url in review_urls[dataset] for dataset in review_urls.keys()})\n",
        "    review_urls_count.append(item)\n",
        "\n",
        "review_urls_count = pd.DataFrame(review_urls_count).set_index(\"review_url\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "20028"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(all_review_urls)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {},
      "outputs": [],
      "source": [
        "url_idx = pd.DataFrame(review_urls_count.index).reset_index()\n",
        "url_idx[\"index\"] = url_idx[\"index\"].apply(lambda idx: f\"review_{idx:05d}\")\n",
        "url_idx = url_idx.set_index(\"review_url\")[\"index\"].to_dict()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {},
      "outputs": [],
      "source": [
        "review_id = fakeRecogna[\"review_url\"].apply(lambda url: \"//\".join(url.split(\"//\")[1:]).replace(\"www.\",\"\")).map(url_idx)\n",
        "fakeRecogna.insert(0, \"review_id\", review_id)\n",
        "\n",
        "review_id = central_de_fatos[\"review_url\"].apply(lambda url: \"//\".join(url.split(\"//\")[1:]).replace(\"www.\",\"\")).map(url_idx)\n",
        "central_de_fatos.insert(0, \"review_id\", review_id)\n",
        "\n",
        "review_id = fact_check_tt_pt[\"review_url\"].apply(lambda url: \"//\".join(url.split(\"//\")[1:]).replace(\"www.\",\"\")).map(url_idx)\n",
        "fact_check_tt_pt.insert(0, \"review_id\", review_id)\n",
        "\n",
        "review_id = fakeNewsSet[\"review_url\"].apply(lambda url: \"//\".join(url.split(\"//\")[1:]).replace(\"www.\",\"\")).map(url_idx)\n",
        "fakeNewsSet.insert(0, \"review_id\", review_id)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {},
      "outputs": [],
      "source": [
        "review_urls_count.columns = [\"FakeRecogna\", \"central_de_fatos\", \"fact_check_tweet_pt\", 'FakeNewsSet']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c2cb6ffef1a246b5a69bd5c3b8bbcd22",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/3303 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "diff_reviews = set()\n",
        "\n",
        "intersection_urls = review_urls_count[review_urls_count.sum(axis=1) > 1]\n",
        "for url, row in tqdm(intersection_urls.iterrows(), total=intersection_urls.shape[0]):\n",
        "    intersection_label = set()\n",
        "\n",
        "    review_id = url_idx[url]\n",
        "    for dataset in row[row == True].index:\n",
        "        dataset_label = final_dataset[dataset][final_dataset[dataset][\"review_id\"] == review_id][\"is_fake\"]\n",
        "        if not dataset_label.any():\n",
        "            continue\n",
        "        \n",
        "        dataset_label = dataset_label.iloc[0] \n",
        "        intersection_label.add(dataset_label)\n",
        "\n",
        "    if len(intersection_label) > 1:\n",
        "        diff_reviews.add(review_id)\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {},
      "outputs": [],
      "source": [
        "for dataset in final_dataset:\n",
        "    if not \"review_id\" in final_dataset[dataset].columns:\n",
        "        continue\n",
        "\n",
        "    final_dataset[dataset] = final_dataset[dataset][~final_dataset[dataset][\"review_id\"].isin(diff_reviews)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/mnt/d/Pesquisa/Fact br eval\n"
          ]
        }
      ],
      "source": [
        "%cd ..\n",
        "os.makedirs(\"data\", exist_ok=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {},
      "outputs": [],
      "source": [
        "for dataset in final_dataset:\n",
        "    final_dataset[dataset].to_csv(os.path.join(\"data\",f\"{dataset}.tsv\"), sep=\"\\t\", index=None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 106,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "updating: data/ (stored 0%)\n",
            "updating: data/central_de_fatos.tsv (deflated 68%)\n",
            "updating: data/fact_check_tweet_pt.tsv (deflated 75%)\n",
            "updating: data/FakeNewsSet.tsv (deflated 61%)\n",
            "updating: data/FakeRecogna.tsv (deflated 65%)\n",
            "updating: data/fakebr.tsv (deflated 63%)\n"
          ]
        }
      ],
      "source": [
        "!zip -r FactChecksbr.zip data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## E - Análises iniciais"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {},
      "outputs": [],
      "source": [
        "pretty_name = {\n",
        "    \"fact_check_tweet_pt\": \"Fact-check_tweet (pt)\",\n",
        "    \"FakeNewsSet\": \"FakeNewsSet\",\n",
        "    \"FakeRecogna\": \"FakeRecogna\",\n",
        "    \"central_de_fatos\": \"Central de fatos\",\n",
        "    \"fakebr\": \"Fake.br\",\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Fact-check_tweet (pt) 656 656\n",
            "FakeNewsSet 26970 598\n",
            "FakeRecogna 0 11773\n",
            "Central de fatos 0 10461\n",
            "Fake.br 7200 0\n"
          ]
        }
      ],
      "source": [
        "for dataset in final_dataset:\n",
        "    name = pretty_name[dataset]\n",
        "    n_reviews = 0\n",
        "    n_claims = 0\n",
        "\n",
        "    if dataset != \"fakebr\":\n",
        "        n_reviews = final_dataset[dataset].shape[0]\n",
        "    else:\n",
        "        n_claims = final_dataset[dataset].shape[0]\n",
        "    \n",
        "    if dataset in [\"FakeNewsSet\", \"fact_check_tweet_pt\"]:\n",
        "        n_claims = final_dataset[dataset][\"claim_ids\"].apply(len).sum()\n",
        "\n",
        "    print(name, n_claims, n_reviews)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {},
      "outputs": [],
      "source": [
        "domain_count = pd.DataFrame()\n",
        "for dataset in final_dataset:\n",
        "    if dataset == \"fakebr\":\n",
        "        continue\n",
        "\n",
        "    count = final_dataset[dataset].value_counts(\"review_domain\").reset_index(name=pretty_name[dataset])\n",
        "    \n",
        "    if domain_count.shape[0] == 0:\n",
        "        domain_count = count\n",
        "    else:\n",
        "        domain_count = domain_count.merge(count, on=\"review_domain\", how=\"outer\")\n",
        "\n",
        "domain_count = domain_count.fillna(0).set_index(\"review_domain\").astype(int)\n",
        "domain_count = domain_count.loc[(domain_count > 0).sum(axis=1).sort_values(ascending=False).index]\n",
        "domain_count = domain_count.loc[(domain_count > 0).sum(axis=1).sort_values(ascending=False).index]\n",
        "domain_count = domain_count.loc[domain_count.index[:-6].to_list() + [\"e-farsas.com\", \"gov.br\", \"sapo.pt\", \"observador.pt\", \"r7.com\", \"ricmais.com.br\"]]\n",
        "domain_count = domain_count[[\"FakeRecogna\", \"Central de fatos\", \"Fact-check_tweet (pt)\", \"FakeNewsSet\"]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "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>FakeRecogna</th>\n",
              "      <th>Central de fatos</th>\n",
              "      <th>Fact-check_tweet (pt)</th>\n",
              "      <th>FakeNewsSet</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>review_domain</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>uol.com.br</th>\n",
              "      <td>4987</td>\n",
              "      <td>1824</td>\n",
              "      <td>214</td>\n",
              "      <td>143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>globo.com</th>\n",
              "      <td>2451</td>\n",
              "      <td>785</td>\n",
              "      <td>27</td>\n",
              "      <td>279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>aosfatos.org</th>\n",
              "      <td>0</td>\n",
              "      <td>1430</td>\n",
              "      <td>135</td>\n",
              "      <td>142</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>afp.com</th>\n",
              "      <td>503</td>\n",
              "      <td>0</td>\n",
              "      <td>60</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>boatos.org</th>\n",
              "      <td>2425</td>\n",
              "      <td>5468</td>\n",
              "      <td>45</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>projetocomprova.com.br</th>\n",
              "      <td>388</td>\n",
              "      <td>361</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>estadao.com.br</th>\n",
              "      <td>0</td>\n",
              "      <td>593</td>\n",
              "      <td>70</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>apublica.org</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>e-farsas.com</th>\n",
              "      <td>804</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gov.br</th>\n",
              "      <td>215</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sapo.pt</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>73</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>observador.pt</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>r7.com</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ricmais.com.br</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        FakeRecogna  Central de fatos  Fact-check_tweet (pt)  \\\n",
              "review_domain                                                                  \n",
              "uol.com.br                     4987              1824                    214   \n",
              "globo.com                      2451               785                     27   \n",
              "aosfatos.org                      0              1430                    135   \n",
              "afp.com                         503                 0                     60   \n",
              "boatos.org                     2425              5468                     45   \n",
              "projetocomprova.com.br          388               361                      2   \n",
              "estadao.com.br                    0               593                     70   \n",
              "apublica.org                      0                 0                      4   \n",
              "e-farsas.com                    804                 0                      0   \n",
              "gov.br                          215                 0                      0   \n",
              "sapo.pt                           0                 0                     73   \n",
              "observador.pt                     0                 0                     26   \n",
              "r7.com                            0                 0                      0   \n",
              "ricmais.com.br                    0                 0                      0   \n",
              "\n",
              "                        FakeNewsSet  \n",
              "review_domain                        \n",
              "uol.com.br                      143  \n",
              "globo.com                       279  \n",
              "aosfatos.org                    142  \n",
              "afp.com                          10  \n",
              "boatos.org                        0  \n",
              "projetocomprova.com.br            0  \n",
              "estadao.com.br                    0  \n",
              "apublica.org                     11  \n",
              "e-farsas.com                      0  \n",
              "gov.br                            0  \n",
              "sapo.pt                           0  \n",
              "observador.pt                     0  \n",
              "r7.com                           12  \n",
              "ricmais.com.br                    1  "
            ]
          },
          "execution_count": 89,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "domain_count"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "ax = domain_count.T.plot(kind='bar', stacked=True, color=[plt.cm.Accent(i) for i in range(15)], title=\"Número de fontes por conjunto de dados\")\n",
        "_ = plt.legend(bbox_to_anchor=(1.05, 1.0), loc='upper left')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "_ = venn2_unweighted([review_urls[\"FakeRecogna\"], review_urls[\"central_de_fatos\"]], set_labels=[pretty_name[\"FakeRecogna\"], pretty_name[\"central_de_fatos\"]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {},
      "outputs": [],
      "source": [
        "categories_1  = final_dataset[\"FakeRecogna\"].value_counts(subset=\"category\").reset_index(name=\"FakeRecogna\")\n",
        "categories_1[\"category\"] = categories_1[\"category\"].str.lower()\n",
        "\n",
        "categories_2 = final_dataset[\"central_de_fatos\"].value_counts(subset=\"category\").reset_index(name=\"Central de fatos\")\n",
        "categories_2[\"category\"] = categories_2[\"category\"].str.lower()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "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>category</th>\n",
              "      <th>FakeRecogna</th>\n",
              "      <th>Central de fatos</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>entretenimento</td>\n",
              "      <td>1383</td>\n",
              "      <td>460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>brasil</td>\n",
              "      <td>890</td>\n",
              "      <td>1076</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>mundo</td>\n",
              "      <td>558</td>\n",
              "      <td>546</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         category  FakeRecogna  Central de fatos\n",
              "0  entretenimento         1383               460\n",
              "1          brasil          890              1076\n",
              "2           mundo          558               546"
            ]
          },
          "execution_count": 56,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "categories_1.merge(categories_2, on=\"category\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "_ = venn2_unweighted([review_urls['fact_check_tweet_pt'], review_urls[\"FakeNewsSet\"]], set_labels=[pretty_name['fact_check_tweet_pt'], \"FakeNewsSet\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "23488"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "domain_count.sum().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "3303 130\n"
          ]
        }
      ],
      "source": [
        "print(intersection_urls.shape[0], len(diff_reviews))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "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>FakeRecogna</th>\n",
              "      <th>central_de_fatos</th>\n",
              "      <th>fact_check_tweet_pt</th>\n",
              "      <th>FakeNewsSet</th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>8841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>7298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>312</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   FakeRecogna  central_de_fatos  fact_check_tweet_pt  FakeNewsSet     0\n",
              "0        False             False                 True        False  8841\n",
              "1        False             False                False         True  7298\n",
              "6         True             False                False         True   192\n",
              "3        False              True                False        False   312"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "review_urls_count.value_counts().reset_index().loc[[0,1,6,3]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "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>FakeRecogna</th>\n",
              "      <th>central_de_fatos</th>\n",
              "      <th>fact_check_tweet_pt</th>\n",
              "      <th>FakeNewsSet</th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>2675</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>274</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    FakeRecogna  central_de_fatos  fact_check_tweet_pt  FakeNewsSet     0\n",
              "2         False             False                 True         True  2675\n",
              "8          True             False                 True        False    42\n",
              "10        False              True                 True        False     5\n",
              "5         False              True                False         True   250\n",
              "4          True             False                False        False   274\n",
              "11         True              True                False        False     3"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "review_urls_count.value_counts().reset_index().loc[[2,8,10,5,4,11]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "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>FakeRecogna</th>\n",
              "      <th>central_de_fatos</th>\n",
              "      <th>fact_check_tweet_pt</th>\n",
              "      <th>FakeNewsSet</th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   FakeRecogna  central_de_fatos  fact_check_tweet_pt  FakeNewsSet    0\n",
              "7         True             False                 True         True  112\n",
              "9         True              True                False         True   24"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "review_urls_count.value_counts().reset_index().loc[[7,9]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {},
      "outputs": [],
      "source": [
        "review_count = review_urls_count.sum(axis=1).sort_values(ascending=False).reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1    16725\n",
              "2     3167\n",
              "3      136\n",
              "Name: 0, dtype: int64"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "review_count[0].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {},
      "outputs": [],
      "source": [
        "tweet_ids = dict()\n",
        "tweet_ids['Fact-check_tweet (pt)'] = set(final_dataset['fact_check_tweet_pt']['claim_ids'].apply(lambda item: item[0]))\n",
        "tweet_ids[\"FakeNewsSet\"] = reduce(lambda a, b: set(a).union(set(b)),final_dataset[\"FakeNewsSet\"]['claim_ids'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "_ = venn2_unweighted([tweet_ids['Fact-check_tweet (pt)'], tweet_ids[\"FakeNewsSet\"]], set_labels=['Fact-check_tweet (pt)', \"FakeNewsSet\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {},
      "outputs": [],
      "source": [
        "categories_1_fixed = final_dataset[\"FakeRecogna\"][\"review_url\"].apply(lambda url: \"//\".join(url.split(\"//\")[1:]).replace(\"www.\",\"\").split(\"/\")[1])\n",
        "categories_1_fixed = categories_1_fixed[categories_1_fixed.str.len() < 30].dropna().value_counts().reset_index(name=\"FakeRecogna\").rename(columns={\"index\": \"category\"})\n",
        "categories_2[\"category\"] = categories_2[\"category\"].apply(deaccent)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "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>category</th>\n",
              "      <th>Central de fatos</th>\n",
              "      <th>FakeRecogna</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td></td>\n",
              "      <td>2417</td>\n",
              "      <td>503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>politica</td>\n",
              "      <td>1545</td>\n",
              "      <td>1523</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>brasil</td>\n",
              "      <td>1076</td>\n",
              "      <td>336</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>saude</td>\n",
              "      <td>652</td>\n",
              "      <td>1303</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tecnologia</td>\n",
              "      <td>549</td>\n",
              "      <td>295</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>mundo</td>\n",
              "      <td>546</td>\n",
              "      <td>385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>entretenimento</td>\n",
              "      <td>460</td>\n",
              "      <td>163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>religiao</td>\n",
              "      <td>267</td>\n",
              "      <td>97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>esporte</td>\n",
              "      <td>215</td>\n",
              "      <td>276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>ciencia</td>\n",
              "      <td>78</td>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>opiniao</td>\n",
              "      <td>43</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>eleicoes</td>\n",
              "      <td>30</td>\n",
              "      <td>185</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          category  Central de fatos  FakeRecogna\n",
              "0                               2417          503\n",
              "1         politica              1545         1523\n",
              "2           brasil              1076          336\n",
              "3            saude               652         1303\n",
              "4       tecnologia               549          295\n",
              "5            mundo               546          385\n",
              "6   entretenimento               460          163\n",
              "7         religiao               267           97\n",
              "8          esporte               215          276\n",
              "9          ciencia                78           38\n",
              "10         opiniao                43            2\n",
              "11        eleicoes                30          185"
            ]
          },
          "execution_count": 61,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "categories_2.merge(categories_1_fixed,on=\"category\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {},
      "outputs": [],
      "source": [
        "label_count = pd.DataFrame()\n",
        "for dataset in final_dataset:\n",
        "    count = final_dataset[dataset].value_counts(\"is_fake\").reset_index(name=pretty_name[dataset])\n",
        "    \n",
        "    if label_count.shape[0] == 0:\n",
        "        label_count = count\n",
        "    else:\n",
        "        label_count = label_count.merge(count, on=\"is_fake\", how=\"outer\")\n",
        "\n",
        "label_count = label_count.sort_values(\"is_fake\").set_index(\"is_fake\").fillna(0).astype(int)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Balanceamento dos conjuntos de dados processados'}>"
            ]
          },
          "execution_count": 105,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "label_count.T.plot(kind=\"bar\", color=[\"green\", \"orange\", \"red\"], title=\"Balanceamento dos conjuntos de dados processados\")"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.4"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}