sample_experiment_round3.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UmYctjknFwYq"
      },
      "source": [
        "# 0. Initialize"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zi4RmGMbFwY1"
      },
      "source": [
        "## 0.1. Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T19:51:16.327348Z",
          "start_time": "2022-12-29T19:51:16.300408Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4gSxULDvFwY3",
        "outputId": "d401cd8f-b3f2-4cbf-a06f-c790fd82fd2d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The autoreload extension is already loaded. To reload it, use:\n",
            "  %reload_ext autoreload\n"
          ]
        }
      ],
      "source": [
        "%matplotlib inline\n",
        "%load_ext autoreload\n",
        "%autoreload 2\n",
        "import os, sys, glob\n",
        "import gzip\n",
        "import random\n",
        "import tqdm\n",
        "import json\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "pd.set_option(\"display.max_columns\", None)\n",
        "from datetime import datetime, timezone\n",
        "\n",
        "import re\n",
        "import string\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.python.keras.models import Sequential\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.python.keras.layers import Embedding, GRU, Dense\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "from IPython import display\n",
        "from IPython.display import clear_output\n",
        "import matplotlib as mpl\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "from IPython.display import clear_output"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MkNF8Z7qFwY7"
      },
      "source": [
        "## 0.2. DEFINE VARIABLES "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T19:51:18.531817Z",
          "start_time": "2022-12-29T19:51:18.502380Z"
        },
        "collapsed": true,
        "id": "1S0tP7M1FwZA"
      },
      "outputs": [],
      "source": [
        "DATA_PATH = 'D:\\Qsync\\BILGEHAN\\sabanci\\CS\\CS412\\project\\part2\\\\' # '<insert-your-training-data-path-here>'\n",
        "\n",
        "ROUND = 3 # This project will have 3 rounds of predictions: 1,2,3\n",
        "STUDENT_ID = '28452'#'<insert-your-id-here>'\n",
        "PROJECT_CODE = 'CS412de753a916abf'#'<insert-your-code-here>' # Same code for the annotation eg. CS412xxxxx"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Czw6tW6GFwZE"
      },
      "source": [
        "## 0.3. Read Training & Evaluation Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "62uwtRwFFwZH"
      },
      "source": [
        "### 0.3.1. Get the labels for tweets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T19:51:21.373018Z",
          "start_time": "2022-12-29T19:51:21.289213Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "zpSotPxgFwZI",
        "outputId": "2ec37d02-a62f-43b9-b8eb-3ad3055cde2d",
        "scrolled": false
      },
      "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>tweet_id</th>\n",
              "      <th>isPolitical</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1597170281545551872</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1431700027471192069</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1566035577090281472</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1591538690869940225</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1583898169238167554</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3795</th>\n",
              "      <td>1596978345560150016</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3796</th>\n",
              "      <td>1582399680343789568</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3797</th>\n",
              "      <td>1420407025847132171</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3798</th>\n",
              "      <td>1589993032975544320</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3799</th>\n",
              "      <td>1595521343587508236</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3800 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 tweet_id isPolitical\n",
              "0     1597170281545551872         Yes\n",
              "1     1431700027471192069          No\n",
              "2     1566035577090281472         Yes\n",
              "3     1591538690869940225         Yes\n",
              "4     1583898169238167554         Yes\n",
              "...                   ...         ...\n",
              "3795  1596978345560150016         Yes\n",
              "3796  1582399680343789568         Yes\n",
              "3797  1420407025847132171          No\n",
              "3798  1589993032975544320         Yes\n",
              "3799  1595521343587508236         Yes\n",
              "\n",
              "[3800 rows x 2 columns]"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#trainingTweetDf = pd.read_csv('{}training-tweet.csv'.format(DATA_PATH))\n",
        "trainingTweetDf = pd.read_csv('{}training-tweet.csv'.format(DATA_PATH), dtype={'tweet_id': str, 'isPolitical': str})\n",
        "trainingTweetDf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T19:51:24.272948Z",
          "start_time": "2022-12-29T19:51:24.242672Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bVFbM4TzFwZK",
        "outputId": "83488313-5597-4447-9f43-cedf4e1a3329"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Yes    2442\n",
              "No     1353\n",
              "Name: isPolitical, dtype: int64"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainingTweetDf.isPolitical.value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xjbgo85SFwZL"
      },
      "source": [
        "### 0.3.2. Get the labels for users"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T19:51:26.780981Z",
          "start_time": "2022-12-29T19:51:26.746390Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "wgvmL9pTFwZL",
        "outputId": "9f99db3f-e9df-4ce8-bb94-bb07e920c504",
        "scrolled": true
      },
      "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>screen_name</th>\n",
              "      <th>isBot</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>koftecancaddy</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ahaber</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>selahat03949652</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>erdin06357062</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>bhct__necatii</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3795</th>\n",
              "      <td>ddionysos_s</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3796</th>\n",
              "      <td>alpar_kaan</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3797</th>\n",
              "      <td>nedenburdaysam</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3798</th>\n",
              "      <td>muhibban_eco</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3799</th>\n",
              "      <td>bensuveyda_</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3800 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          screen_name isBot\n",
              "0       koftecancaddy    No\n",
              "1              ahaber    No\n",
              "2     selahat03949652    No\n",
              "3       erdin06357062    No\n",
              "4       bhct__necatii    No\n",
              "...               ...   ...\n",
              "3795      ddionysos_s    No\n",
              "3796       alpar_kaan    No\n",
              "3797   nedenburdaysam    No\n",
              "3798     muhibban_eco    No\n",
              "3799      bensuveyda_    No\n",
              "\n",
              "[3800 rows x 2 columns]"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainingUserDf = pd.read_csv('{}training-user.csv'.format(DATA_PATH))\n",
        "trainingUserDf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T19:51:29.432466Z",
          "start_time": "2022-12-29T19:51:29.391678Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tFLHQQF5FwZM",
        "outputId": "0f1988d4-03a1-4699-c8c1-2ef47ebac13c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "No          3027\n",
              "Yes          690\n",
              "Not sure      78\n",
              "Name: isBot, dtype: int64"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainingUserDf.isBot.value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d3OSIrlMFwZN"
      },
      "source": [
        "### 0.3.3. Expand your dataset with metadata and tweets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T19:06:17.870105Z",
          "start_time": "2022-12-29T19:06:17.848202Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o1sD_tP8FwZN",
        "outputId": "9c9fee60-7b62-42ca-a668-5ab4b53004da"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "http://www.onurvarol.com/Annotation-CS412-202201/reports/report_CS412de753a916abf.html\n"
          ]
        }
      ],
      "source": [
        "# You can also expand training data by downloading your own labeled datasets following the link\n",
        "# Download the documents under \"Link to training data\"\n",
        "\n",
        "print('http://www.onurvarol.com/Annotation-CS412-202201/reports/report_{}.html'.format(PROJECT_CODE))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9n4yOS2dFwZO"
      },
      "source": [
        "# 1. EXTRACT FEATURES\n",
        "Under *1.1. Political Tweet Detection* and *1.2. Bot Detection*, we firstly collect raw data for processing. We then combine some of them (total_interactions = num_favorites + num_retweets) or use them to extract features (whether the tweet has one of the political entities @meralaksener, @kilicdarogluk etc.).\n",
        "\n",
        "We expect you to collect more raw data from **tweet_metadata**, **user_profiles** and **user_tweets** files by creating a function as shown in below examples such as *check_if_retweet()* and using it while iterating over data as shown under *Merge Collected Features*.\n",
        "\n",
        "We also expect you to create new variables as much as you can from the data in order to make your predictions more accurate. For example, you may want to check:\n",
        "\n",
        "- The tweet sources that a user frequently uses\n",
        "- Whether the user is a verified account or not\n",
        "\n",
        "...\n",
        "\n",
        "to assess whether **a user is a bot or not** and whether **a tweet is political or not**."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T19:51:35.984719Z",
          "start_time": "2022-12-29T19:51:35.960085Z"
        },
        "collapsed": true,
        "id": "vFOHerRbFwZP"
      },
      "outputs": [],
      "source": [
        "PATH_TO_DOWNLOADED = DATA_PATH # 'D:/Users/suuser/Desktop/Sabancı/CS412/spring-2022/project/'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s-z4fp_TFwZP"
      },
      "source": [
        "## 1.1. Political Tweet Detection\n",
        "This part stands for the feature extraction of tweets. We start with collecting the raw data from *tweet_metadata*, then use some of them to extract features."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fNJfMw-0FwZQ"
      },
      "source": [
        "### 1.1.1. Get Raw Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CDBRP7MTbMu3",
        "outputId": "0da70bf8-ffd3-4e8b-ee81-813781c03501"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'def test(jsoninput): #test function to show how json inputs work and are parsed. Switch the \"service\" field between null and a value to see\\n    a = jsoninput[\\'service\\']\\n\\n    if a:\\n        a = a.lower()\\n        print(\"True: \" + a)\\n    else:\\n        print(\"False: \", a)\\n\\n\\ndata = \\'{ \"name\":\"WandaVision\", \"service\":\"1\" }\\'\\n\\ndict_1 = json.loads(data)\\ntest(dict_1)\\n'"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "'''def test(jsoninput): #test function to show how json inputs work and are parsed. Switch the \"service\" field between null and a value to see\n",
        "    a = jsoninput['service']\n",
        "\n",
        "    if a:\n",
        "        a = a.lower()\n",
        "        print(\"True: \" + a)\n",
        "    else:\n",
        "        print(\"False: \", a)\n",
        "\n",
        "\n",
        "data = '{ \"name\":\"WandaVision\", \"service\":\"1\" }'\n",
        "\n",
        "dict_1 = json.loads(data)\n",
        "test(dict_1)\n",
        "'''"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9axsmiBIFwZQ"
      },
      "source": [
        "#### 1.1.1.1. Check if Retweet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T19:51:38.278103Z",
          "start_time": "2022-12-29T19:51:38.251310Z"
        },
        "collapsed": true,
        "id": "98czJibCFwZR"
      },
      "outputs": [],
      "source": [
        "def check_if_retweet(tweet_metadata_line):\n",
        "    is_retweet = 0\n",
        "    retweeted_username = None\n",
        "\n",
        "    try:\n",
        "        tweet_metadata_line['retweeted_status'] #check if \"retweeted_status\" exists (may or may not, no guarantee). If so, return info on it\n",
        "        retweeted_username = tweet_metadata_line['retweeted_status']['user']['screen_name'].lower()\n",
        "        is_retweet = 1\n",
        "\n",
        "    except KeyError:\n",
        "        pass\n",
        "\n",
        "    return is_retweet, retweeted_username"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FgggEWWdFwZR"
      },
      "source": [
        "#### 1.1.1.2. Get Tweet Text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T19:51:40.164136Z",
          "start_time": "2022-12-29T19:51:40.140464Z"
        },
        "collapsed": true,
        "id": "fXMViD73FwZU"
      },
      "outputs": [],
      "source": [
        "def get_tweet_text(tweet_metadata_line):\n",
        "    text = tweet_metadata_line['text'].lower()\n",
        "    \n",
        "    return text"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kJF-BvekFwZV"
      },
      "source": [
        "#### 1.1.1.3. Get Tweet ID"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T19:51:41.568908Z",
          "start_time": "2022-12-29T19:51:41.542671Z"
        },
        "collapsed": true,
        "id": "unfB0mdVFwZV"
      },
      "outputs": [],
      "source": [
        "def get_tweet_id(tweet_metadata_line):\n",
        "    id_str = tweet_metadata_line['id_str']\n",
        "    \n",
        "    return id_str"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xzp8HgulFwZW"
      },
      "source": [
        "#### 1.1.1.4. Get Number of Mentions and Hashtags"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T19:51:43.434204Z",
          "start_time": "2022-12-29T19:51:43.407810Z"
        },
        "collapsed": true,
        "id": "qZ1aBEugFwZW"
      },
      "outputs": [],
      "source": [
        "def get_number_mentions_hashtags(tweet_metadata_line):\n",
        "    num_mentions = len(tweet_metadata_line['entities']['user_mentions'])\n",
        "    num_hashtags = len(tweet_metadata_line['entities']['hashtags'])\n",
        "\n",
        "    return num_mentions, num_hashtags"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jz1Eksa2FwZX"
      },
      "source": [
        "#### 1.1.1.5. Get Number of Retweets and Favorites"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T19:51:44.743701Z",
          "start_time": "2022-12-29T19:51:44.712534Z"
        },
        "collapsed": true,
        "id": "mZdorGdRFwZX"
      },
      "outputs": [],
      "source": [
        "def get_number_retweets_favorites(tweet_metadata_line):\n",
        "    retweet_count = tweet_metadata_line['retweet_count']\n",
        "    favorite_count = tweet_metadata_line['favorite_count']\n",
        "    \n",
        "    return retweet_count, favorite_count"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cmj_hIwUFwZY"
      },
      "source": [
        "#### 1.1.1.6. Get User Info"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T19:51:47.435465Z",
          "start_time": "2022-12-29T19:51:47.411177Z"
        },
        "collapsed": true,
        "id": "t6US-W_tFwZY"
      },
      "outputs": [],
      "source": [
        "def get_user_info(tweet_metadata_line):\n",
        "    id = tweet_metadata_line['user']['id_str']\n",
        "    screen_name = tweet_metadata_line['user']['screen_name'].lower()\n",
        "    description = tweet_metadata_line['user']['description']\n",
        "\n",
        "    return id, screen_name, description"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "esCiiHK7dc5-"
      },
      "source": [
        "#### 1.1.1.7. Check If Tweet Was Made As a Reply To Another User"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "vG4Qw_jcdqQi"
      },
      "outputs": [],
      "source": [
        "def check_if_reply(tweet_metadata_line):\n",
        "    is_reply = 0\n",
        "    parent_username = None\n",
        "\n",
        "    try:\n",
        "        parent_username = tweet_metadata_line['in_reply_to_screen_name']\n",
        "        if parent_username:\n",
        "            parent_username = parent_username.lower()\n",
        "            is_reply = 1\n",
        "\n",
        "    except KeyError:\n",
        "        pass\n",
        "\n",
        "    return is_reply, parent_username"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eV47rou9hchl"
      },
      "source": [
        "#### 1.1.1.8. Get User Location at the Time of Tweet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "8SoHJPp4hgR_"
      },
      "outputs": [],
      "source": [
        "def get_user_location(tweet_metadata_line): #no guarantee of this being simple. ex. \"Meram, Konya\", \"Ankara\"\n",
        "    location = tweet_metadata_line['user']['location']\n",
        "\n",
        "    if(location == \"\"):#no location info\n",
        "        return False\n",
        "\n",
        "    return location.lower()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rxWHQTsThiiT"
      },
      "source": [
        "#### 1.1.1.9. Is the User Verified?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "IEiwh46-hmfG"
      },
      "outputs": [],
      "source": [
        "def get_verified(tweet_metadata_line):\n",
        "    verified = tweet_metadata_line['user']['verified']\n",
        "    return verified"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M1GmCylmhogr"
      },
      "source": [
        "#### 1.1.1.10. Age of Account"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "80EpaIQRhsRr"
      },
      "outputs": [],
      "source": [
        "def get_age(tweet_metadata_line):\n",
        "    str_age = tweet_metadata_line['user']['created_at'] # ex. \"Tue Sep 06 13:22:17 +0000 2020\",\n",
        "    age = datetime.strptime(str_age, '%a %b %d %H:%M:%S %z %Y') #date formats: https://docs.python.org/3/library/datetime.html#strftime-and-strptime-format-codes\n",
        "\n",
        "    return age"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QKTVvrYfh7CR"
      },
      "source": [
        "#### 1.1.1.11. Get Friends and Followers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "KRx5XI7Lh7q4"
      },
      "outputs": [],
      "source": [
        "def get_friends_followers(tweet_metadata_line):\n",
        "    friends = tweet_metadata_line['user']['friends_count']\n",
        "    followers = tweet_metadata_line['user']['followers_count']\n",
        "\n",
        "    return friends, followers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y9RFufiliB12"
      },
      "source": [
        "#### 1.1.1.12 Number of Tweets They Have Made"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "irijQKpziI0g"
      },
      "outputs": [],
      "source": [
        "def get_tweet_count(tweet_metadata_line): #can be used along with age to see tweets per day ratio (how active they are)\n",
        "    tweets = tweet_metadata_line['user']['statuses_count']\n",
        "\n",
        "    return tweets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oAItVxUjiKN5"
      },
      "source": [
        "#### 1.1.1.13 Get How Many Times They Have Been Listed (tells you how influential the account is)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "aQZ5hNNUiSsL"
      },
      "outputs": [],
      "source": [
        "def get_listed_count(tweet_metadata_line):\n",
        "    listedCount = tweet_metadata_line['user']['listed_count']\n",
        "    return listedCount"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A2c-uNctiXMw"
      },
      "source": [
        "#### 1.1.1.14 The Source of the Tweet (PC or Mobile)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "XXFJMiPeiYHv"
      },
      "outputs": [],
      "source": [
        "def get_tweet_source(tweet_metadata_line):\n",
        "    tweetSource = tweet_metadata_line['source'].lower()\n",
        "    forLoc = tweetSource.find(\"for\")\n",
        "    tweetSource.replace(tweetSource[0:forLoc+4], \"\")\n",
        "\n",
        "    return tweetSource"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sa5v_LfLFwZZ"
      },
      "source": [
        "### 1.1.2. Derive Manually Crafted Features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5S-3JtR0FwZa"
      },
      "source": [
        "#### 1.1.2.1. Check for political entity in text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T19:51:49.101699Z",
          "start_time": "2022-12-29T19:51:49.073731Z"
        },
        "collapsed": true,
        "id": "e1ghv3zIFwZa"
      },
      "outputs": [],
      "source": [
        "def check_political_ent(text):\n",
        "    \n",
        "    # the list below can be modified and some new names may be added (or removed)\n",
        "    list_of_entities = ['06melihgokcek', 'ahmet_davutoglu', 'akaraismagiloglu', 'alibabacan', 'alimahir', 'arzuerdemdb', 'avhamzadag', 'barisatay', \n",
        "                        'bulent_arinc', 'by', 'bybekirbozdag', 'cbabdullahgul', 'celebimehmeta', 'chpmuratbakan', 'dbdevletbahceli', 'deryayanikashb', \n",
        "                        'dogu_perincek', 'drfahrettinkoca', 'durmusyillmaz', 'e_semihyalcin', 'eczozgurozel', 'ekrem_imamoglu', 'enginozkoc', 'erbakanfatih', \n",
        "                        'erkbas', 'fahrettinaltun', 'fatih_donmez', 'fethigurer', 'fuatoktay', 'ifarukaksu', 'ikalin1', 'jsarieroglu' 'kasapoglu', 'kilicdarogluk', \n",
        "                        'mahirunal', 'mansuryavas06', 'mehmedmus', 'mehmetnuriersoy', 'meral_aksener', 'mevlutcavusoglu', 'mkulunk', 'murat_kurum', \n",
        "                        'mustafa_destici', 'mustafaelitas', 'numankurtulmus', 'nureddinnebati', 'nurettincanikli', 'omerrcelik', 'rterdogan', 'seyit_torun', \n",
        "                        'suleymansoylu', 't_karamollaoglu', 'tcaytunciray', 'tuncsoyer', 'umitozdag', 'vahitkirisci', 'varank', 'vedatbilgn', 'vekilince', \n",
        "                        'yavuzagiraliog', 'yavuzyilmazd','tcmeb','herkesicinchp','akparti','iyiparti','hdpgenelmerkezi','devapartisi','tdp_turkiye','zaferpartisi',\n",
        "                        'memleketimparti','gelecekpartitr','saadetpartisi','mhp_bilgi','_mhpistanbul_','btp_parti','bbpgenelmerkez','rprefahpartisi','uabakanligi',\n",
        "                        'tctarim','ticaret','tcsanayi','saglikbakanligi','tcsavunma','tckulturturizm','tc_icisleri','hmbakanligi','gencliksporbak','tcenerji',\n",
        "                        'tc_disisleri','csbgovtr','csgbakanligi','tcailesosyal','adalet_bakanlik']\n",
        "    \n",
        "    entities_in_text = [ent for ent in list_of_entities if ent.lower() in text.lower()]\n",
        "    number_entities = len(entities_in_text)\n",
        "\n",
        "    return number_entities"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cFuNBmKKFwZa"
      },
      "source": [
        "#### 1.1.2.2. Number of total interactions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T19:51:51.428175Z",
          "start_time": "2022-12-29T19:51:51.403663Z"
        },
        "collapsed": true,
        "id": "Koh1HQDpFwZa"
      },
      "outputs": [],
      "source": [
        "def total_interactions(retweet_count, favorite_count):\n",
        "    total_num_interactions = retweet_count + favorite_count\n",
        "    \n",
        "    return total_num_interactions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MQcfKPhyjrFm"
      },
      "source": [
        "#### 1.1.2.3. Amount of Numbers in Username"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "Pzx8_LLrjvVM"
      },
      "outputs": [],
      "source": [
        "def UsernameNumberCount(displayName):\n",
        "    numberCount = 0\n",
        "    \n",
        "    for i in displayName:\n",
        "        if i.isnumeric():\n",
        "            numberCount += 1\n",
        "\n",
        "    return numberCount"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "KDrHmLKZNxgp"
      },
      "outputs": [],
      "source": [
        "def UsernameSpecialCharacterCount(displayName):\n",
        "    specialCount = 0\n",
        "    \n",
        "    for i in displayName:\n",
        "        if not i.isalpha():\n",
        "            specialCount += 1\n",
        "\n",
        "    return specialCount"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gbIaykiFj3cH"
      },
      "source": [
        "#### 1.1.2.4. Friends to Followers ratio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "Cz-Shsz3j4WT"
      },
      "outputs": [],
      "source": [
        "def FriendsVFollowers(friends, followers):\n",
        "\n",
        "  if (followers != 0):\n",
        "    return friends/followers\n",
        "  \n",
        "  else:\n",
        "    return friends/1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fnt67FYbj84-"
      },
      "source": [
        "### 1.1.2.5. Account Activeness"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "QYr98Urhj_nk"
      },
      "outputs": [],
      "source": [
        "def TweetsPerDay(tweetCount, age):\n",
        "    now = datetime.now(timezone.utc)\n",
        "    deltaTime = now-age\n",
        "\n",
        "    return tweetCount/deltaTime.days"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rnxco26rFwZb"
      },
      "source": [
        "### 1.1.2. Collect data using the functions above and transform into a Pandas DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T19:51:55.204868Z",
          "start_time": "2022-12-29T19:51:52.783226Z"
        },
        "collapsed": true,
        "id": "E2MPCh7bFwZb"
      },
      "outputs": [],
      "source": [
        "dfPolitical = {'tweet_id':[],\n",
        "              'is_retweet':[],\n",
        "              'retweeted_username':[],\n",
        "              'text':[],\n",
        "              'num_mentions':[],\n",
        "              'num_hashtags':[],\n",
        "              'num_retweets':[],\n",
        "              'num_favorites':[],\n",
        "              'user_id':[],\n",
        "              'user_screen_name':[],\n",
        "              'user_description':[],\n",
        "              'num_political_entities':[],\n",
        "              'total_interactions':[],\n",
        "              'total_special_char_in_username':[],\n",
        "              'friend_ratio':[],\n",
        "              'account_activeness':[],\n",
        "              'is_reply':[],\n",
        "              'reply_parent':[],\n",
        "              'location':[],\n",
        "              'is_verified':[],\n",
        "              'account_age':[],\n",
        "              'friend_count':[],\n",
        "              'follower_count':[],\n",
        "              'tweets_count':[],\n",
        "              'tweets_listed_count':[],\n",
        "              'tweet_source':[]}\n",
        "\n",
        "\n",
        "with gzip.open(f\"{PATH_TO_DOWNLOADED}tweet_metadata.jsons.gz\", \"rb\") as f:\n",
        "    for line in f:\n",
        "        line = json.loads(line)\n",
        "        get_tweet_source\n",
        "        # raw data:\n",
        "        id_str = get_tweet_id(line)\n",
        "        is_retweet, retweeted_username = check_if_retweet(line)\n",
        "        text = get_tweet_text(line)\n",
        "        num_mentions, num_hashtags = get_number_mentions_hashtags(line)\n",
        "        retweet_count, favorite_count = get_number_retweets_favorites(line)\n",
        "        user_id_str, screen_name, user_description = get_user_info(line)\n",
        "        is_reply, reply_parent = check_if_reply(line)\n",
        "        location = get_user_location(line)\n",
        "        is_verified = get_verified(line)\n",
        "        account_age = get_age(line)\n",
        "        friend_count, follower_count = get_friends_followers(line)\n",
        "        tweets_count = get_tweet_count(line)\n",
        "        tweets_listed_count = get_listed_count(line)\n",
        "        tweet_source = get_tweet_source(line)#not used or placed in df\n",
        "\n",
        "        # manually crafted data:\n",
        "        num_political_entities = check_political_ent(text)\n",
        "        total_num_interactions = total_interactions(retweet_count, favorite_count)\n",
        "        total_special_char_in_username = UsernameSpecialCharacterCount(screen_name)\n",
        "        friend_ratio = FriendsVFollowers(friend_count, follower_count)\n",
        "        account_activeness = TweetsPerDay(tweets_count, account_age)\n",
        "\n",
        "        dfPolitical['tweet_id'].append(id_str)\n",
        "        dfPolitical['is_retweet'].append(is_retweet)\n",
        "        dfPolitical['retweeted_username'].append(retweeted_username)\n",
        "        dfPolitical['text'].append(text)\n",
        "        dfPolitical['num_mentions'].append(num_mentions)\n",
        "        dfPolitical['num_hashtags'].append(num_hashtags)\n",
        "        dfPolitical['num_retweets'].append(retweet_count)\n",
        "        dfPolitical['num_favorites'].append(favorite_count)\n",
        "        dfPolitical['user_id'].append(user_id_str)\n",
        "        dfPolitical['user_screen_name'].append(screen_name)\n",
        "        dfPolitical['user_description'].append(user_description)\n",
        "        dfPolitical['num_political_entities'].append(num_political_entities)\n",
        "        dfPolitical['total_interactions'].append(total_num_interactions)\n",
        "        dfPolitical['total_special_char_in_username'].append(total_special_char_in_username)\n",
        "        dfPolitical['friend_ratio'].append(friend_ratio)\n",
        "        dfPolitical['account_activeness'].append(account_activeness)\n",
        "        dfPolitical['is_reply'].append(is_reply)\n",
        "        dfPolitical['reply_parent'].append(reply_parent)\n",
        "        dfPolitical['location'].append(location)\n",
        "        dfPolitical['is_verified'].append(is_verified)\n",
        "        dfPolitical['account_age'].append(account_age)\n",
        "        dfPolitical['friend_count'].append(friend_count)\n",
        "        dfPolitical['follower_count'].append(follower_count)\n",
        "        dfPolitical['tweets_count'].append(tweets_count)\n",
        "        dfPolitical['tweets_listed_count'].append(tweets_listed_count)\n",
        "        dfPolitical['tweet_source'].append(tweet_source)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T19:51:55.336507Z",
          "start_time": "2022-12-29T19:51:55.207199Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 783
        },
        "id": "miwcn0WHFwZc",
        "outputId": "2c56df51-b77f-4887-ccf8-a022745e44cf",
        "scrolled": true
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tweet_id</th>\n",
              "      <th>is_retweet</th>\n",
              "      <th>retweeted_username</th>\n",
              "      <th>text</th>\n",
              "      <th>num_mentions</th>\n",
              "      <th>num_hashtags</th>\n",
              "      <th>num_retweets</th>\n",
              "      <th>num_favorites</th>\n",
              "      <th>user_id</th>\n",
              "      <th>user_screen_name</th>\n",
              "      <th>user_description</th>\n",
              "      <th>num_political_entities</th>\n",
              "      <th>total_interactions</th>\n",
              "      <th>total_special_char_in_username</th>\n",
              "      <th>friend_ratio</th>\n",
              "      <th>account_activeness</th>\n",
              "      <th>is_reply</th>\n",
              "      <th>reply_parent</th>\n",
              "      <th>location</th>\n",
              "      <th>is_verified</th>\n",
              "      <th>account_age</th>\n",
              "      <th>friend_count</th>\n",
              "      <th>follower_count</th>\n",
              "      <th>tweets_count</th>\n",
              "      <th>tweets_listed_count</th>\n",
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              "      <td>sosyal hizmetin temelini çocuk oluşturur,çocuğ...</td>\n",
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              "      <td>49</td>\n",
              "      <td>98</td>\n",
              "      <td>920963718103650304</td>\n",
              "      <td>maviruh_</td>\n",
              "      <td>shu/\\nburaya afilli bir söz yazdığımı varsayın</td>\n",
              "      <td>0</td>\n",
              "      <td>147</td>\n",
              "      <td>1</td>\n",
              "      <td>1.897887</td>\n",
              "      <td>1.373243</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "      <td>ankara</td>\n",
              "      <td>False</td>\n",
              "      <td>2017-10-19 10:43:55+00:00</td>\n",
              "      <td>539</td>\n",
              "      <td>284</td>\n",
              "      <td>2638</td>\n",
              "      <td>0</td>\n",
              "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1588452263047069697</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "      <td>@mahirunal gavur i̇zmir ya onlar, hani cumhuri...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>595514060</td>\n",
              "      <td>mtfdan</td>\n",
              "      <td></td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4.496183</td>\n",
              "      <td>1.709619</td>\n",
              "      <td>1</td>\n",
              "      <td>mahirunal</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>2012-05-31 14:08:26+00:00</td>\n",
              "      <td>589</td>\n",
              "      <td>131</td>\n",
              "      <td>6647</td>\n",
              "      <td>3</td>\n",
              "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1569589330544398336</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "      <td>#şehitadayıuzmçvşakadro\\nsiz i̇stesenizde iste...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1356375754561490947</td>\n",
              "      <td>ahsucilginuzman</td>\n",
              "      <td>Vatan Sevdalisi</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.966667</td>\n",
              "      <td>4.061111</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>2021-02-01 22:56:06+00:00</td>\n",
              "      <td>118</td>\n",
              "      <td>60</td>\n",
              "      <td>2924</td>\n",
              "      <td>0</td>\n",
              "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1570428119609139201</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "      <td>@ajans_muhbir siz kaypak olmayıp onay vermesey...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1478775431008595968</td>\n",
              "      <td>hamitelkelle</td>\n",
              "      <td>HighOne</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>34.500000</td>\n",
              "      <td>4.667539</td>\n",
              "      <td>1</td>\n",
              "      <td>ajans_muhbir</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>2022-01-05 17:08:49+00:00</td>\n",
              "      <td>69</td>\n",
              "      <td>2</td>\n",
              "      <td>1783</td>\n",
              "      <td>0</td>\n",
              "      <td>&lt;a href=\"https://mobile.twitter.com\" rel=\"nofo...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1551163840368414722</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "      <td>engelli öğretmenler olarak önümüzdeki engeller...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1511976696337113088</td>\n",
              "      <td>sed58417690</td>\n",
              "      <td></td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>1.394958</td>\n",
              "      <td>26.065517</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>2022-04-07 07:58:42+00:00</td>\n",
              "      <td>166</td>\n",
              "      <td>119</td>\n",
              "      <td>7559</td>\n",
              "      <td>0</td>\n",
              "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              tweet_id  is_retweet retweeted_username  \\\n",
              "0  1588568792984346624           0               None   \n",
              "1  1588452263047069697           0               None   \n",
              "2  1569589330544398336           0               None   \n",
              "3  1570428119609139201           0               None   \n",
              "4  1551163840368414722           0               None   \n",
              "\n",
              "                                                text  num_mentions  \\\n",
              "0  sosyal hizmetin temelini çocuk oluşturur,çocuğ...             0   \n",
              "1  @mahirunal gavur i̇zmir ya onlar, hani cumhuri...             1   \n",
              "2  #şehitadayıuzmçvşakadro\\nsiz i̇stesenizde iste...             0   \n",
              "3  @ajans_muhbir siz kaypak olmayıp onay vermesey...             1   \n",
              "4  engelli öğretmenler olarak önümüzdeki engeller...             0   \n",
              "\n",
              "   num_hashtags  num_retweets  num_favorites              user_id  \\\n",
              "0             0            49             98   920963718103650304   \n",
              "1             0             0              0            595514060   \n",
              "2             1             0              0  1356375754561490947   \n",
              "3             0             0              0  1478775431008595968   \n",
              "4             0             0              0  1511976696337113088   \n",
              "\n",
              "  user_screen_name                                user_description  \\\n",
              "0         maviruh_  shu/\\nburaya afilli bir söz yazdığımı varsayın   \n",
              "1           mtfdan                                                   \n",
              "2  ahsucilginuzman                                 Vatan Sevdalisi   \n",
              "3     hamitelkelle                                         HighOne   \n",
              "4      sed58417690                                                   \n",
              "\n",
              "   num_political_entities  total_interactions  total_special_char_in_username  \\\n",
              "0                       0                 147                               1   \n",
              "1                       1                   0                               0   \n",
              "2                       0                   0                               0   \n",
              "3                       0                   0                               0   \n",
              "4                       0                   0                               8   \n",
              "\n",
              "   friend_ratio  account_activeness  is_reply  reply_parent location  \\\n",
              "0      1.897887            1.373243         0          None   ankara   \n",
              "1      4.496183            1.709619         1     mahirunal    False   \n",
              "2      1.966667            4.061111         0          None    False   \n",
              "3     34.500000            4.667539         1  ajans_muhbir    False   \n",
              "4      1.394958           26.065517         0          None    False   \n",
              "\n",
              "   is_verified               account_age  friend_count  follower_count  \\\n",
              "0        False 2017-10-19 10:43:55+00:00           539             284   \n",
              "1        False 2012-05-31 14:08:26+00:00           589             131   \n",
              "2        False 2021-02-01 22:56:06+00:00           118              60   \n",
              "3        False 2022-01-05 17:08:49+00:00            69               2   \n",
              "4        False 2022-04-07 07:58:42+00:00           166             119   \n",
              "\n",
              "   tweets_count  tweets_listed_count  \\\n",
              "0          2638                    0   \n",
              "1          6647                    3   \n",
              "2          2924                    0   \n",
              "3          1783                    0   \n",
              "4          7559                    0   \n",
              "\n",
              "                                        tweet_source  \n",
              "0  <a href=\"http://twitter.com/download/android\" ...  \n",
              "1  <a href=\"http://twitter.com/download/android\" ...  \n",
              "2  <a href=\"http://twitter.com/download/iphone\" r...  \n",
              "3  <a href=\"https://mobile.twitter.com\" rel=\"nofo...  \n",
              "4  <a href=\"http://twitter.com/download/android\" ...  "
            ]
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dfPolitical = pd.DataFrame(dfPolitical)\n",
        "dfPolitical.head()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "process account_activeness to convert it into 0-1 range"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\necagiltay\\AppData\\Local\\Temp\\ipykernel_24144\\2692078383.py:7: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  dfPolitical['account_activeness'][i] = (dfPolitical['account_activeness'][i] - min) / (max - min)\n"
          ]
        }
      ],
      "source": [
        "#option 1: take it as absolute and use (value-min)/(max-min). This runs the risk of suffering from extreme outliers\n",
        "#option 2: use bell-curve and take the ~98% range or the data from that curve: max = dfPolitical.mean(account_activeness) + (2*dfPolitical.std(account_activeness))\n",
        "max = dfPolitical['account_activeness'].max()\n",
        "min = 0\n",
        "\n",
        "for i in range(dfPolitical.shape[0]):#couldn't be bothered to do curve...\n",
        "    dfPolitical['account_activeness'][i] = (dfPolitical['account_activeness'][i] - min) / (max - min)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dsTCGY0WFwZd"
      },
      "source": [
        "## 1.2. From Users"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fkYQc2eHFwZd"
      },
      "source": [
        "### 1.2.1. Get user metadata from user_profiles.jsons.gz"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y7sE1oU0FwZf"
      },
      "source": [
        "#### 1.2.1.1. Get user info metadata"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T19:51:57.726354Z",
          "start_time": "2022-12-29T19:51:57.697428Z"
        },
        "collapsed": true,
        "id": "jL5EV22SFwZg"
      },
      "outputs": [],
      "source": [
        "def get_user_info_metadata(user_metadata_line):\n",
        "    user_id = user_metadata_line['id_str']\n",
        "    user_name = user_metadata_line['name']\n",
        "    user_screen_name = user_metadata_line['screen_name'].lower()\n",
        "    user_location = user_metadata_line['location']\n",
        "    user_description = user_metadata_line['description']\n",
        "    user_followers_count = user_metadata_line['followers_count']\n",
        "    user_friends_count = user_metadata_line['friends_count']\n",
        "    \n",
        "    favourites_count = user_metadata_line['favourites_count']\n",
        "    geo_enabled = user_metadata_line['geo_enabled']\n",
        "    verified = user_metadata_line['verified']\n",
        "    statuses_count = user_metadata_line['statuses_count']\n",
        "    protected = user_metadata_line['protected']\n",
        "    listed_count = user_metadata_line['listed_count']\n",
        "    \n",
        "    dictionary = {'user_id':user_id, 'user_name': user_name, 'user_screen_name':user_screen_name,\n",
        "        'user_location':user_location, 'user_description':user_description, 'user_followers_count':user_followers_count, 'user_friends_count':user_friends_count,\n",
        "        \n",
        "        'favourites_count':favourites_count, 'geo_enabled':geo_enabled,\n",
        "        'verified':verified, 'statuses_count':statuses_count, 'protected':protected, 'listed_count':listed_count\n",
        "        }\n",
        "\n",
        "    return dictionary"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "n66_ycr_Nxgr"
      },
      "outputs": [],
      "source": [
        "def get_age_user(tweet_metadata_line):\n",
        "    str_age = tweet_metadata_line['created_at'] # ex. \"Tue Sep 06 13:22:17 +0000 2020\",\n",
        "    age = datetime.strptime(str_age, '%a %b %d %H:%M:%S %z %Y') #date formats: https://docs.python.org/3/library/datetime.html#strftime-and-strptime-format-codes\n",
        "\n",
        "    return age"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JkrDkSy-FwZg"
      },
      "source": [
        "#### 1.2.1.2. Get followers/(followers+friends) ratio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T19:51:59.451761Z",
          "start_time": "2022-12-29T19:51:59.427527Z"
        },
        "collapsed": true,
        "id": "fG9IcWLtFwZh"
      },
      "outputs": [],
      "source": [
        "def get_followers_all_ratio(user_followers_count, user_friends_count):\n",
        "    \n",
        "    if user_friends_count + user_followers_count == 0:\n",
        "        followers_all_ratio = 0\n",
        "\n",
        "    else:\n",
        "        followers_all_ratio =  user_followers_count / (user_friends_count + user_followers_count)\n",
        "\n",
        "    return followers_all_ratio"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y_MgVWFkFwZi"
      },
      "source": [
        "#### 1.2.1.3. Get description length"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T19:52:00.728176Z",
          "start_time": "2022-12-29T19:52:00.707177Z"
        },
        "collapsed": true,
        "id": "heXXzwy8FwZi"
      },
      "outputs": [],
      "source": [
        "def get_desc_len(user_description):\n",
        "    \n",
        "    description_len = len(user_description)\n",
        "\n",
        "    return description_len"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T19:52:04.792897Z",
          "start_time": "2022-12-29T19:52:02.798985Z"
        },
        "collapsed": true,
        "id": "MKJQ89jDFwZm",
        "scrolled": true
      },
      "outputs": [],
      "source": [
        "dfBot = {'user_id':[],\n",
        "         'user_name':[],\n",
        "         'user_screen_name':[],\n",
        "         'user_location':[],\n",
        "         'user_description':[],\n",
        "         'user_followers_count':[],\n",
        "         'user_friends_count':[],\n",
        "         'favourites_count':[],\n",
        "         'geo_enabled':[],\n",
        "         'verified':[],\n",
        "         'statuses_count':[],\n",
        "         'protected':[],\n",
        "         'listed_count':[],\n",
        "         \n",
        "         'description_len':[],\n",
        "         'followers_to_all_ratio':[],\n",
        "         'account_age':[],\n",
        "         'account_activeness':[],\n",
        "         'UsernameSpecialCharacterCount':[]}\n",
        "\n",
        "with gzip.open(f\"{PATH_TO_DOWNLOADED}user_profiles.jsons.gz\", \"rb\") as f:\n",
        "    for line in f:\n",
        "        line = json.loads(line)\n",
        "\n",
        "        dictionary = get_user_info_metadata(line)\n",
        "        for k,v in dictionary.items():\n",
        "            dfBot[k].append(v)\n",
        "        account_age = get_age_user(line)\n",
        "\n",
        "        dfBot['account_age'].append(account_age)\n",
        "        \n",
        "        # manually crafted data:\n",
        "        description_len = get_desc_len(dictionary['user_description'])\n",
        "        followers_all_ratio = get_followers_all_ratio(dictionary['user_followers_count'],\n",
        "                                                      dictionary['user_friends_count'])\n",
        "        account_activeness = TweetsPerDay(dictionary['statuses_count'], account_age)\n",
        "        specialCharacters = UsernameSpecialCharacterCount(dictionary['user_screen_name'])\n",
        "        \n",
        "        dfBot['description_len'].append(description_len)\n",
        "        dfBot['followers_to_all_ratio'].append(followers_all_ratio)\n",
        "        dfBot['account_activeness'].append(account_activeness)\n",
        "        dfBot['UsernameSpecialCharacterCount'].append(specialCharacters)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T19:52:04.868449Z",
          "start_time": "2022-12-29T19:52:04.795396Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 693
        },
        "id": "aczXlfebFwZm",
        "outputId": "4c1b2529-97cf-4c7d-a31c-01df862b5490",
        "scrolled": true
      },
      "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",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user_id</th>\n",
              "      <th>user_name</th>\n",
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              "      <th>verified</th>\n",
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              "      <th>protected</th>\n",
              "      <th>listed_count</th>\n",
              "      <th>description_len</th>\n",
              "      <th>followers_to_all_ratio</th>\n",
              "      <th>account_age</th>\n",
              "      <th>account_activeness</th>\n",
              "      <th>UsernameSpecialCharacterCount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1431241870848450577</td>\n",
              "      <td>Nasreena Khan Wazir</td>\n",
              "      <td>nasreenakhan006</td>\n",
              "      <td>Islamabad, Pakistan</td>\n",
              "      <td>Student</td>\n",
              "      <td>65</td>\n",
              "      <td>185</td>\n",
              "      <td>17676</td>\n",
              "      <td>True</td>\n",
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              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0.260000</td>\n",
              "      <td>2021-08-27 13:07:30+00:00</td>\n",
              "      <td>4.972710</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1304340303080386560</td>\n",
              "      <td>fania :((((</td>\n",
              "      <td>scorpiehoez</td>\n",
              "      <td>bogor</td>\n",
              "      <td>have a holly jolly🎄</td>\n",
              "      <td>8235</td>\n",
              "      <td>3011</td>\n",
              "      <td>15474</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>42771</td>\n",
              "      <td>False</td>\n",
              "      <td>11</td>\n",
              "      <td>19</td>\n",
              "      <td>0.732260</td>\n",
              "      <td>2020-09-11 08:45:44+00:00</td>\n",
              "      <td>49.560834</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1116042038577958914</td>\n",
              "      <td>Yusuf Aksoy</td>\n",
              "      <td>yusufak63712920</td>\n",
              "      <td></td>\n",
              "      <td>Bir şeyden pişmanlık duymak istemiyorsan,her ş...</td>\n",
              "      <td>95</td>\n",
              "      <td>399</td>\n",
              "      <td>18220</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>14300</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>64</td>\n",
              "      <td>0.192308</td>\n",
              "      <td>2019-04-10 18:15:31+00:00</td>\n",
              "      <td>10.339841</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4859899931</td>\n",
              "      <td>Be (VIXX6) ama oppalarının düğününe gidemiyor</td>\n",
              "      <td>nedenburdaysam</td>\n",
              "      <td>Hufflepuff ortak salon</td>\n",
              "      <td>SMStan\\n/St☆rlight ///come on girls,this is ou...</td>\n",
              "      <td>40</td>\n",
              "      <td>83</td>\n",
              "      <td>26999</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>21303</td>\n",
              "      <td>False</td>\n",
              "      <td>5</td>\n",
              "      <td>65</td>\n",
              "      <td>0.325203</td>\n",
              "      <td>2016-01-29 11:01:25+00:00</td>\n",
              "      <td>8.354118</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2225373636</td>\n",
              "      <td>SLMDMR</td>\n",
              "      <td>biologselim</td>\n",
              "      <td></td>\n",
              "      <td>BİYOLOG🔬🦠\\nNanoteknoloji</td>\n",
              "      <td>100</td>\n",
              "      <td>98</td>\n",
              "      <td>2179</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>1629</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>23</td>\n",
              "      <td>0.505051</td>\n",
              "      <td>2013-12-01 18:16:41+00:00</td>\n",
              "      <td>0.487871</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29665</th>\n",
              "      <td>1320834618220781569</td>\n",
              "      <td>islammm</td>\n",
              "      <td>islam_mrsj</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>229</td>\n",
              "      <td>217</td>\n",
              "      <td>10820</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>2396</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.513453</td>\n",
              "      <td>2020-10-26 21:08:22+00:00</td>\n",
              "      <td>2.929095</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29666</th>\n",
              "      <td>111074128</td>\n",
              "      <td>Melda Onur</td>\n",
              "      <td>meldaonur</td>\n",
              "      <td></td>\n",
              "      <td>Şekersiz çay, etsiz sofra, SAVAŞSIZ dünya... 🐌...</td>\n",
              "      <td>212457</td>\n",
              "      <td>5428</td>\n",
              "      <td>36671</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>75178</td>\n",
              "      <td>False</td>\n",
              "      <td>279</td>\n",
              "      <td>100</td>\n",
              "      <td>0.975088</td>\n",
              "      <td>2010-02-03 18:39:01+00:00</td>\n",
              "      <td>15.873733</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29667</th>\n",
              "      <td>36946875</td>\n",
              "      <td>ali ydm</td>\n",
              "      <td>ali_ydm</td>\n",
              "      <td>İstanbul, Türkiye</td>\n",
              "      <td>hayat oyunsa bende jeton çok</td>\n",
              "      <td>116</td>\n",
              "      <td>141</td>\n",
              "      <td>7389</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>6482</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
              "      <td>0.451362</td>\n",
              "      <td>2009-05-01 13:56:23+00:00</td>\n",
              "      <td>1.292780</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29668</th>\n",
              "      <td>2389587396</td>\n",
              "      <td>Türkan Usta</td>\n",
              "      <td>turkanusta</td>\n",
              "      <td>Ankara, Türkiye</td>\n",
              "      <td>Ustaya sormuşlar; hayatta yaptığın en büyük is...</td>\n",
              "      <td>1669</td>\n",
              "      <td>3639</td>\n",
              "      <td>140095</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>121113</td>\n",
              "      <td>False</td>\n",
              "      <td>1</td>\n",
              "      <td>116</td>\n",
              "      <td>0.314431</td>\n",
              "      <td>2014-03-14 18:05:09+00:00</td>\n",
              "      <td>37.426761</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29669</th>\n",
              "      <td>1575904134</td>\n",
              "      <td>ARAZ</td>\n",
              "      <td>ben121975</td>\n",
              "      <td></td>\n",
              "      <td>Korkak gerçek le yüzleşmeyi redd eder,hırçınla...</td>\n",
              "      <td>6026</td>\n",
              "      <td>5625</td>\n",
              "      <td>39605</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>7120</td>\n",
              "      <td>False</td>\n",
              "      <td>4</td>\n",
              "      <td>158</td>\n",
              "      <td>0.517209</td>\n",
              "      <td>2013-07-07 19:57:01+00:00</td>\n",
              "      <td>2.042456</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>29670 rows × 18 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                   user_id                                      user_name  \\\n",
              "0      1431241870848450577                            Nasreena Khan Wazir   \n",
              "1      1304340303080386560                                    fania :((((   \n",
              "2      1116042038577958914                                    Yusuf Aksoy   \n",
              "3               4859899931  Be (VIXX6) ama oppalarının düğününe gidemiyor   \n",
              "4               2225373636                                         SLMDMR   \n",
              "...                    ...                                            ...   \n",
              "29665  1320834618220781569                                        islammm   \n",
              "29666            111074128                                     Melda Onur   \n",
              "29667             36946875                                        ali ydm   \n",
              "29668           2389587396                                    Türkan Usta   \n",
              "29669           1575904134                                           ARAZ   \n",
              "\n",
              "      user_screen_name            user_location  \\\n",
              "0      nasreenakhan006      Islamabad, Pakistan   \n",
              "1          scorpiehoez                   bogor    \n",
              "2      yusufak63712920                            \n",
              "3       nedenburdaysam  Hufflepuff ortak salon    \n",
              "4          biologselim                            \n",
              "...                ...                      ...   \n",
              "29665       islam_mrsj                            \n",
              "29666        meldaonur                            \n",
              "29667          ali_ydm        İstanbul, Türkiye   \n",
              "29668       turkanusta          Ankara, Türkiye   \n",
              "29669        ben121975                            \n",
              "\n",
              "                                        user_description  \\\n",
              "0                                                Student   \n",
              "1                                    have a holly jolly🎄   \n",
              "2      Bir şeyden pişmanlık duymak istemiyorsan,her ş...   \n",
              "3      SMStan\\n/St☆rlight ///come on girls,this is ou...   \n",
              "4                               BİYOLOG🔬🦠\\nNanoteknoloji   \n",
              "...                                                  ...   \n",
              "29665                                                      \n",
              "29666  Şekersiz çay, etsiz sofra, SAVAŞSIZ dünya... 🐌...   \n",
              "29667                       hayat oyunsa bende jeton çok   \n",
              "29668  Ustaya sormuşlar; hayatta yaptığın en büyük is...   \n",
              "29669  Korkak gerçek le yüzleşmeyi redd eder,hırçınla...   \n",
              "\n",
              "       user_followers_count  user_friends_count  favourites_count  \\\n",
              "0                        65                 185             17676   \n",
              "1                      8235                3011             15474   \n",
              "2                        95                 399             18220   \n",
              "3                        40                  83             26999   \n",
              "4                       100                  98              2179   \n",
              "...                     ...                 ...               ...   \n",
              "29665                   229                 217             10820   \n",
              "29666                212457                5428             36671   \n",
              "29667                   116                 141              7389   \n",
              "29668                  1669                3639            140095   \n",
              "29669                  6026                5625             39605   \n",
              "\n",
              "       geo_enabled  verified  statuses_count  protected  listed_count  \\\n",
              "0             True     False            2551      False             0   \n",
              "1             True     False           42771      False            11   \n",
              "2            False     False           14300      False             0   \n",
              "3            False     False           21303      False             5   \n",
              "4            False     False            1629      False             0   \n",
              "...            ...       ...             ...        ...           ...   \n",
              "29665        False     False            2396      False             0   \n",
              "29666         True     False           75178      False           279   \n",
              "29667        False     False            6482      False             0   \n",
              "29668         True     False          121113      False             1   \n",
              "29669         True     False            7120      False             4   \n",
              "\n",
              "       description_len  followers_to_all_ratio               account_age  \\\n",
              "0                    7                0.260000 2021-08-27 13:07:30+00:00   \n",
              "1                   19                0.732260 2020-09-11 08:45:44+00:00   \n",
              "2                   64                0.192308 2019-04-10 18:15:31+00:00   \n",
              "3                   65                0.325203 2016-01-29 11:01:25+00:00   \n",
              "4                   23                0.505051 2013-12-01 18:16:41+00:00   \n",
              "...                ...                     ...                       ...   \n",
              "29665                0                0.513453 2020-10-26 21:08:22+00:00   \n",
              "29666              100                0.975088 2010-02-03 18:39:01+00:00   \n",
              "29667               28                0.451362 2009-05-01 13:56:23+00:00   \n",
              "29668              116                0.314431 2014-03-14 18:05:09+00:00   \n",
              "29669              158                0.517209 2013-07-07 19:57:01+00:00   \n",
              "\n",
              "       account_activeness  UsernameSpecialCharacterCount  \n",
              "0                4.972710                              3  \n",
              "1               49.560834                              0  \n",
              "2               10.339841                              8  \n",
              "3                8.354118                              0  \n",
              "4                0.487871                              0  \n",
              "...                   ...                            ...  \n",
              "29665            2.929095                              1  \n",
              "29666           15.873733                              0  \n",
              "29667            1.292780                              1  \n",
              "29668           37.426761                              0  \n",
              "29669            2.042456                              6  \n",
              "\n",
              "[29670 rows x 18 columns]"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dfBot = pd.DataFrame(dfBot)\n",
        "dfBot"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GLMXQaO2FwZo"
      },
      "source": [
        "### 1.2.2. Get Tweet Info of Users in user_profiles.jsons.gz"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rp54Y5pNFwZp"
      },
      "source": [
        "#### 1.2.2.1. Check ratio of retweets to all tweets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 116,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T19:52:07.323429Z",
          "start_time": "2022-12-29T19:52:07.295798Z"
        },
        "collapsed": true,
        "id": "PWb7ylnyFwZp"
      },
      "outputs": [],
      "source": [
        "def get_retweet_tweet_ratio(line):\n",
        "    number_retweets = 0\n",
        "    number_original_tweets = 0\n",
        "\n",
        "    for tweet in line['tweets']:\n",
        "        try:\n",
        "            tweet['retweeted_status']\n",
        "            number_retweets += 1\n",
        "                \n",
        "        except:\n",
        "            number_original_tweets += 1\n",
        "            \n",
        "    total_tweets = number_retweets + number_original_tweets\n",
        "    \n",
        "    if total_tweets == 0:\n",
        "        retweet_total_ratio = None\n",
        "    else:\n",
        "        retweet_total_ratio = number_retweets/(total_tweets)\n",
        "    \n",
        "    return retweet_total_ratio"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yjt-wdA4FwZq"
      },
      "source": [
        "#### 1.2.2.2. Check median number of favorites"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 117,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T19:52:08.882460Z",
          "start_time": "2022-12-29T19:52:08.858018Z"
        },
        "collapsed": true,
        "id": "t3YA8ZOPFwZq"
      },
      "outputs": [],
      "source": [
        "def get_median_number_favorites(line):\n",
        "    num_median_favorites = np.median([tweet['favorite_count'] for tweet in line['tweets']])\n",
        "\n",
        "    return num_median_favorites"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sAYLmWI6FwZr"
      },
      "source": [
        "### 1.2.3. Collect data using the functions above and transform into a Pandas DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 118,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T19:58:51.844652Z",
          "start_time": "2022-12-29T19:52:10.806040Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vAYQ7dHLFwZr",
        "outputId": "3663e389-8072-4283-880b-b8371bc3d578",
        "scrolled": false
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "100.0 %. Processed 28300 lines\n"
          ]
        }
      ],
      "source": [
        "dfBotTweets = {'user_id':[],\n",
        "               'retweet_total_ratio':[],\n",
        "               'num_median_favorites':[],\n",
        "               'num_of_tweets':[]\n",
        "              }\n",
        "\n",
        "i = 0\n",
        "\n",
        "with gzip.open(f\"{PATH_TO_DOWNLOADED}user_tweets.jsons.gz\", \"rb\") as f:\n",
        "    for line in f:\n",
        "\n",
        "        line = json.loads(line)\n",
        "\n",
        "        user_id = line['user_id']\n",
        "        dfBotTweets['user_id'].append(user_id)\n",
        "        \n",
        "        retweet_total_ratio = get_retweet_tweet_ratio(line)\n",
        "        dfBotTweets['retweet_total_ratio'].append(retweet_total_ratio)\n",
        "        \n",
        "        num_median_favorites = get_median_number_favorites(line)\n",
        "        dfBotTweets['num_median_favorites'].append(num_median_favorites)\n",
        "        \n",
        "        dfBotTweets['num_of_tweets'].append(len(line['tweets']))\n",
        "\n",
        "        i += 1\n",
        "        if i % 100 == 0:\n",
        "            clear_output(wait=True)\n",
        "            print(i/283, \"%. Processed\", i , \"lines\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 119,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T19:58:51.997352Z",
          "start_time": "2022-12-29T19:58:51.847603Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "PSKLV63vFwZt",
        "outputId": "4f333521-5186-44bc-b668-ef693c218ca8"
      },
      "outputs": [
        {
          "data": {
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              "      <th>retweet_total_ratio</th>\n",
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              "      <td>1475272459616235525</td>\n",
              "      <td>0.825000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28312</th>\n",
              "      <td>1096753792731750401</td>\n",
              "      <td>0.051020</td>\n",
              "      <td>1.0</td>\n",
              "      <td>196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28313</th>\n",
              "      <td>1269527617687953409</td>\n",
              "      <td>0.095000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28314</th>\n",
              "      <td>1483177252271570944</td>\n",
              "      <td>0.630000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>28315 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                   user_id  retweet_total_ratio  num_median_favorites  \\\n",
              "0                594642154             0.115000                   2.0   \n",
              "1                525600289             0.005025                   1.0   \n",
              "2       931895965501534209             0.900000                   0.0   \n",
              "3      1591543462746329088             0.185000                   0.0   \n",
              "4       734801354749796352             1.000000                   0.0   \n",
              "...                    ...                  ...                   ...   \n",
              "28310  1591370361488252928             0.800000                   0.0   \n",
              "28311  1475272459616235525             0.825000                   0.0   \n",
              "28312  1096753792731750401             0.051020                   1.0   \n",
              "28313  1269527617687953409             0.095000                   2.0   \n",
              "28314  1483177252271570944             0.630000                   0.0   \n",
              "\n",
              "       num_of_tweets  \n",
              "0                200  \n",
              "1                199  \n",
              "2                200  \n",
              "3                200  \n",
              "4                200  \n",
              "...              ...  \n",
              "28310            200  \n",
              "28311            200  \n",
              "28312            196  \n",
              "28313            200  \n",
              "28314            200  \n",
              "\n",
              "[28315 rows x 4 columns]"
            ]
          },
          "execution_count": 119,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dfBotTweets = pd.DataFrame(dfBotTweets)\n",
        "dfBotTweets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "msvBWp57FwZu"
      },
      "source": [
        "### 1.2.3. Merge dfBot and dfBotTweets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 120,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T19:58:52.117992Z",
          "start_time": "2022-12-29T19:58:52.004064Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 783
        },
        "id": "I_8tqxBxFwZu",
        "outputId": "9c688757-84cf-47bf-f1cd-953283ab74a0",
        "scrolled": true
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user_id</th>\n",
              "      <th>user_name</th>\n",
              "      <th>user_screen_name</th>\n",
              "      <th>user_location</th>\n",
              "      <th>user_description</th>\n",
              "      <th>user_followers_count</th>\n",
              "      <th>user_friends_count</th>\n",
              "      <th>favourites_count</th>\n",
              "      <th>geo_enabled</th>\n",
              "      <th>verified</th>\n",
              "      <th>statuses_count</th>\n",
              "      <th>protected</th>\n",
              "      <th>listed_count</th>\n",
              "      <th>description_len</th>\n",
              "      <th>followers_to_all_ratio</th>\n",
              "      <th>account_age</th>\n",
              "      <th>account_activeness</th>\n",
              "      <th>UsernameSpecialCharacterCount</th>\n",
              "      <th>retweet_total_ratio</th>\n",
              "      <th>num_median_favorites</th>\n",
              "      <th>num_of_tweets</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1431241870848450577</td>\n",
              "      <td>Nasreena Khan Wazir</td>\n",
              "      <td>nasreenakhan006</td>\n",
              "      <td>Islamabad, Pakistan</td>\n",
              "      <td>Student</td>\n",
              "      <td>65</td>\n",
              "      <td>185</td>\n",
              "      <td>17676</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>2551</td>\n",
              "      <td>False</td>\n",
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              "      <td>7</td>\n",
              "      <td>0.260000</td>\n",
              "      <td>2021-08-27 13:07:30+00:00</td>\n",
              "      <td>4.992172</td>\n",
              "      <td>3</td>\n",
              "      <td>0.395939</td>\n",
              "      <td>0.0</td>\n",
              "      <td>197.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1304340303080386560</td>\n",
              "      <td>fania :((((</td>\n",
              "      <td>scorpiehoez</td>\n",
              "      <td>bogor</td>\n",
              "      <td>have a holly jolly🎄</td>\n",
              "      <td>8235</td>\n",
              "      <td>3011</td>\n",
              "      <td>15474</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>42771</td>\n",
              "      <td>False</td>\n",
              "      <td>11</td>\n",
              "      <td>19</td>\n",
              "      <td>0.732260</td>\n",
              "      <td>2020-09-11 08:45:44+00:00</td>\n",
              "      <td>49.675958</td>\n",
              "      <td>0</td>\n",
              "      <td>0.125000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1116042038577958914</td>\n",
              "      <td>Yusuf Aksoy</td>\n",
              "      <td>yusufak63712920</td>\n",
              "      <td></td>\n",
              "      <td>Bir şeyden pişmanlık duymak istemiyorsan,her ş...</td>\n",
              "      <td>95</td>\n",
              "      <td>399</td>\n",
              "      <td>18220</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>14300</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>64</td>\n",
              "      <td>0.192308</td>\n",
              "      <td>2019-04-10 18:15:31+00:00</td>\n",
              "      <td>10.354815</td>\n",
              "      <td>8</td>\n",
              "      <td>0.910000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4859899931</td>\n",
              "      <td>Be (VIXX6) ama oppalarının düğününe gidemiyor</td>\n",
              "      <td>nedenburdaysam</td>\n",
              "      <td>Hufflepuff ortak salon</td>\n",
              "      <td>SMStan\\n/St☆rlight ///come on girls,this is ou...</td>\n",
              "      <td>40</td>\n",
              "      <td>83</td>\n",
              "      <td>26999</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>21303</td>\n",
              "      <td>False</td>\n",
              "      <td>5</td>\n",
              "      <td>65</td>\n",
              "      <td>0.325203</td>\n",
              "      <td>2016-01-29 11:01:25+00:00</td>\n",
              "      <td>8.360675</td>\n",
              "      <td>0</td>\n",
              "      <td>0.015306</td>\n",
              "      <td>1.0</td>\n",
              "      <td>196.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2225373636</td>\n",
              "      <td>SLMDMR</td>\n",
              "      <td>biologselim</td>\n",
              "      <td></td>\n",
              "      <td>BİYOLOG🔬🦠\\nNanoteknoloji</td>\n",
              "      <td>100</td>\n",
              "      <td>98</td>\n",
              "      <td>2179</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>1629</td>\n",
              "      <td>False</td>\n",
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              "      <td>23</td>\n",
              "      <td>0.505051</td>\n",
              "      <td>2013-12-01 18:16:41+00:00</td>\n",
              "      <td>0.488163</td>\n",
              "      <td>0</td>\n",
              "      <td>0.659898</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>29665</th>\n",
              "      <td>1320834618220781569</td>\n",
              "      <td>islammm</td>\n",
              "      <td>islam_mrsj</td>\n",
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              "      <td></td>\n",
              "      <td>229</td>\n",
              "      <td>217</td>\n",
              "      <td>10820</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>2396</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.513453</td>\n",
              "      <td>2020-10-26 21:08:22+00:00</td>\n",
              "      <td>2.939877</td>\n",
              "      <td>1</td>\n",
              "      <td>0.015000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>200.0</td>\n",
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              "    <tr>\n",
              "      <th>29666</th>\n",
              "      <td>111074128</td>\n",
              "      <td>Melda Onur</td>\n",
              "      <td>meldaonur</td>\n",
              "      <td></td>\n",
              "      <td>Şekersiz çay, etsiz sofra, SAVAŞSIZ dünya... 🐌...</td>\n",
              "      <td>212457</td>\n",
              "      <td>5428</td>\n",
              "      <td>36671</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>75178</td>\n",
              "      <td>False</td>\n",
              "      <td>279</td>\n",
              "      <td>100</td>\n",
              "      <td>0.975088</td>\n",
              "      <td>2010-02-03 18:39:01+00:00</td>\n",
              "      <td>15.880439</td>\n",
              "      <td>0</td>\n",
              "      <td>0.291457</td>\n",
              "      <td>2.0</td>\n",
              "      <td>199.0</td>\n",
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              "    <tr>\n",
              "      <th>29667</th>\n",
              "      <td>36946875</td>\n",
              "      <td>ali ydm</td>\n",
              "      <td>ali_ydm</td>\n",
              "      <td>İstanbul, Türkiye</td>\n",
              "      <td>hayat oyunsa bende jeton çok</td>\n",
              "      <td>116</td>\n",
              "      <td>141</td>\n",
              "      <td>7389</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>6482</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
              "      <td>0.451362</td>\n",
              "      <td>2009-05-01 13:56:23+00:00</td>\n",
              "      <td>1.293296</td>\n",
              "      <td>1</td>\n",
              "      <td>0.061538</td>\n",
              "      <td>0.0</td>\n",
              "      <td>195.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29668</th>\n",
              "      <td>2389587396</td>\n",
              "      <td>Türkan Usta</td>\n",
              "      <td>turkanusta</td>\n",
              "      <td>Ankara, Türkiye</td>\n",
              "      <td>Ustaya sormuşlar; hayatta yaptığın en büyük is...</td>\n",
              "      <td>1669</td>\n",
              "      <td>3639</td>\n",
              "      <td>140095</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>121113</td>\n",
              "      <td>False</td>\n",
              "      <td>1</td>\n",
              "      <td>116</td>\n",
              "      <td>0.314431</td>\n",
              "      <td>2014-03-14 18:05:09+00:00</td>\n",
              "      <td>37.449907</td>\n",
              "      <td>0</td>\n",
              "      <td>0.995000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29669</th>\n",
              "      <td>1575904134</td>\n",
              "      <td>ARAZ</td>\n",
              "      <td>ben121975</td>\n",
              "      <td></td>\n",
              "      <td>Korkak gerçek le yüzleşmeyi redd eder,hırçınla...</td>\n",
              "      <td>6026</td>\n",
              "      <td>5625</td>\n",
              "      <td>39605</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>7120</td>\n",
              "      <td>False</td>\n",
              "      <td>4</td>\n",
              "      <td>158</td>\n",
              "      <td>0.517209</td>\n",
              "      <td>2013-07-07 19:57:01+00:00</td>\n",
              "      <td>2.044215</td>\n",
              "      <td>6</td>\n",
              "      <td>0.030000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>200.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>29670 rows × 21 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                   user_id                                      user_name  \\\n",
              "0      1431241870848450577                            Nasreena Khan Wazir   \n",
              "1      1304340303080386560                                    fania :((((   \n",
              "2      1116042038577958914                                    Yusuf Aksoy   \n",
              "3               4859899931  Be (VIXX6) ama oppalarının düğününe gidemiyor   \n",
              "4               2225373636                                         SLMDMR   \n",
              "...                    ...                                            ...   \n",
              "29665  1320834618220781569                                        islammm   \n",
              "29666            111074128                                     Melda Onur   \n",
              "29667             36946875                                        ali ydm   \n",
              "29668           2389587396                                    Türkan Usta   \n",
              "29669           1575904134                                           ARAZ   \n",
              "\n",
              "      user_screen_name            user_location  \\\n",
              "0      nasreenakhan006      Islamabad, Pakistan   \n",
              "1          scorpiehoez                   bogor    \n",
              "2      yusufak63712920                            \n",
              "3       nedenburdaysam  Hufflepuff ortak salon    \n",
              "4          biologselim                            \n",
              "...                ...                      ...   \n",
              "29665       islam_mrsj                            \n",
              "29666        meldaonur                            \n",
              "29667          ali_ydm        İstanbul, Türkiye   \n",
              "29668       turkanusta          Ankara, Türkiye   \n",
              "29669        ben121975                            \n",
              "\n",
              "                                        user_description  \\\n",
              "0                                                Student   \n",
              "1                                    have a holly jolly🎄   \n",
              "2      Bir şeyden pişmanlık duymak istemiyorsan,her ş...   \n",
              "3      SMStan\\n/St☆rlight ///come on girls,this is ou...   \n",
              "4                               BİYOLOG🔬🦠\\nNanoteknoloji   \n",
              "...                                                  ...   \n",
              "29665                                                      \n",
              "29666  Şekersiz çay, etsiz sofra, SAVAŞSIZ dünya... 🐌...   \n",
              "29667                       hayat oyunsa bende jeton çok   \n",
              "29668  Ustaya sormuşlar; hayatta yaptığın en büyük is...   \n",
              "29669  Korkak gerçek le yüzleşmeyi redd eder,hırçınla...   \n",
              "\n",
              "       user_followers_count  user_friends_count  favourites_count  \\\n",
              "0                        65                 185             17676   \n",
              "1                      8235                3011             15474   \n",
              "2                        95                 399             18220   \n",
              "3                        40                  83             26999   \n",
              "4                       100                  98              2179   \n",
              "...                     ...                 ...               ...   \n",
              "29665                   229                 217             10820   \n",
              "29666                212457                5428             36671   \n",
              "29667                   116                 141              7389   \n",
              "29668                  1669                3639            140095   \n",
              "29669                  6026                5625             39605   \n",
              "\n",
              "       geo_enabled  verified  statuses_count  protected  listed_count  \\\n",
              "0             True     False            2551      False             0   \n",
              "1             True     False           42771      False            11   \n",
              "2            False     False           14300      False             0   \n",
              "3            False     False           21303      False             5   \n",
              "4            False     False            1629      False             0   \n",
              "...            ...       ...             ...        ...           ...   \n",
              "29665        False     False            2396      False             0   \n",
              "29666         True     False           75178      False           279   \n",
              "29667        False     False            6482      False             0   \n",
              "29668         True     False          121113      False             1   \n",
              "29669         True     False            7120      False             4   \n",
              "\n",
              "       description_len  followers_to_all_ratio               account_age  \\\n",
              "0                    7                0.260000 2021-08-27 13:07:30+00:00   \n",
              "1                   19                0.732260 2020-09-11 08:45:44+00:00   \n",
              "2                   64                0.192308 2019-04-10 18:15:31+00:00   \n",
              "3                   65                0.325203 2016-01-29 11:01:25+00:00   \n",
              "4                   23                0.505051 2013-12-01 18:16:41+00:00   \n",
              "...                ...                     ...                       ...   \n",
              "29665                0                0.513453 2020-10-26 21:08:22+00:00   \n",
              "29666              100                0.975088 2010-02-03 18:39:01+00:00   \n",
              "29667               28                0.451362 2009-05-01 13:56:23+00:00   \n",
              "29668              116                0.314431 2014-03-14 18:05:09+00:00   \n",
              "29669              158                0.517209 2013-07-07 19:57:01+00:00   \n",
              "\n",
              "       account_activeness  UsernameSpecialCharacterCount  retweet_total_ratio  \\\n",
              "0                4.992172                              3             0.395939   \n",
              "1               49.675958                              0             0.125000   \n",
              "2               10.354815                              8             0.910000   \n",
              "3                8.360675                              0             0.015306   \n",
              "4                0.488163                              0             0.659898   \n",
              "...                   ...                            ...                  ...   \n",
              "29665            2.939877                              1             0.015000   \n",
              "29666           15.880439                              0             0.291457   \n",
              "29667            1.293296                              1             0.061538   \n",
              "29668           37.449907                              0             0.995000   \n",
              "29669            2.044215                              6             0.030000   \n",
              "\n",
              "       num_median_favorites  num_of_tweets  \n",
              "0                       0.0          197.0  \n",
              "1                       0.0          200.0  \n",
              "2                       0.0          200.0  \n",
              "3                       1.0          196.0  \n",
              "4                       0.0          197.0  \n",
              "...                     ...            ...  \n",
              "29665                   1.0          200.0  \n",
              "29666                   2.0          199.0  \n",
              "29667                   0.0          195.0  \n",
              "29668                   0.0          200.0  \n",
              "29669                   1.0          200.0  \n",
              "\n",
              "[29670 rows x 21 columns]"
            ]
          },
          "execution_count": 120,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dfBotAll = dfBot.merge(dfBotTweets,\n",
        "                       how='left')\n",
        "\n",
        "dfBotAll[['retweet_total_ratio', 'num_median_favorites']] = dfBotAll[['retweet_total_ratio', 'num_median_favorites']].fillna(0)\n",
        "\n",
        "dfBotAll"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YLuO0gO1FwZv"
      },
      "source": [
        "# 2. TRAIN MODEL"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IvuktICyFwZv"
      },
      "source": [
        "## 2.1. Political Tweet Prediction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "blYbJJWQFwZv"
      },
      "source": [
        "### 2.1.1. Merge dfPolitical data with labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 121,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T19:59:11.293718Z",
          "start_time": "2022-12-29T19:59:11.232405Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "r9_7O4PmFwZv",
        "outputId": "cd2c9eec-c255-485a-8017-98331227fd21",
        "scrolled": true
      },
      "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>tweet_id</th>\n",
              "      <th>is_retweet</th>\n",
              "      <th>retweeted_username</th>\n",
              "      <th>text</th>\n",
              "      <th>num_mentions</th>\n",
              "      <th>num_hashtags</th>\n",
              "      <th>num_retweets</th>\n",
              "      <th>num_favorites</th>\n",
              "      <th>user_id</th>\n",
              "      <th>user_screen_name</th>\n",
              "      <th>user_description</th>\n",
              "      <th>num_political_entities</th>\n",
              "      <th>total_interactions</th>\n",
              "      <th>total_special_char_in_username</th>\n",
              "      <th>friend_ratio</th>\n",
              "      <th>account_activeness</th>\n",
              "      <th>is_reply</th>\n",
              "      <th>reply_parent</th>\n",
              "      <th>location</th>\n",
              "      <th>is_verified</th>\n",
              "      <th>account_age</th>\n",
              "      <th>friend_count</th>\n",
              "      <th>follower_count</th>\n",
              "      <th>tweets_count</th>\n",
              "      <th>tweets_listed_count</th>\n",
              "      <th>tweet_source</th>\n",
              "      <th>isPolitical</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1271</th>\n",
              "      <td>1592563280471818242</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "      <td>“ NATO Parlamenter Asamblesi Büro Yönetim Topl...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>52</td>\n",
              "      <td>2164886426</td>\n",
              "      <td>oa_bak</td>\n",
              "      <td>AK Parti Rize Milletvekili , NATO Parlamenter ...</td>\n",
              "      <td>0</td>\n",
              "      <td>60</td>\n",
              "      <td>1</td>\n",
              "      <td>0.024708</td>\n",
              "      <td>4.744731</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "      <td>rize, türkiye</td>\n",
              "      <td>True</td>\n",
              "      <td>2013-10-30 14:31:01+00:00</td>\n",
              "      <td>1807</td>\n",
              "      <td>73133</td>\n",
              "      <td>15985</td>\n",
              "      <td>119</td>\n",
              "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1645</th>\n",
              "      <td>1588162008066985984</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "      <td>@kilicdarogluk BüyüklerLiderler Affeder\\nYüzyı...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1587941807651946502</td>\n",
              "      <td>can25422934</td>\n",
              "      <td>⚖️Adil Yaşam Derneği Gönüllüsü ⚖️🌈. Ben bu dün...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>3.528239</td>\n",
              "      <td>574.076923</td>\n",
              "      <td>1</td>\n",
              "      <td>kilicdarogluk</td>\n",
              "      <td>konya, türkiye</td>\n",
              "      <td>False</td>\n",
              "      <td>2022-11-02 22:57:06+00:00</td>\n",
              "      <td>1062</td>\n",
              "      <td>301</td>\n",
              "      <td>44778</td>\n",
              "      <td>0</td>\n",
              "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2659</th>\n",
              "      <td>1567983304485519360</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "      <td>@umitozdag 1 oy benden</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1230951913476231169</td>\n",
              "      <td>muratvedaterdo1</td>\n",
              "      <td></td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>8.888889</td>\n",
              "      <td>1.962371</td>\n",
              "      <td>1</td>\n",
              "      <td>umitozdag</td>\n",
              "      <td>oporto, portugal</td>\n",
              "      <td>False</td>\n",
              "      <td>2020-02-21 20:26:39+00:00</td>\n",
              "      <td>160</td>\n",
              "      <td>18</td>\n",
              "      <td>2086</td>\n",
              "      <td>0</td>\n",
              "      <td>&lt;a href=\"https://mobile.twitter.com\" rel=\"nofo...</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1927</th>\n",
              "      <td>1594780754973986816</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "      <td>@vedatbilgn @akbasogluemin @Mustafa_Destici @e...</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1493609942821056514</td>\n",
              "      <td>vatanpe34901187</td>\n",
              "      <td></td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>0.488000</td>\n",
              "      <td>5.589971</td>\n",
              "      <td>1</td>\n",
              "      <td>vedatbilgn</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>2022-02-15 15:35:53+00:00</td>\n",
              "      <td>61</td>\n",
              "      <td>125</td>\n",
              "      <td>1895</td>\n",
              "      <td>0</td>\n",
              "      <td>&lt;a href=\"https://mobile.twitter.com\" rel=\"nofo...</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>838</th>\n",
              "      <td>1585907446215806977</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "      <td>Yalandan kim ölmüş??? https://t.co/usF9mh9Pvv</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1148257518239584257</td>\n",
              "      <td>rsgmss</td>\n",
              "      <td>İHL | Kocaeli</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.264535</td>\n",
              "      <td>10.441176</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>2019-07-08 15:48:20+00:00</td>\n",
              "      <td>1305</td>\n",
              "      <td>1032</td>\n",
              "      <td>13490</td>\n",
              "      <td>0</td>\n",
              "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 tweet_id  is_retweet retweeted_username  \\\n",
              "1271  1592563280471818242           0               None   \n",
              "1645  1588162008066985984           0               None   \n",
              "2659  1567983304485519360           0               None   \n",
              "1927  1594780754973986816           0               None   \n",
              "838   1585907446215806977           0               None   \n",
              "\n",
              "                                                   text  num_mentions  \\\n",
              "1271  “ NATO Parlamenter Asamblesi Büro Yönetim Topl...             0   \n",
              "1645  @kilicdarogluk BüyüklerLiderler Affeder\\nYüzyı...             1   \n",
              "2659                             @umitozdag 1 oy benden             1   \n",
              "1927  @vedatbilgn @akbasogluemin @Mustafa_Destici @e...             7   \n",
              "838       Yalandan kim ölmüş??? https://t.co/usF9mh9Pvv             0   \n",
              "\n",
              "      num_hashtags  num_retweets  num_favorites              user_id  \\\n",
              "1271             0             8             52           2164886426   \n",
              "1645             0             0              0  1587941807651946502   \n",
              "2659             0             0              3  1230951913476231169   \n",
              "1927             1             0              0  1493609942821056514   \n",
              "838              0             1              0  1148257518239584257   \n",
              "\n",
              "     user_screen_name                                   user_description  \\\n",
              "1271           oa_bak  AK Parti Rize Milletvekili , NATO Parlamenter ...   \n",
              "1645      can25422934  ⚖️Adil Yaşam Derneği Gönüllüsü ⚖️🌈. Ben bu dün...   \n",
              "2659  muratvedaterdo1                                                      \n",
              "1927  vatanpe34901187                                                      \n",
              "838            rsgmss                                      İHL | Kocaeli   \n",
              "\n",
              "      num_political_entities  total_interactions  \\\n",
              "1271                       0                  60   \n",
              "1645                       1                   0   \n",
              "2659                       1                   3   \n",
              "1927                       4                   0   \n",
              "838                        0                   1   \n",
              "\n",
              "      total_special_char_in_username  friend_ratio  account_activeness  \\\n",
              "1271                               1      0.024708            4.744731   \n",
              "1645                               8      3.528239          574.076923   \n",
              "2659                               1      8.888889            1.962371   \n",
              "1927                               8      0.488000            5.589971   \n",
              "838                                0      1.264535           10.441176   \n",
              "\n",
              "      is_reply   reply_parent          location  is_verified  \\\n",
              "1271         0           None     rize, türkiye         True   \n",
              "1645         1  kilicdarogluk    konya, türkiye        False   \n",
              "2659         1      umitozdag  oporto, portugal        False   \n",
              "1927         1     vedatbilgn             False        False   \n",
              "838          0           None             False        False   \n",
              "\n",
              "                   account_age  friend_count  follower_count  tweets_count  \\\n",
              "1271 2013-10-30 14:31:01+00:00          1807           73133         15985   \n",
              "1645 2022-11-02 22:57:06+00:00          1062             301         44778   \n",
              "2659 2020-02-21 20:26:39+00:00           160              18          2086   \n",
              "1927 2022-02-15 15:35:53+00:00            61             125          1895   \n",
              "838  2019-07-08 15:48:20+00:00          1305            1032         13490   \n",
              "\n",
              "      tweets_listed_count                                       tweet_source  \\\n",
              "1271                  119  <a href=\"http://twitter.com/download/iphone\" r...   \n",
              "1645                    0  <a href=\"http://twitter.com/download/iphone\" r...   \n",
              "2659                    0  <a href=\"https://mobile.twitter.com\" rel=\"nofo...   \n",
              "1927                    0  <a href=\"https://mobile.twitter.com\" rel=\"nofo...   \n",
              "838                     0  <a href=\"http://twitter.com/download/android\" ...   \n",
              "\n",
              "     isPolitical  \n",
              "1271         Yes  \n",
              "1645         Yes  \n",
              "2659         Yes  \n",
              "1927         Yes  \n",
              "838          Yes  "
            ]
          },
          "execution_count": 121,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dfPoliticalAll_train = dfPolitical.merge(trainingTweetDf, on='tweet_id')\n",
        "dfPoliticalAll_train = dfPoliticalAll_train.sample(frac=1) #\"merge\" sorts the dataframe. Make sure to reshuffle it to reduce error chance\n",
        "dfPoliticalAll_train.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ycIWfHXaFwZw"
      },
      "source": [
        "### 2.1.2. Separate X and y values\n",
        "We only use 7 features here to create a baseline model. However, it is not enough to get good results."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 137,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T19:59:13.428223Z",
          "start_time": "2022-12-29T19:59:13.399385Z"
        },
        "collapsed": true,
        "id": "GApkcWmQFwZw"
      },
      "outputs": [],
      "source": [
        "X = dfPoliticalAll_train[['num_political_entities','total_interactions','num_hashtags','is_reply','account_activeness', 'tweets_listed_count']]\n",
        "y = dfPoliticalAll_train['isPolitical'].apply(lambda x: 1 if x=='Yes' else 0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XnuR_T6xNxgw"
      },
      "source": [
        "#### 2.1.2.1 balance data by oversampling with SMOTE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 138,
      "metadata": {
        "id": "jb43T0u_Nxgw"
      },
      "outputs": [],
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "from imblearn.pipeline import Pipeline\n",
        "\n",
        "# transform the dataset\n",
        "oversample = SMOTE(sampling_strategy=0.6)\n",
        "undersample = RandomUnderSampler(sampling_strategy=0.6)\n",
        "\n",
        "pipeline = Pipeline(steps=[('o', oversample), ('u', undersample)])\n",
        "X, y = pipeline.fit_resample(X, y)\n",
        "#X, y = oversample.fit_resample(X, y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xp7i4WxMFwZw"
      },
      "source": [
        "### 2.1.3. Train - validation split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 139,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T19:59:16.802482Z",
          "start_time": "2022-12-29T19:59:15.183571Z"
        },
        "collapsed": true,
        "id": "48tEoZfPFwZw"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.20, random_state=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-3HDYu3_FwZw"
      },
      "source": [
        "### 2.1.4. Train the model\n",
        "\n",
        "Here, you may use different models such as neural networks, XGBoost, AdaBoost, RandomForest, Linear Regression, Logistic Regression etc. to see which model does the best. Also, you can use grid_search_cv() or a basic for loop to optimize the hyperparameters of your model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 150,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T20:01:13.817609Z",
          "start_time": "2022-12-29T20:01:13.762926Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eWq9Lb3TFwZx",
        "outputId": "6cacce77-a152-45e1-d0fc-e0ee1131c8f3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Fitting 10 folds for each of 13 candidates, totalling 130 fits\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\necagiltay\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:679: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
            "  warnings.warn(\n",
            "C:\\Users\\necagiltay\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:679: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
            "  warnings.warn(\n",
            "C:\\Users\\necagiltay\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:679: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
            "  warnings.warn(\n",
            "C:\\Users\\necagiltay\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:679: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
            "  warnings.warn(\n",
            "C:\\Users\\necagiltay\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:679: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
            "  warnings.warn(\n",
            "C:\\Users\\necagiltay\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:679: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MSE: 0.27365728900255754 \n",
            " Accuracy Score: 0.7263427109974424\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\necagiltay\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\sklearn\\base.py:402: UserWarning: X has feature names, but MLPClassifier was fitted without feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.linear_model import LogisticRegression, LinearRegression\n",
        "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, mean_squared_error\n",
        "\n",
        "# create an instance\n",
        "dtc_political = MLPClassifier()\n",
        "\n",
        "# fit your model\n",
        "cross_v = 10\n",
        "\n",
        "max_depth = [550]\n",
        "param_grid = {'max_iter': max_depth, 'solver': ['adam'], 'activation': ['tanh'], 'hidden_layer_sizes': [(50,100,50)]}\n",
        "\n",
        "clf_max_depth = MLPClassifier()\n",
        "\n",
        "grid = GridSearchCV(clf_max_depth,\n",
        "                    param_grid, \n",
        "                    cv=cross_v,\n",
        "                    scoring='precision',\n",
        "                    return_train_score=False, \n",
        "                    verbose=1, \n",
        "                    refit=True)\n",
        "\n",
        "dtc_political = grid.fit(X_train.values, y_train)\n",
        "\n",
        "# make predictions\n",
        "preds = dtc_political.predict(X_valid)\n",
        "\n",
        "# evaluate on validation set\n",
        "acc_score = accuracy_score(y_valid, preds)\n",
        "mse = mean_squared_error(y_valid, preds)\n",
        "\n",
        "print(\"MSE:\", mse, \"\\n\",\n",
        "      \"Accuracy Score:\", acc_score)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 151,
      "metadata": {
        "id": "2AP6erfENxgw",
        "outputId": "6b3a198b-c87b-4ff4-ccc4-ce8ce3229b48"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'activation': 'tanh',\n",
              " 'hidden_layer_sizes': (50, 100, 50),\n",
              " 'max_iter': 550,\n",
              " 'solver': 'adam'}"
            ]
          },
          "execution_count": 151,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dtc_political.best_params_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 147,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "phriwRwLE_dH",
        "outputId": "3aafa95f-8b12-4f22-f3fa-5d11cf81c8c5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'activation': 'relu',\n",
              " 'alpha': 0.0001,\n",
              " 'batch_size': 'auto',\n",
              " 'beta_1': 0.9,\n",
              " 'beta_2': 0.999,\n",
              " 'early_stopping': False,\n",
              " 'epsilon': 1e-08,\n",
              " 'hidden_layer_sizes': (100,),\n",
              " 'learning_rate': 'constant',\n",
              " 'learning_rate_init': 0.001,\n",
              " 'max_fun': 15000,\n",
              " 'max_iter': 200,\n",
              " 'momentum': 0.9,\n",
              " 'n_iter_no_change': 10,\n",
              " 'nesterovs_momentum': True,\n",
              " 'power_t': 0.5,\n",
              " 'random_state': None,\n",
              " 'shuffle': True,\n",
              " 'solver': 'adam',\n",
              " 'tol': 0.0001,\n",
              " 'validation_fraction': 0.1,\n",
              " 'verbose': False,\n",
              " 'warm_start': False}"
            ]
          },
          "execution_count": 147,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "MLPClassifier().get_params()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Az-GChRFwZx"
      },
      "source": [
        "## 2.2. Bot Detection"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iIWt3WrgFwZx"
      },
      "source": [
        "### 2.2.1. Merge dfBotAll data with labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T20:01:17.642390Z",
          "start_time": "2022-12-29T20:01:17.596328Z"
        },
        "collapsed": true,
        "id": "bwJGDFDIFwZy"
      },
      "outputs": [],
      "source": [
        "dfBotAll.user_screen_name = dfBotAll.user_screen_name.str.lower()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T20:01:18.350217Z",
          "start_time": "2022-12-29T20:01:18.284039Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 644
        },
        "id": "AroHVEAQFwZy",
        "outputId": "cf3b82b0-e97f-405a-d6e5-8c79526107c8",
        "scrolled": true
      },
      "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>user_id</th>\n",
              "      <th>user_name</th>\n",
              "      <th>user_screen_name</th>\n",
              "      <th>user_location</th>\n",
              "      <th>user_description</th>\n",
              "      <th>user_followers_count</th>\n",
              "      <th>user_friends_count</th>\n",
              "      <th>favourites_count</th>\n",
              "      <th>geo_enabled</th>\n",
              "      <th>verified</th>\n",
              "      <th>statuses_count</th>\n",
              "      <th>protected</th>\n",
              "      <th>listed_count</th>\n",
              "      <th>description_len</th>\n",
              "      <th>followers_to_all_ratio</th>\n",
              "      <th>account_age</th>\n",
              "      <th>account_activeness</th>\n",
              "      <th>UsernameSpecialCharacterCount</th>\n",
              "      <th>retweet_total_ratio</th>\n",
              "      <th>num_median_favorites</th>\n",
              "      <th>num_of_tweets</th>\n",
              "      <th>screen_name</th>\n",
              "      <th>isBot</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1431241870848450577</td>\n",
              "      <td>Nasreena Khan Wazir</td>\n",
              "      <td>nasreenakhan006</td>\n",
              "      <td>Islamabad, Pakistan</td>\n",
              "      <td>Student</td>\n",
              "      <td>65</td>\n",
              "      <td>185</td>\n",
              "      <td>17676</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>2551</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0.260000</td>\n",
              "      <td>2021-08-27 13:07:30+00:00</td>\n",
              "      <td>5.001961</td>\n",
              "      <td>3</td>\n",
              "      <td>0.395939</td>\n",
              "      <td>0.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>nasreenakhan006</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1431241870848450577</td>\n",
              "      <td>Nasreena Khan Wazir</td>\n",
              "      <td>nasreenakhan006</td>\n",
              "      <td>Islamabad, Pakistan</td>\n",
              "      <td>Student</td>\n",
              "      <td>65</td>\n",
              "      <td>185</td>\n",
              "      <td>17676</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>2551</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0.260000</td>\n",
              "      <td>2021-08-27 13:07:30+00:00</td>\n",
              "      <td>5.001961</td>\n",
              "      <td>3</td>\n",
              "      <td>0.395939</td>\n",
              "      <td>0.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>nasreenakhan006</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1431241870848450577</td>\n",
              "      <td>Nasreena Khan Wazir</td>\n",
              "      <td>nasreenakhan006</td>\n",
              "      <td>Islamabad, Pakistan</td>\n",
              "      <td>Student</td>\n",
              "      <td>65</td>\n",
              "      <td>185</td>\n",
              "      <td>17676</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>2551</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0.260000</td>\n",
              "      <td>2021-08-27 13:07:30+00:00</td>\n",
              "      <td>5.001961</td>\n",
              "      <td>3</td>\n",
              "      <td>0.395939</td>\n",
              "      <td>0.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>nasreenakhan006</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1431241870848450577</td>\n",
              "      <td>Nasreena Khan Wazir</td>\n",
              "      <td>nasreenakhan006</td>\n",
              "      <td>Islamabad, Pakistan</td>\n",
              "      <td>Student</td>\n",
              "      <td>65</td>\n",
              "      <td>185</td>\n",
              "      <td>17676</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>2551</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0.260000</td>\n",
              "      <td>2021-08-27 13:07:30+00:00</td>\n",
              "      <td>5.001961</td>\n",
              "      <td>3</td>\n",
              "      <td>0.395939</td>\n",
              "      <td>0.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>nasreenakhan006</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1304340303080386560</td>\n",
              "      <td>fania :((((</td>\n",
              "      <td>scorpiehoez</td>\n",
              "      <td>bogor</td>\n",
              "      <td>have a holly jolly🎄</td>\n",
              "      <td>8235</td>\n",
              "      <td>3011</td>\n",
              "      <td>15474</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>42771</td>\n",
              "      <td>False</td>\n",
              "      <td>11</td>\n",
              "      <td>19</td>\n",
              "      <td>0.732260</td>\n",
              "      <td>2020-09-11 08:45:44+00:00</td>\n",
              "      <td>49.675958</td>\n",
              "      <td>0</td>\n",
              "      <td>0.125000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>scorpiehoez</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3795</th>\n",
              "      <td>1286770207134973954</td>\n",
              "      <td>Hamide Arabacı</td>\n",
              "      <td>anka6054</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>151</td>\n",
              "      <td>61</td>\n",
              "      <td>5347</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>5288</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.712264</td>\n",
              "      <td>2020-07-24 21:08:34+00:00</td>\n",
              "      <td>5.817382</td>\n",
              "      <td>4</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>anka6054</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3796</th>\n",
              "      <td>1598032338323214338</td>\n",
              "      <td>atamabekleyenbahceci</td>\n",
              "      <td>atamabekleyenzz</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>173</td>\n",
              "      <td>367</td>\n",
              "      <td>196</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>311</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.320370</td>\n",
              "      <td>2022-11-30 19:13:03+00:00</td>\n",
              "      <td>6.220000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.580000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>atamabekleyenzz</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3797</th>\n",
              "      <td>760235343966863360</td>\n",
              "      <td>Emrah İNCİ</td>\n",
              "      <td>memrahinci</td>\n",
              "      <td>Istanbul - Bayburt</td>\n",
              "      <td>Researcher | Middle East | Political Science |...</td>\n",
              "      <td>5863</td>\n",
              "      <td>5905</td>\n",
              "      <td>725</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>1029</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>71</td>\n",
              "      <td>0.498215</td>\n",
              "      <td>2016-08-01 22:06:45+00:00</td>\n",
              "      <td>0.435648</td>\n",
              "      <td>0</td>\n",
              "      <td>0.040000</td>\n",
              "      <td>36.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>memrahinci</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3798</th>\n",
              "      <td>1553973684100124672</td>\n",
              "      <td>Murat Kkk</td>\n",
              "      <td>muratkkk18</td>\n",
              "      <td></td>\n",
              "      <td>Normal sıradan bir insanım</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>38</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>18</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>26</td>\n",
              "      <td>0.090909</td>\n",
              "      <td>2022-08-01 05:19:56+00:00</td>\n",
              "      <td>0.104651</td>\n",
              "      <td>2</td>\n",
              "      <td>0.769231</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>muratkkk18</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3799</th>\n",
              "      <td>1392390985645281283</td>\n",
              "      <td>Münferit Fenerbahçeliler</td>\n",
              "      <td>mnferitfenerba1</td>\n",
              "      <td></td>\n",
              "      <td>Fenerbahçe S.K. Kongre Üyesi Kırmızı çizgileri...</td>\n",
              "      <td>281</td>\n",
              "      <td>270</td>\n",
              "      <td>4106</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>7165</td>\n",
              "      <td>False</td>\n",
              "      <td>2</td>\n",
              "      <td>156</td>\n",
              "      <td>0.509982</td>\n",
              "      <td>2021-05-12 08:08:04+00:00</td>\n",
              "      <td>11.593851</td>\n",
              "      <td>1</td>\n",
              "      <td>0.045000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>mnferitfenerba1</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3800 rows × 23 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                  user_id                 user_name user_screen_name  \\\n",
              "0     1431241870848450577       Nasreena Khan Wazir  nasreenakhan006   \n",
              "1     1431241870848450577       Nasreena Khan Wazir  nasreenakhan006   \n",
              "2     1431241870848450577       Nasreena Khan Wazir  nasreenakhan006   \n",
              "3     1431241870848450577       Nasreena Khan Wazir  nasreenakhan006   \n",
              "4     1304340303080386560               fania :((((      scorpiehoez   \n",
              "...                   ...                       ...              ...   \n",
              "3795  1286770207134973954            Hamide Arabacı         anka6054   \n",
              "3796  1598032338323214338      atamabekleyenbahceci  atamabekleyenzz   \n",
              "3797   760235343966863360                Emrah İNCİ       memrahinci   \n",
              "3798  1553973684100124672                 Murat Kkk       muratkkk18   \n",
              "3799  1392390985645281283  Münferit Fenerbahçeliler  mnferitfenerba1   \n",
              "\n",
              "            user_location                                   user_description  \\\n",
              "0     Islamabad, Pakistan                                            Student   \n",
              "1     Islamabad, Pakistan                                            Student   \n",
              "2     Islamabad, Pakistan                                            Student   \n",
              "3     Islamabad, Pakistan                                            Student   \n",
              "4                  bogor                                 have a holly jolly🎄   \n",
              "...                   ...                                                ...   \n",
              "3795                                                                           \n",
              "3796                                                                           \n",
              "3797   Istanbul - Bayburt  Researcher | Middle East | Political Science |...   \n",
              "3798                                              Normal sıradan bir insanım   \n",
              "3799                       Fenerbahçe S.K. Kongre Üyesi Kırmızı çizgileri...   \n",
              "\n",
              "      user_followers_count  user_friends_count  favourites_count  geo_enabled  \\\n",
              "0                       65                 185             17676         True   \n",
              "1                       65                 185             17676         True   \n",
              "2                       65                 185             17676         True   \n",
              "3                       65                 185             17676         True   \n",
              "4                     8235                3011             15474         True   \n",
              "...                    ...                 ...               ...          ...   \n",
              "3795                   151                  61              5347        False   \n",
              "3796                   173                 367               196        False   \n",
              "3797                  5863                5905               725         True   \n",
              "3798                     1                  10                38        False   \n",
              "3799                   281                 270              4106         True   \n",
              "\n",
              "      verified  statuses_count  protected  listed_count  description_len  \\\n",
              "0        False            2551      False             0                7   \n",
              "1        False            2551      False             0                7   \n",
              "2        False            2551      False             0                7   \n",
              "3        False            2551      False             0                7   \n",
              "4        False           42771      False            11               19   \n",
              "...        ...             ...        ...           ...              ...   \n",
              "3795     False            5288      False             0                0   \n",
              "3796     False             311      False             0                0   \n",
              "3797     False            1029      False             0               71   \n",
              "3798     False              18      False             0               26   \n",
              "3799     False            7165      False             2              156   \n",
              "\n",
              "      followers_to_all_ratio               account_age  account_activeness  \\\n",
              "0                   0.260000 2021-08-27 13:07:30+00:00            5.001961   \n",
              "1                   0.260000 2021-08-27 13:07:30+00:00            5.001961   \n",
              "2                   0.260000 2021-08-27 13:07:30+00:00            5.001961   \n",
              "3                   0.260000 2021-08-27 13:07:30+00:00            5.001961   \n",
              "4                   0.732260 2020-09-11 08:45:44+00:00           49.675958   \n",
              "...                      ...                       ...                 ...   \n",
              "3795                0.712264 2020-07-24 21:08:34+00:00            5.817382   \n",
              "3796                0.320370 2022-11-30 19:13:03+00:00            6.220000   \n",
              "3797                0.498215 2016-08-01 22:06:45+00:00            0.435648   \n",
              "3798                0.090909 2022-08-01 05:19:56+00:00            0.104651   \n",
              "3799                0.509982 2021-05-12 08:08:04+00:00           11.593851   \n",
              "\n",
              "      UsernameSpecialCharacterCount  retweet_total_ratio  \\\n",
              "0                                 3             0.395939   \n",
              "1                                 3             0.395939   \n",
              "2                                 3             0.395939   \n",
              "3                                 3             0.395939   \n",
              "4                                 0             0.125000   \n",
              "...                             ...                  ...   \n",
              "3795                              4             0.000000   \n",
              "3796                              0             0.580000   \n",
              "3797                              0             0.040000   \n",
              "3798                              2             0.769231   \n",
              "3799                              1             0.045000   \n",
              "\n",
              "      num_median_favorites  num_of_tweets      screen_name isBot  \n",
              "0                      0.0          197.0  nasreenakhan006   Yes  \n",
              "1                      0.0          197.0  nasreenakhan006   Yes  \n",
              "2                      0.0          197.0  nasreenakhan006    No  \n",
              "3                      0.0          197.0  nasreenakhan006    No  \n",
              "4                      0.0          200.0      scorpiehoez    No  \n",
              "...                    ...            ...              ...   ...  \n",
              "3795                   1.0          200.0         anka6054    No  \n",
              "3796                   0.0          200.0  atamabekleyenzz    No  \n",
              "3797                  36.0          200.0       memrahinci    No  \n",
              "3798                   0.0           13.0       muratkkk18    No  \n",
              "3799                   1.0          200.0  mnferitfenerba1    No  \n",
              "\n",
              "[3800 rows x 23 columns]"
            ]
          },
          "execution_count": 62,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dfBotAll_train = dfBotAll.merge(trainingUserDf,\n",
        "                               left_on='user_screen_name',\n",
        "                               right_on='screen_name')\n",
        "\n",
        "dfBotAll_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T20:01:21.419283Z",
          "start_time": "2022-12-29T20:01:21.380530Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MDuzoN9GFwZy",
        "outputId": "b444ae21-3869-4e8d-8526-df2b587633e4",
        "scrolled": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "No          3027\n",
              "Yes          690\n",
              "Not sure      78\n",
              "Name: isBot, dtype: int64"
            ]
          },
          "execution_count": 63,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainingUserDf.isBot.value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qq6wCVglFwZy"
      },
      "source": [
        "### 2.2.2. Separate X and y values\n",
        "We use only 4 features here to create a baseline model. However, it is not enough to get good results."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T20:01:23.353587Z",
          "start_time": "2022-12-29T20:01:23.320196Z"
        },
        "collapsed": true,
        "id": "P_PsDneQFwZy"
      },
      "outputs": [],
      "source": [
        "X = dfBotAll_train[['description_len', 'followers_to_all_ratio', 'retweet_total_ratio', 'num_median_favorites']]\n",
        "y = dfBotAll_train.isBot.apply(lambda x: 1 if x=='Yes' else 0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gNUsIUQ2FwZz"
      },
      "source": [
        "### 2.2.3. Train-test split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T20:01:25.431157Z",
          "start_time": "2022-12-29T20:01:25.394434Z"
        },
        "collapsed": true,
        "id": "zBJjxRcnFwZz"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.20, random_state=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ejWXbMAXFwZz"
      },
      "source": [
        "### 2.2.4. Train the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T20:01:53.550133Z",
          "start_time": "2022-12-29T20:01:53.500634Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "blDe5ixDFwZz",
        "outputId": "eb8ee50b-aebe-49b8-a7b5-ea658ba10f6a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Fitting 10 folds for each of 1 candidates, totalling 10 fits\n",
            "MSE: 0.26854219948849106 \n",
            " Accuracy Score: 0.8342105263157895 \n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\necagiltay\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\sklearn\\base.py:402: UserWarning: X has feature names, but RandomForestClassifier was fitted without feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.linear_model import LogisticRegression, LinearRegression\n",
        "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, mean_squared_error\n",
        "\n",
        "# create an instance\n",
        "dtc_bot = RandomForestClassifier()\n",
        "\n",
        "# fit your model\n",
        "cross_v = 10\n",
        "\n",
        "max_depth = [5]\n",
        "param_grid = {'max_depth': max_depth, 'n_estimators': [14]}\n",
        "\n",
        "clf_max_depth = RandomForestClassifier()\n",
        "\n",
        "grid = GridSearchCV(clf_max_depth,\n",
        "                    param_grid, \n",
        "                    cv=cross_v,\n",
        "                    scoring='precision',\n",
        "                    return_train_score=False, \n",
        "                    verbose=1, \n",
        "                    refit=True)\n",
        "\n",
        "dtc_bot = grid.fit(X_train.values, y_train)\n",
        "\n",
        "# make predictions\n",
        "preds = dtc_bot.predict(X_valid)\n",
        "\n",
        "# evaluate on validation set\n",
        "acc_score = accuracy_score(y_valid, preds)\n",
        "confusion = confusion_matrix(y_valid, preds)\n",
        "\n",
        "print(\"MSE:\", mse, \"\\n\",\n",
        "      \"Accuracy Score:\", acc_score, \"\\n\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pbe_IlRt4ChG",
        "outputId": "ca3fe6ec-53ce-455d-e3e9-f0c2c53215b5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'max_depth': 5, 'n_estimators': 14}"
            ]
          },
          "execution_count": 67,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dtc_bot.best_params_"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W3u-kG_NFwZ0"
      },
      "source": [
        "# 3. MAKE PREDICTIONS\n",
        "\n",
        "Here, you will make predictions with the models that you have trained above."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1xq7AZIMFwZ0"
      },
      "source": [
        "## 3.1. Predictions for Tweets (Political or Not)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T20:02:00.337133Z",
          "start_time": "2022-12-29T20:01:59.985051Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "7JLizM5OFwZ0",
        "outputId": "340f818e-82d0-4b6f-cbad-1a8ee5ed1886"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\necagiltay\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\sklearn\\base.py:402: UserWarning: X has feature names, but MLPClassifier was fitted without feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "# read the evaluation file as follows\n",
        "evaluationTweetDf = pd.read_csv('{}evaluation-round{}-tweet.csv'.format(DATA_PATH,ROUND), dtype={0: str}, header=None, names=['tweet_id'])\n",
        "evaluationTweetDf = evaluationTweetDf.dropna()\n",
        "evaluationTweetDf\n",
        "\n",
        "# merge it with the political dataframe so that you can use the make predictions based on the variables\n",
        "dfPolitical_test = dfPolitical.merge(evaluationTweetDf)\n",
        "\n",
        "# define X as we did above in section (2.x.2. Separate X and y values)\n",
        "X = dfPolitical_test[['num_political_entities','total_interactions','num_hashtags','is_reply','account_activeness', 'friend_ratio','tweets_listed_count']]\n",
        "\n",
        "# make predictions based on these variables\n",
        "predictions_political = dtc_political.predict(X)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hiaislYYFwZ0"
      },
      "source": [
        "### This part is important! We expect you to return your predictions in the following format:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T20:02:02.548379Z",
          "start_time": "2022-12-29T20:02:02.490462Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9sDkhoXlFwZ1",
        "outputId": "d4be54fe-48e6-4c57-ac3d-a8e7f6f7a16f"
      },
      "outputs": [
        {
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              " '1588321676323950593': 1.0,\n",
              " '1544186612246003713': 1.0,\n",
              " '1593561913761452032': 1.0,\n",
              " '1400907711148089348': 0.0,\n",
              " ...}"
            ]
          },
          "execution_count": 73,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "modelPredTweet = dict([(x,float(y)) for x,y in zip([*dfPolitical_test.tweet_id], predictions_political)])\n",
        "modelPredTweet"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Ml_6ayXFwZ1"
      },
      "source": [
        "## 3.2. Predictions for Users (Bot or Not)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T20:02:05.659491Z",
          "start_time": "2022-12-29T20:02:05.592498Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "Envp8eU5FwZ1",
        "outputId": "7e6f8bca-1d8c-40eb-b45b-72a4785bb068"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\necagiltay\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\sklearn\\base.py:402: UserWarning: X has feature names, but RandomForestClassifier was fitted without feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "evaluationUserDf = pd.read_csv('{}evaluation-round{}-user.csv'.format(DATA_PATH,ROUND), dtype={0: str}, header=None, names=['user_screen_name'])\n",
        "evaluationUserDf = evaluationUserDf.dropna()\n",
        "\n",
        "# merge it with the political dataframe so that you can use the make predictions based on the variables\n",
        "dfBot_test = dfBotAll.merge(evaluationUserDf)\n",
        "\n",
        "# define X as we did above in section (2.x.2. Separate X and y values)\n",
        "X = dfBot_test[['description_len', 'followers_to_all_ratio', 'retweet_total_ratio', 'num_median_favorites']]\n",
        "\n",
        "# make predictions based on these variables\n",
        "predictions_bot = dtc_bot.predict(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T20:02:07.375558Z",
          "start_time": "2022-12-29T20:02:07.271586Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a2y7wxjXFwZ1",
        "outputId": "a099f0e0-d905-4dbf-c2d2-25bd8266b92d"
      },
      "outputs": [
        {
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              " ...}"
            ]
          },
          "execution_count": 75,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "modelPredUser = dict([(x,float(y)) for x,y in zip([*dfBot_test.user_screen_name], predictions_bot)])\n",
        "modelPredUser"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aVJHUnHYFwZ2"
      },
      "source": [
        "# PREPARE SUBMISSION"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VG858BQ_FwZ2"
      },
      "source": [
        "You will need to submit exact same file produced by using the following code. Any deviation from the desired format willbe marked as 0."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T20:02:09.743420Z",
          "start_time": "2022-12-29T20:02:09.716634Z"
        },
        "collapsed": true,
        "id": "wsmfsDOeFwZ2"
      },
      "outputs": [],
      "source": [
        "# Explain your approach\n",
        "\n",
        "data_explanations = '''\n",
        "Not much to say about how I handled the data, I mostly utilized the given codes and fixed a couple of \n",
        "small issues relating to them like specifying the 'PATH_DATA' and the formats regarding the '.csv' files.\n",
        "I also added all of our project members' annotation data to the dataset to get better results.\n",
        "'''\n",
        "\n",
        "feature_explanations = '''\n",
        "We have added two features to the user dataset: 'account_activeness' and 'friend_ratio'. These features are\n",
        "important because political accounts are generally more active in social media whether they retweet or publish.\n",
        "If an account is more active than others, there is a chance that the account could be related to someone political.\n",
        "The friend ratio feature is important too because in Twitter, political accounts usually have more followers than they\n",
        "follow, so if an account's ratio is lower than an average account, chances are that account is someone important or\n",
        "related to an organization, sometimes political and sometimes not.\n",
        "\n",
        "We have also added the 'is_reply' feature to the tweets dataset to see if the user is replying to another user.\n",
        "Users may be messaging in reply to another figure and trolling or making political comments about them was the reasoning for this.\n",
        "'''\n",
        "\n",
        "model_explanations = '''\n",
        "I have experimented with various machine learning classifiers like GaussianNB, KNearestNeigbors, LogicalRegression,\n",
        "XGBoostClassifier and many more. I noticed that RandomForestClassifier gives better results for our Tweet dataset and I tried to\n",
        "optimize the results by changing different parameters of the classifier, eventually deciding on the final values of the parameters.\n",
        "I also noticed that for the users dataset, GaussianNB is a better alternative than RandomForest, but I unfortunately couldn't get it\n",
        "to work in time, so I used RandomForestClassifier for the users dataset as well.\n",
        "'''\n",
        "\n",
        "additional_explanations = '''\n",
        "While testing with different classifiers, I used the '.best_params_' method for various models to find out the most optimized values\n",
        "for the parameters of the classifiers. By comparing different classifiers' most optimized results with this method, I have decided on\n",
        "using RandomForestClassifier.\n",
        "'''\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T20:02:10.502116Z",
          "start_time": "2022-12-29T20:02:10.448874Z"
        },
        "id": "YhFO_5h_FwZ2"
      },
      "outputs": [],
      "source": [
        "predictions = {\n",
        "    'round': ROUND,\n",
        "    'student_id': STUDENT_ID,\n",
        "    'user_predictions': modelPredUser,\n",
        "    'tweet_predictions': modelPredTweet,\n",
        "    'explanations': {\n",
        "        'data': data_explanations,\n",
        "        'feature': feature_explanations,\n",
        "        'model': model_explanations,\n",
        "        'other': additional_explanations,\n",
        "    }\n",
        "}\n",
        "\n",
        "\n",
        "with open('{}predictions-{}_round{}.json'.format(DATA_PATH, STUDENT_ID, ROUND), 'w') as fl:\n",
        "    fl.write(json.dumps(predictions, indent=4))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-12-29T20:02:12.373244Z",
          "start_time": "2022-12-29T20:02:12.302973Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PxAxcrznFwZ2",
        "outputId": "4202540b-cf5b-4034-9f7c-47bafa98cd4b"
      },
      "outputs": [
        {
          "data": {
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              " 'explanations': {'data': \"\\nNot much to say about how I handled the data, I mostly utilized the given codes and fixed a couple of \\nsmall issues relating to them like specifying the 'PATH_DATA' and the formats regarding the '.csv' files.\\nI also added all of our project members' annotation data to the dataset to get better results.\\n\",\n",
              "  'feature': \"\\nWe have added two features to the user dataset: 'account_activeness' and 'friend_ratio'. These features are\\nimportant because political accounts are generally more active in social media whether they retweet or publish.\\nIf an account is more active than others, there is a chance that the account could be related to someone political.\\nThe friend ratio feature is important too because in Twitter, political accounts usually have more followers than they\\nfollow, so if an account's ratio is lower than an average account, chances are that account is someone important or\\nrelated to an organization, sometimes political and sometimes not.\\n\\nWe have also added the 'is_reply' feature to the tweets dataset to see if the user is replying to another user.\\nUsers may be messaging in reply to another figure and trolling or making political comments about them was the reasoning for this.\\n\",\n",
              "  'model': \"\\nI have experimented with various machine learning classifiers like GaussianNB, KNearestNeigbors, LogicalRegression,\\nXGBoostClassifier and many more. I noticed that RandomForestClassifier gives better results for our Tweet dataset and I tried to\\noptimize the results by changing different parameters of the classifier, eventually deciding on the final values of the parameters.\\nI also noticed that for the users dataset, GaussianNB is a better alternative than RandomForest, but I unfortunately couldn't get it\\nto work in time, so I used RandomForestClassifier for the users dataset as well.\\n\",\n",
              "  'other': \"\\nWhile testing with different classifiers, I used the '.best_params_' method for various models to find out the most optimized values\\nfor the parameters of the classifiers. By comparing different classifiers' most optimized results with this method, I have decided on\\nusing RandomForestClassifier.\\n\"}}"
            ]
          },
          "execution_count": 78,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Test your submission file\n",
        "\n",
        "submission = json.load(open('{}predictions-{}_round{}.json'.format(DATA_PATH, STUDENT_ID, ROUND), 'r'))\n",
        "submission"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "pQWvYVH3FwZ3"
      },
      "outputs": [],
      "source": []
    }
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