QReddit_plots.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HeYlsxIq5CqK"
      },
      "source": [
        "## Environment Set up"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AudSyK3x59MP"
      },
      "source": [
        "### Colab to Delete"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HjlGsmAkMmyo"
      },
      "outputs": [],
      "source": [
        "# Put your input and output paths here:\n",
        "\n",
        "inputPath = ''\n",
        "outputPath = ''"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ScoeJ8vI6DLz",
        "outputId": "8aa89ac1-c436-45b6-a147-04f587ed5286"
      },
      "outputs": [],
      "source": [
        "# Uncomment this if running on Google Colab to install Latex:\n",
        "\n",
        "# !git clone https://github.com/latex3/latex2e.git\n",
        "\n",
        "# !apt-get update\n",
        "# !sudo apt-get install texlive-full "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nnn9mt1R6LqO",
        "outputId": "be6498a2-b362-4943-d6e3-a35b923a6266"
      },
      "outputs": [],
      "source": [
        "# Uncomment this if running on Google Colab to install Latex:\n",
        "\n",
        "# !pip install latex\n",
        "# import latex"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1HUZ5lv35GD-"
      },
      "source": [
        "### Global Settings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "7rZmT9BY5M1m"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import datetime\n",
        "import numpy as np\n",
        "\n",
        "# Latex Configuration\n",
        "import matplotlib.font_manager as font_manager\n",
        "from matplotlib.lines import Line2D\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as ticker\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib as mpl\n",
        "import seaborn as sns\n",
        "import os\n",
        "from collections import defaultdict\n",
        "\n",
        "from scipy.stats import chi2_contingency\n",
        "\n",
        "params = {\n",
        "    \"axes.titlesize\" : 25,\n",
        "    'axes.labelsize': 20,\n",
        "    'font.size': 15,\n",
        "    'legend.fontsize': 20,\n",
        "    'xtick.labelsize': 15,\n",
        "    'ytick.labelsize': 20,\n",
        "    'font.family': 'serif',\n",
        "    'text.usetex': True\n",
        "}\n",
        "\n",
        "mpl.rcParams.update(params)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "CrNbxDzL4MqX"
      },
      "outputs": [],
      "source": [
        "TIMEFORMAT = \"%Y-%m-%d %H:%M:%S\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "FtyHiah_4MqX"
      },
      "outputs": [],
      "source": [
        "Q_emergence = datetime.date(2017, 10, 28)\n",
        "Q_ban = datetime.date(2018, 9, 12)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "XQTR5QED4MqY"
      },
      "outputs": [],
      "source": [
        "time_delta = Q_ban - Q_emergence"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ylLLUoic4MqZ",
        "outputId": "be863cfd-8abf-458a-d868-a1dcb2a3e067"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "datetime.timedelta(days=319)"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "time_delta"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "nk7OatIp4Mqa"
      },
      "outputs": [],
      "source": [
        "colors = [\n",
        "    \"#72a555\",\n",
        "    \"#ab62c0\",\n",
        "    \"#c57c3c\",\n",
        "    \"#638ccc\",\n",
        "    \"#ca5670\"\n",
        "]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W3428jc67E3L"
      },
      "source": [
        "## Import and Format Input Files"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "buJ2C6OV7I3p"
      },
      "source": [
        "### Reddit submissions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KNOfdss-4Mqa",
        "outputId": "7d3462cd-ef44-4b9a-a550-3fa24676df74"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py:2882: DtypeWarning: Columns (6) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "(2099875, 12)"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sub_df = pd.read_csv(inputPath + \"Hashed_Q_Submissions_Raw_Combined.csv\", lineterminator='\\n')\n",
        "sub_df = sub_df.drop(columns=[\"upvote_ratio\"]).drop_duplicates()\n",
        "sub_df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qBrd5eAG4Mqb",
        "outputId": "441bac1c-b517-4b95-9e9c-a3ad22be5b96"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['subreddit', 'id', 'score', 'numReplies', 'author', 'title', 'text',\n",
              "       'is_self', 'domain', 'url', 'permalink', 'date_created'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sub_df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "h9bxWH6j4Mqc"
      },
      "outputs": [],
      "source": [
        "sub_df['date_created'] = pd.to_datetime(sub_df['date_created'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "O1fbvxTP4Mqd"
      },
      "outputs": [],
      "source": [
        "sub_df['type'] = 'submission'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fs8ypJ9j8OHO"
      },
      "source": [
        "### Reddit Comments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jMSQqz8B4Mqe",
        "outputId": "7e6a4e9e-0d7d-4778-aa74-07f9dc317d58"
      },
      "outputs": [],
      "source": [
        "comments_df = pd.read_csv(inputPath + \"Hashed_Q_Comments_Raw_Combined.csv\", lineterminator='\\n')\n",
        "comments_df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6X6bWIPg4Mqe",
        "outputId": "8e8e8647-f64e-428c-c003-49553ae0acf1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['id', 'link_id', 'parent_id', 'author', 'subreddit', 'body',\n",
              "       'date_created'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "comments_df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "Xapxyf9T4Mqf"
      },
      "outputs": [],
      "source": [
        "comments_df['date_created'] = pd.to_datetime(comments_df['date_created'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3kmqPChS4Mqg",
        "outputId": "1cf077e0-90e5-4e93-b3ef-349b702ea645"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "10831922"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(comments_df[~pd.isnull(comments_df['id'])])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "a1rGnoa44Mqh"
      },
      "outputs": [],
      "source": [
        "comments_df['type'] = 'comment'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uBTerNpQ8i6o"
      },
      "source": [
        "### Combined submission and comment activity"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "wtuRvUOQ4Mqh"
      },
      "outputs": [],
      "source": [
        "all_df = pd.concat([sub_df, comments_df])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k1SfEai_4Mqi",
        "outputId": "cdd75fe5-868a-4ea3-8934-c20257a73d97"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(12931797, 16) 13182 authors 63697 subreddits\n"
          ]
        }
      ],
      "source": [
        "print(all_df.shape, all_df.author.nunique(), 'authors', all_df.subreddit.nunique(), 'subreddits')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "id": "9xsuFBFnD6AN",
        "outputId": "41d31219-76ae-4a9a-e653-3202909fc724"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>subreddit</th>\n",
              "      <th>id</th>\n",
              "      <th>score</th>\n",
              "      <th>numReplies</th>\n",
              "      <th>author</th>\n",
              "      <th>title</th>\n",
              "      <th>text</th>\n",
              "      <th>is_self</th>\n",
              "      <th>domain</th>\n",
              "      <th>url</th>\n",
              "      <th>permalink</th>\n",
              "      <th>date_created</th>\n",
              "      <th>type</th>\n",
              "      <th>link_id</th>\n",
              "      <th>parent_id</th>\n",
              "      <th>body</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>greatawakening</td>\n",
              "      <td>8xuv4i</td>\n",
              "      <td>1.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>879f283b831c13474e219e88663d95b0763cca9b</td>\n",
              "      <td>I’ve been writing “Trump Lives Here” on my $20...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>i.redd.it</td>\n",
              "      <td>https://i.redd.it/h3mbbxvxq7911.jpg</td>\n",
              "      <td>/r/greatawakening/comments/8xuv4i/ive_been_wri...</td>\n",
              "      <td>2018-07-11 00:27:24</td>\n",
              "      <td>submission</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>greatawakening</td>\n",
              "      <td>8ydw3e</td>\n",
              "      <td>1.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>879f283b831c13474e219e88663d95b0763cca9b</td>\n",
              "      <td>Trying to take him seriously but...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>i.redd.it</td>\n",
              "      <td>https://i.redd.it/62gaw0th4l911.jpg</td>\n",
              "      <td>/r/greatawakening/comments/8ydw3e/trying_to_ta...</td>\n",
              "      <td>2018-07-12 21:26:32</td>\n",
              "      <td>submission</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>greatawakening</td>\n",
              "      <td>8ytwg0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>879f283b831c13474e219e88663d95b0763cca9b</td>\n",
              "      <td>“It is all happening!” Crumb?</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>i.redd.it</td>\n",
              "      <td>https://i.redd.it/yo9zscb1jx911.jpg</td>\n",
              "      <td>/r/greatawakening/comments/8ytwg0/it_is_all_ha...</td>\n",
              "      <td>2018-07-14 15:09:25</td>\n",
              "      <td>submission</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c79082be-1d10-4d59-b850-17835f192ef6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "      fill: #1967D2;\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c79082be-1d10-4d59-b850-17835f192ef6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
            ],
            "text/plain": [
              "        subreddit      id  score  numReplies  ...        type link_id parent_id body\n",
              "0  greatawakening  8xuv4i    1.0        14.0  ...  submission     NaN       NaN  NaN\n",
              "1  greatawakening  8ydw3e    1.0        13.0  ...  submission     NaN       NaN  NaN\n",
              "2  greatawakening  8ytwg0    1.0         0.0  ...  submission     NaN       NaN  NaN\n",
              "\n",
              "[3 rows x 16 columns]"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "all_df.head(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7uWFtQAQ8qIz"
      },
      "source": [
        "### Q-user authors"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "SZzOMyGM4Mqi"
      },
      "outputs": [],
      "source": [
        "author_status = pd.read_csv(inputPath + \"Hashed_allAuthorStatus.csv\", index_col=0)\n",
        "author_status.reset_index(inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4TY8qqmv4Mqj",
        "outputId": "9b8d1e4f-39b4-484d-9db8-6babb1f1c1ab"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['QAuthor', 'isUQ', 'status'], dtype='object')"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "author_status.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
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          "height": 206
        },
        "id": "pvQdfMXP4Mqk",
        "outputId": "ba1e13fb-e7fd-4218-9429-eb15007b4cce"
      },
      "outputs": [
        {
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>QAuthor</th>\n",
              "      <th>isUQ</th>\n",
              "      <th>status</th>\n",
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              "      <th>0</th>\n",
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            "text/plain": [
              "                                    QAuthor  isUQ  status\n",
              "0  aa65b7dd5d5fa660d058e094669f884bf7d52299     0  Active\n",
              "1  2b1505f289338751829dfa129c0b52d145c9eceb     1  Active\n",
              "2  4eeddb9abeb3c4889f1b037016bf2aeb834bb66d     0  Active\n",
              "3  08a6fae5a56fcdb495b2de8a02625ea2b4abe32f     1  Active\n",
              "4  01301652214982c57efe894efe7e2c7d57df2801     0  Active"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "author_status.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HuPsBXUKD-3R",
        "outputId": "b2ed74f4-75e3-44f6-f7e6-1216e93660d5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "13182 authors 3506 Q-enthusiastic 9676 Q-interested\n"
          ]
        }
      ],
      "source": [
        "print(author_status.QAuthor.nunique(), 'authors', author_status[author_status['isUQ'] == 1].QAuthor.nunique(), 'Q-enthusiastic', author_status[author_status['isUQ'] == 0].QAuthor.nunique(), 'Q-interested')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n6nanhHbEPqP",
        "outputId": "b82ac730-29a9-431f-a2e6-2e95fbccfe6b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Active          11014\n",
              "DNE              1624\n",
              "Is_suspended      544\n",
              "Name: status, dtype: int64"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "author_status.status.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kY-tXIroJSUy",
        "outputId": "f367b3aa-0c10-4cdf-f25d-70faacf91954"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.4150028522532801"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "the_donald_submissions = sub_df[sub_df[\"subreddit\"]==\"The_Donald\"]\n",
        "the_donald_users = set(the_donald_submissions.author.unique().tolist())\n",
        "\n",
        "q_enth = set(author_status[author_status[\"isUQ\"]==1][\"QAuthor\"].tolist())\n",
        "\n",
        "len(the_donald_users.intersection(q_enth)) / len(q_enth)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5sQAOFK-KXO9",
        "outputId": "50768549-1eb7-41d5-e8da-0995e14207e8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.5581859669138619"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "the_donald_submissions = all_df[all_df[\"subreddit\"]==\"The_Donald\"]\n",
        "the_donald_users = set(the_donald_submissions.author.unique().tolist())\n",
        "\n",
        "q_enth = set(author_status[author_status[\"isUQ\"]==1][\"QAuthor\"].tolist())\n",
        "\n",
        "len(the_donald_users.intersection(q_enth)) / len(q_enth)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HNRRGbFI9WJV"
      },
      "source": [
        "### Active subreddit statistics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "Puzxkgck4Mqk"
      },
      "outputs": [],
      "source": [
        "subreddit_stat = pd.read_csv(inputPath + \"Hashed_subredditStats.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-fqcF2DF4Mql",
        "outputId": "840f428f-1162-4f2e-bc19-87b39b22e58b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['subreddit', 'numSubscribers', 'status', 'allModNames', 'allMods',\n",
              "       'qModNames', 'qMods', 'top_qModNames', 'top_qMods',\n",
              "       'firstPostSubmission', 'lastPostSubmission', 'firstPostComment',\n",
              "       'lastPostComment', 'qModsRatio', 'top_qModsRatio', 'activePreBanOnly',\n",
              "       'activePreQ', 'activePostBan', 'qAuth', 'top_qAuth', 'qSubmissions',\n",
              "       'top_qSubmissions', 'nonTop_qSubmissions', 'qComments', 'top_qComments',\n",
              "       'nonTop_qComments', 'top_qPercent', 'qPercent',\n",
              "       'Monthly Average Total Authors', 'Monthly Average Total Submissions',\n",
              "       'Monthly Average UQ Authors', 'Monthly Average UQ Submissions',\n",
              "       'Monthly Average QAnon Authors', 'Monthly Average QAnon Submissions',\n",
              "       '% UQ Submissions', '% UQ Authors', '% QAnon Submissions',\n",
              "       '% QAnon Authors'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "subreddit_stat.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_M8sc6xnE7Na"
      },
      "source": [
        "### Subreddit Labels (Mapping Topic and Relation Labels to Subreddits)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "xCfbUlx84Mql"
      },
      "outputs": [],
      "source": [
        "subreddit_label_df = pd.read_csv(inputPath + \"Subreddit_Cluster_Map.csv\", index_col=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FNyLldwq4Mqm",
        "outputId": "63580975-13da-4959-d477-29858b9e1e33"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['subreddit', 'Codes', 'Final Topic Clusters',\n",
              "       'Final Relation Clusters'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "subreddit_label_df.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UAfo7yLC4Mqr"
      },
      "source": [
        "## ACTIVITY PATTERNS OF Q-USERS"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AzNAmc6FTUbz"
      },
      "source": [
        "### Utils and Processing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "NdceAuhnTYZz"
      },
      "outputs": [],
      "source": [
        "def paint_bar_plot(bars, seqs, labels, title, filename):\n",
        "  height = [[] for s in seqs]\n",
        "  before = 0\n",
        "  cnted = 0\n",
        "  bar_names = []\n",
        "  color = []\n",
        "\n",
        "  for ind, b in enumerate(bars[::-1]):\n",
        "    for seqind, seq in enumerate(seqs):\n",
        "      cnt = 0\n",
        "      for s in seq:\n",
        "        if (ind == 0 or s > 1. / float(before)) and (s <= 1. / float(b)):\n",
        "            cnt += 1\n",
        "      height[seqind].append(cnt)\n",
        "    if ind == 0:\n",
        "        bar_names.append(r'$[0, \\frac{1}{' + str(b) + '}]$')\n",
        "    else:\n",
        "      bar_names.append(r'$(\\frac{1}{' + str(before) + '},$' + r'$\\frac{1}{' + str(b) + '}]$')\n",
        "    before = b\n",
        "    color.append(colors[0])\n",
        "    \n",
        "  color.append(colors[1])\n",
        "  cnt = 0.\n",
        "  for seqind, seq in enumerate(seqs):\n",
        "    cnt = 0\n",
        "    for s in seq:\n",
        "      if (s > 1. / float(bars[0])) and (s < float(bars[0])):\n",
        "        cnt += 1  \n",
        "    height[seqind].append(cnt)\n",
        "  bar_names.append(r'$(\\frac{1}{' + str(bars[0]) + '}, ' + str(bars[0]) + ')$')\n",
        "\n",
        "\n",
        "  before = bars[0]\n",
        "  for ind, b in enumerate(bars):\n",
        "    for seqind, seq in enumerate(seqs):\n",
        "      cnt = 0\n",
        "      for s in seq:\n",
        "        if (s >= b) and (ind == len(bars)-1 or (s < bars[ind+1])):\n",
        "            cnt += 1\n",
        "      height[seqind].append(cnt)\n",
        "    if ind == len(bars)-1:\n",
        "        bar_names.append(r'$[' + str(b) + ', \\inf)$')\n",
        "    else:\n",
        "      bar_names.append(r'$[' + str(b)+ ', ' + str(bars[ind+1]) + ')$'.format(b, bars[ind+1]))\n",
        "    color.append(colors[2])  \n",
        "  #error = [2.65, 7.27, 7.07, 5.91, 6.11, 12.8]\n",
        "  print(chi2_contingency(height))\n",
        "  height = [[h / float(len(seqs[ind])) for h in hh] for ind, hh in enumerate(height)]\n",
        "  y_pos = np.arange(len(bar_names))\n",
        "\n",
        "  return height, y_pos, bar_names"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "c8pYKO0uVYtc"
      },
      "outputs": [],
      "source": [
        "# Q-user's first and last post (including submissions and comments)\n",
        "all_df['date_created'] = pd.to_datetime(all_df['date_created'])\n",
        "\n",
        "authFirstPost = all_df.groupby('author')[['date_created']].min().reset_index()\n",
        "authFirstPost.rename(columns={\"date_created\": \"first_post\", \"author\":\"QAuthor\"},inplace=True)\n",
        "author_status = author_status.merge(authFirstPost, on=\"QAuthor\", how=\"left\")\n",
        "\n",
        "authLastPost = all_df.groupby('author')[['date_created']].max().reset_index()\n",
        "authLastPost.rename(columns={\"date_created\": \"last_post\", \"author\":\"QAuthor\"},inplace=True)\n",
        "author_status = author_status.merge(authLastPost, on=\"QAuthor\", how=\"left\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ayhA8s33Vfxp",
        "outputId": "b9726ee7-8e0f-4dbb-826e-0da9dcdfcc47"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: FutureWarning: Comparison of Timestamp with datetime.date is deprecated in order to match the standard library behavior.  In a future version these will be considered non-comparable.Use 'ts == pd.Timestamp(date)' or 'ts.date() == date' instead.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: FutureWarning: Comparison of Timestamp with datetime.date is deprecated in order to match the standard library behavior.  In a future version these will be considered non-comparable.Use 'ts == pd.Timestamp(date)' or 'ts.date() == date' instead.\n",
            "  \n"
          ]
        }
      ],
      "source": [
        "author_status[\"active_pre_emergence\"] = author_status.apply(lambda row: row.first_post < Q_emergence, axis=1)\n",
        "author_status[\"active_post_ban\"] = author_status.apply(lambda row: row.last_post > Q_ban, axis=1)\n",
        "Q_intr_df = author_status[author_status[\"isUQ\"]==0]\n",
        "Q_enth_df = author_status[author_status[\"isUQ\"]==1]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7vfh5WyFT0xz"
      },
      "source": [
        "### Table 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ocwTcOLXVL63",
        "outputId": "d6103f7a-a3b3-41b7-de07-2b449c9b89e0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[[9676, 8081, 1156, 439, 2702, 4611], [3506, 2933, 468, 105, 855, 1951]]"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def get_status_count(input_df):\n",
        "    return [\n",
        "        input_df.count()[0], \\\n",
        "        input_df[input_df[\"status\"]==\"Active\"].count()[0], \\\n",
        "        input_df[input_df[\"status\"]==\"DNE\"].count()[0], \\\n",
        "        input_df[input_df[\"status\"]==\"Is_suspended\"].count()[0], \\\n",
        "        input_df[(input_df[\"status\"]==\"Active\") & (input_df[\"active_pre_emergence\"])].count()[0], \\\n",
        "        input_df[(input_df[\"status\"]==\"Active\") & (input_df[\"active_post_ban\"])].count()[0],\n",
        "    ]\n",
        "\n",
        "results = [get_status_count(Q_intr_df), get_status_count(Q_enth_df)]\n",
        "results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8DU9aHi9G1pb"
      },
      "source": [
        "### Fig. 1 Q-users are more active in the Q period"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iChLgZyr4Mqs",
        "outputId": "8532d1e2-17be-4d6b-8d9c-aef997cf7a5d"
      },
      "outputs": [],
      "source": [
        "all_df['phase_1'] = all_df.apply(lambda x: (x['date_created'] < Q_emergence) and\n",
        "                                (x['date_created'] >= Q_emergence - time_delta), axis = 1)\n",
        "all_df['phase_2'] = all_df.apply(lambda x: (x['date_created'] >= Q_emergence) and\n",
        "                                (x['date_created'] <= Q_ban), axis = 1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GuPXS0po4Mqt",
        "outputId": "c0e8be51-91d8-46f9-b89f-181dfeb3298d"
      },
      "outputs": [],
      "source": [
        "all_df['phase_3'] = all_df.apply(lambda x: (x['date_created'] > Q_ban) and\n",
        "                                 (x['date_created'] <= Q_ban + time_delta), axis = 1)\n",
        "all_df['phase_4'] = all_df.apply(lambda x: (x['date_created'] > Q_ban + time_delta) and\n",
        "                                 (x['date_created'] <= Q_ban + 2*time_delta), axis = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5t_7JPNI4Mqw"
      },
      "outputs": [],
      "source": [
        "qauth_activity = pd.merge(all_df[['phase_1', 'phase_2', 'phase_3', 'phase_4', 'author']],\n",
        "                          author_status, left_on=['author'], right_on=['QAuthor'], how='inner')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 835
        },
        "id": "HThtrzGf4Mqz",
        "outputId": "b0b97d0a-3145-4c9c-ad4e-1ee4005dc918"
      },
      "outputs": [
        {
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              "      <th>12931796</th>\n",
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              "      <td>False</td>\n",
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              "      <td>True</td>\n",
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              "      <td>c039f11ff985f7d8547748c10ccd5c8416b8404d</td>\n",
              "      <td>1</td>\n",
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              "      <td>2017-01-09 00:19:12</td>\n",
              "      <td>2019-11-05 21:55:07</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>12931797 rows × 12 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d56b2162-0012-4ff4-834b-b2e9dd922f3e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ],
            "text/plain": [
              "          phase_1  phase_2  ...  active_pre_emergence  active_post_ban\n",
              "0           False     True  ...                 False             True\n",
              "1           False     True  ...                 False             True\n",
              "2           False     True  ...                 False             True\n",
              "3           False     True  ...                 False             True\n",
              "4           False     True  ...                 False             True\n",
              "...           ...      ...  ...                   ...              ...\n",
              "12931792    False    False  ...                  True             True\n",
              "12931793    False    False  ...                  True             True\n",
              "12931794    False    False  ...                  True             True\n",
              "12931795    False    False  ...                  True             True\n",
              "12931796    False    False  ...                  True             True\n",
              "\n",
              "[12931797 rows x 12 columns]"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "qauth_activity"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z3QQchDA4Mq0"
      },
      "outputs": [],
      "source": [
        "auth_stats = qauth_activity[qauth_activity['phase_1']].groupby('author').size().reset_index(name='phase_1_cnt')\n",
        "tmp = qauth_activity[qauth_activity['phase_2']].groupby('author').size().reset_index(name='phase_2_cnt')\n",
        "auth_stats = pd.merge(auth_stats, tmp, on='author', how='outer')\n",
        "tmp = qauth_activity[qauth_activity['phase_3']].groupby('author').size().reset_index(name='phase_3_cnt')\n",
        "auth_stats = pd.merge(auth_stats, tmp, on='author', how='outer')\n",
        "tmp = qauth_activity[qauth_activity['phase_4']].groupby('author').size().reset_index(name='phase_4_cnt')\n",
        "auth_stats = pd.merge(auth_stats, tmp, on='author', how='outer')\n",
        "auth_stats = auth_stats.fillna(0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "90UvmB8E4Mq1"
      },
      "outputs": [],
      "source": [
        "auth_stats = auth_stats[auth_stats['phase_2_cnt'] >= 5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qu51X6cd4Mq1",
        "outputId": "481d7860-6810-4b52-d5f2-9a8aa45a8c4b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "10354"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(auth_stats)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1K7GJfkK4Mq1"
      },
      "outputs": [],
      "source": [
        "auth_stats['phase1_to_phase2'] = auth_stats.apply(lambda x: x['phase_1_cnt'] / x['phase_2_cnt'], axis=1)\n",
        "auth_stats['phase3_to_phase2'] = auth_stats.apply(lambda x: x['phase_3_cnt'] / x['phase_2_cnt'], axis=1)\n",
        "auth_stats['phase4_to_phase2'] = auth_stats.apply(lambda x: x['phase_4_cnt'] / x['phase_2_cnt'], axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lLWqJN1z4Mq2"
      },
      "outputs": [],
      "source": [
        "auth_stats = pd.merge(auth_stats, author_status, left_on='author', right_on='QAuthor', how='left')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tH-KpS_-4Mq2",
        "outputId": "a2131256-f862-4f0d-9390-a93f825081ab"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['Active', 'DNE', 'Is_suspended'], dtype=object)"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "auth_stats['status'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YIrJ7vSh4Mq3"
      },
      "outputs": [],
      "source": [
        "active_users = auth_stats['author'].values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8lPyQgZO4Mq4"
      },
      "outputs": [],
      "source": [
        "auth_stats = auth_stats[auth_stats['status'] == 'Active']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ib8jXmIK4Mq4",
        "outputId": "28aabf2a-7f47-4526-99bb-0215a0ab95bf"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.7077304671351798"
            ]
          },
          "execution_count": 49,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(author_status[(author_status.QAuthor.isin(active_users)) & (author_status['isUQ'] == 0)]) / len(author_status[author_status['isUQ'] == 0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "74nIKzK94Mq4",
        "outputId": "00c445fb-7913-4e8a-b563-432b342a3ee4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "status\n",
              "Active          11014\n",
              "DNE              1624\n",
              "Is_suspended      544\n",
              "dtype: int64"
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "author_status.groupby('status').size()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zDSS4V784Mq5",
        "outputId": "b9c7c59a-935d-411b-f418-baaf62b786fd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "status\n",
              "Active          8081\n",
              "DNE             1156\n",
              "Is_suspended     439\n",
              "dtype: int64"
            ]
          },
          "execution_count": 51,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "author_status[author_status['isUQ']==0].groupby('status').size()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DBpml8Gp4Mq5",
        "outputId": "2cd0d677-a8fa-4244-84df-3ac9860190bb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "status\n",
              "Active          2933\n",
              "DNE              468\n",
              "Is_suspended     105\n",
              "dtype: int64"
            ]
          },
          "execution_count": 52,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "author_status[(author_status['isUQ']==1) & (author_status.QAuthor.isin(active_users))].groupby('status').size()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3f8bWXvU5zYF"
      },
      "outputs": [],
      "source": [
        "def calculate_cdf(input_ratios):\n",
        "    cdf = np.arange(1, len(input_ratios)+1) / len(input_ratios)\n",
        "    return cdf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qYtoRMQy9emS"
      },
      "outputs": [],
      "source": [
        "def get_cdf(input_df, input_col):                       \n",
        "    enth_cdf = input_df[input_df[\"isUQ\"]==1][[input_col]].sort_values(by=input_col).copy()\n",
        "    enth_cdf['cdf'] = calculate_cdf(enth_cdf[input_col])\n",
        "    enth_cdf\n",
        "\n",
        "    intr_cdf = input_df[input_df[\"isUQ\"]==0][[input_col]].sort_values(by=input_col).copy()\n",
        "    intr_cdf['cdf'] = calculate_cdf(intr_cdf[input_col])\n",
        "    intr_cdf\n",
        "\n",
        "    return enth_cdf, intr_cdf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dph6DwktT13b"
      },
      "outputs": [],
      "source": [
        "import matplotlib.ticker as mticker\n",
        "\n",
        "def paint_line_graph_log_zero_adjusted(enth, intr, ax, col, title, ifLegend):\n",
        "    enth_adjusted = enth[col].values + 0.00001\n",
        "    ax.plot(enth_adjusted, enth['cdf'].values, color=\"#ab62c0\", linestyle=\"--\", alpha=0.9, linewidth=4, label=\"QAnon-enthusiastic users\", zorder=2)\n",
        "    intr_adjusted = intr[col].values + 0.00001\n",
        "    ax.plot(intr_adjusted, intr['cdf'].values, color=\"#ab62c0\", linestyle=\"-\", alpha=0.45, linewidth=4, label=\"QAnon-interested users\", zorder=2)\n",
        "    \n",
        "    ax.grid(color=\"#d3d3d3\", ls=\":\")\n",
        "    if ifLegend: \n",
        "        ax.legend(loc=2, prop={'size': 18})\n",
        "    ax.set_title(title)\n",
        "    ax.set_xscale('log')\n",
        "    ax.set_xticks([0.00001, 0.00011, 0.00101, 0.01001, 0.10001, 1.00001, 10.00001, 100.00001, 1000.00001, 10000.0001])\n",
        "    vals = ax.get_xticks()\n",
        "    f = mticker.ScalarFormatter(useOffset=False, useMathText=True)\n",
        "    ax.set_xticklabels([\"${}$\".format(f._formatSciNotation('%1.10e' % x)) for x in [0, 0.0001, 0.001, 0.01, 0.1, 1, 10., 100, 1000, 10000]])\n",
        "    return ax"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "id": "BD_CzViXTTMq",
        "outputId": "78b8dea0-036d-4556-bf25-82b939a79736"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:38: UserWarning: Attempted to set non-positive left xlim on a log-scaled axis.\n",
            "Invalid limit will be ignored.\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1440x252 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, axs = plt.subplots(1, 2)\n",
        "\n",
        "ax1, ax2 = axs[0], axs[1]\n",
        "\n",
        "enth_preQ, intr_preQ = get_cdf(auth_stats, \"phase1_to_phase2\")\n",
        "ax1.axvline(x=1.01, color=\"#838383\", ls=\":\", zorder=0)\n",
        "ax1 = paint_line_graph_log_zero_adjusted(enth_preQ, intr_preQ, ax1, \"phase1_to_phase2\", \"(a) Pre-Q and Q period user activity ratio\", False)\n",
        "ax1.annotate('', xy=(0, -0.16), xycoords='axes fraction', xytext=(0.59, -0.16),\n",
        "            arrowprops=dict(arrowstyle=\"->\", color='black'))\n",
        "ax1.annotate(\"More activity in Q period\", xy=(0.01, -0.29), xycoords = 'axes fraction', fontsize=20)\n",
        "ax1.annotate('', xy=(0.64, -0.16), xycoords='axes fraction', xytext=(1, -0.16), \n",
        "            arrowprops=dict(arrowstyle=\"<-\", color='black'))\n",
        "ax1.annotate(\"More activity in\", xy=(0.65, -0.29), xycoords='axes fraction', fontsize=20)\n",
        "ax1.annotate(\"pre-Q period\", xy=(0.65, -0.4), xycoords='axes fraction', fontsize=20)\n",
        "ax1.set_ylabel('\\% Users')\n",
        "ax1.set_yticks([0, 0.2, 0.4, 0.6, 0.8, 1.0])\n",
        "vals = ax1.get_yticks()\n",
        "ax1.set_yticklabels(['${:d}\\%$'.format(int(x * 100)) for x in vals])\n",
        "\n",
        "ax2.axvline(x=1.01, color=\"#838383\", ls=\":\", zorder=0)\n",
        "enth_postQ, intr_postQ = get_cdf(auth_stats, \"phase3_to_phase2\")\n",
        "ax2 = paint_line_graph_log_zero_adjusted(enth_postQ, intr_postQ, ax2, \"phase3_to_phase2\", \"(b) Post-Q and Q period user activity ratio\", True)\n",
        "\n",
        "ax2.annotate('', xy=(0, -0.16), xycoords='axes fraction', xytext=(0.59, -0.16),\n",
        "            arrowprops=dict(arrowstyle=\"->\", color='black'))\n",
        "ax2.annotate(\"More activity in Q period\", xy=(0.01, -0.29), xycoords='axes fraction', fontsize=20)\n",
        "ax2.annotate('', xy=(0.64, -0.16), xycoords='axes fraction', xytext=(1, -0.16), \n",
        "            arrowprops=dict(arrowstyle=\"<-\", color='black'))\n",
        "ax2.annotate(\"More activity in\", xy=(0.65, -0.29), xycoords='axes fraction', fontsize=20)\n",
        "ax2.annotate(\"post-Q period\", xy=(0.65, -0.4), xycoords='axes fraction', fontsize=20)\n",
        "ax2.set_yticks([0, 0.2, 0.4, 0.6, 0.8, 1.0])\n",
        "vals = ax2.get_yticks()\n",
        "ax2.set_yticklabels(['${:d}\\%$'.format(int(x * 100)) for x in vals])\n",
        "\n",
        "for ax in axs:\n",
        "    ax.xaxis.grid(color=\"#d3d3d3\", ls=\":\")\n",
        "    ax.yaxis.grid(color=\"#d3d3d3\", ls=\":\")\n",
        "    ax.set_xlim(0, 2000)\n",
        "\n",
        "fig.set_figwidth(20)\n",
        "fig.set_figheight(3.5)\n",
        "fig.savefig(outputPath + 'figs/user_activity_start_0.pdf', bbox_inches = 'tight')\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D-kVfR5Zr3cv",
        "outputId": "175e04b5-d5ed-4bc0-d453-c72d789b0a42"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.7129219229457893"
            ]
          },
          "execution_count": 57,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(enth_preQ[enth_preQ[\"phase1_to_phase2\"]==0.0]) / len(enth_preQ)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tNoaXblir3cv",
        "outputId": "4d438a40-b0a5-475b-cf7d-c1011de803ae"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.5534135125574814"
            ]
          },
          "execution_count": 58,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(intr_preQ[intr_preQ[\"phase1_to_phase2\"]==0.0]) / len(intr_preQ)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ATLab80r3cv",
        "outputId": "2ac00dab-17d3-4416-9d7b-db4be1899c0f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.3661779747698602"
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(enth_postQ[enth_postQ[\"phase3_to_phase2\"]==0.0]) / len(enth_postQ)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "USFNitZer3cv",
        "outputId": "dcae3adc-265a-4904-d4a8-cab90b1b7eff"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.3256101874778918"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(intr_postQ[intr_postQ[\"phase3_to_phase2\"]==0.0]) / len(intr_postQ)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S2imopay4Mq7"
      },
      "source": [
        "## CHARACTERIZING USER SPREAD"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GfwLA1EKV1zQ"
      },
      "source": [
        "### Utils and Processing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iCg9x3a_4Mq8"
      },
      "outputs": [],
      "source": [
        "\n",
        "def paint_bar_plot2(bars, seqs):\n",
        "  height = [[] for s in seqs]\n",
        "  before = -1\n",
        "  cnted = 0\n",
        "  bar_names = []\n",
        "  color = []\n",
        "\n",
        "  for ind, b in enumerate(bars):\n",
        "    for seqind, seq in enumerate(seqs):\n",
        "      cnt = 0\n",
        "      for s in seq:\n",
        "        if (s > before) and (s <= b):\n",
        "            cnt += 1\n",
        "      height[seqind].append(cnt)\n",
        "    before = b\n",
        "    bar_names.append('({}, {}]'.format(before, b))\n",
        "  for seqind, seq in enumerate(seqs):\n",
        "      cnt = 0\n",
        "      for s in seq:\n",
        "        if (s > b):\n",
        "            cnt += 1\n",
        "      height[seqind].append(cnt)\n",
        "  bar_names.append('> {}'.format(b))\n",
        "  print(chi2_contingency(height))\n",
        "  #height = [[h / float(len(seqs[ind])) for h in hh] for ind, hh in enumerate(height)]\n",
        "  y_pos = np.arange(len(bar_names))\n",
        "\n",
        "  return height, y_pos, bar_names"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3BVuJDGI4Mq8"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "def plot_rq2(labeled, types, subreddit_data, labels, title, filename, use_percentage=True):\n",
        "  bar_names = list(labeled.keys())\n",
        "  height = [[] for t in types]\n",
        "  yerr = [[] for t in types]\n",
        "  total = []\n",
        "  \n",
        "  for b in bar_names:\n",
        "    for tind, t in enumerate(types):\n",
        "      tmp = 0\n",
        "      for subreddit in labeled[b]:\n",
        "          tmp = tmp + subreddit_data[subreddit][t]\n",
        "      height[tind].append(tmp)\n",
        "  print(height)\n",
        "  print(chi2_contingency(height))\n",
        "  for tind, t in enumerate(types):\n",
        "    total = sum([sdata[t] for _, sdata in subreddit_data.items()])\n",
        "    if use_percentage:\n",
        "      height[tind] = [h / total for h in height[tind]]\n",
        " \n",
        "  y_pos = np.arange(len(bar_names))\n",
        "  return y_pos, bar_names, height, types"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rAcQvvHH4Mq9"
      },
      "outputs": [],
      "source": [
        "labeled_topic = defaultdict(list)\n",
        "labeled_relation = defaultdict(list)\n",
        "labeled = set()\n",
        "for _, row in subreddit_label_df.iterrows():\n",
        "    labeled.add(row['subreddit'])\n",
        "    for x in eval(row['Final Topic Clusters']):\n",
        "        labeled_topic[x].append(row['subreddit'])\n",
        "    for x in eval(row['Final Relation Clusters']):\n",
        "        labeled_relation[x].append(row['subreddit'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Cas8vaZw4Mq9"
      },
      "outputs": [],
      "source": [
        "q_interested = set(author_status[author_status['isUQ']==0]['QAuthor'].values)\n",
        "q_enthusiastic = set(author_status[author_status['isUQ']==1]['QAuthor'].values) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 696
        },
        "id": "CuV21CqlO-Gk",
        "outputId": "47607c1c-4876-4996-b018-2a059ad70c89"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-3d7ff542-08a2-45ab-b8c2-91e5800c4ef7\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>subreddit</th>\n",
              "      <th>numSubscribers</th>\n",
              "      <th>status</th>\n",
              "      <th>allModNames</th>\n",
              "      <th>allMods</th>\n",
              "      <th>qModNames</th>\n",
              "      <th>qMods</th>\n",
              "      <th>top_qModNames</th>\n",
              "      <th>top_qMods</th>\n",
              "      <th>firstPostSubmission</th>\n",
              "      <th>lastPostSubmission</th>\n",
              "      <th>firstPostComment</th>\n",
              "      <th>lastPostComment</th>\n",
              "      <th>qModsRatio</th>\n",
              "      <th>top_qModsRatio</th>\n",
              "      <th>activePreBanOnly</th>\n",
              "      <th>activePreQ</th>\n",
              "      <th>activePostBan</th>\n",
              "      <th>qAuth</th>\n",
              "      <th>top_qAuth</th>\n",
              "      <th>qSubmissions</th>\n",
              "      <th>top_qSubmissions</th>\n",
              "      <th>nonTop_qSubmissions</th>\n",
              "      <th>qComments</th>\n",
              "      <th>top_qComments</th>\n",
              "      <th>nonTop_qComments</th>\n",
              "      <th>top_qPercent</th>\n",
              "      <th>qPercent</th>\n",
              "      <th>Monthly Average Total Authors</th>\n",
              "      <th>Monthly Average Total Submissions</th>\n",
              "      <th>Monthly Average UQ Authors</th>\n",
              "      <th>Monthly Average UQ Submissions</th>\n",
              "      <th>Monthly Average QAnon Authors</th>\n",
              "      <th>Monthly Average QAnon Submissions</th>\n",
              "      <th>% UQ Submissions</th>\n",
              "      <th>% UQ Authors</th>\n",
              "      <th>% QAnon Submissions</th>\n",
              "      <th>% QAnon Authors</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Watches</td>\n",
              "      <td>1525243.0</td>\n",
              "      <td>public</td>\n",
              "      <td>['f1c355408b78fd88ebc13aade4c9a7924005c2ab','e...</td>\n",
              "      <td>13</td>\n",
              "      <td>[]</td>\n",
              "      <td>0.0</td>\n",
              "      <td>[]</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2016-12-07 03:21:16</td>\n",
              "      <td>2020-11-27 19:24:49</td>\n",
              "      <td>2016-11-26 13:24:54</td>\n",
              "      <td>2021-01-22 18:22:43</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>58</td>\n",
              "      <td>10</td>\n",
              "      <td>219</td>\n",
              "      <td>99</td>\n",
              "      <td>120</td>\n",
              "      <td>1681.0</td>\n",
              "      <td>244.0</td>\n",
              "      <td>1437.0</td>\n",
              "      <td>0.29</td>\n",
              "      <td>0.44</td>\n",
              "      <td>2881.384615</td>\n",
              "      <td>5911.846154</td>\n",
              "      <td>1.200000</td>\n",
              "      <td>2.40</td>\n",
              "      <td>3.750000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.040596</td>\n",
              "      <td>0.041647</td>\n",
              "      <td>0.101491</td>\n",
              "      <td>0.130146</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>MMA</td>\n",
              "      <td>1518451.0</td>\n",
              "      <td>public</td>\n",
              "      <td>['69e403df92bb49af60d5046c0be60f1a46bfd53d','d...</td>\n",
              "      <td>20</td>\n",
              "      <td>[]</td>\n",
              "      <td>0.0</td>\n",
              "      <td>[]</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2016-11-01 05:51:55</td>\n",
              "      <td>2021-01-03 00:43:44</td>\n",
              "      <td>2016-10-28 00:39:15</td>\n",
              "      <td>2021-01-23 08:16:57</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>97</td>\n",
              "      <td>29</td>\n",
              "      <td>371</td>\n",
              "      <td>99</td>\n",
              "      <td>272</td>\n",
              "      <td>18910.0</td>\n",
              "      <td>2808.0</td>\n",
              "      <td>16102.0</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.74</td>\n",
              "      <td>1840.461538</td>\n",
              "      <td>4953.461538</td>\n",
              "      <td>2.916667</td>\n",
              "      <td>4.75</td>\n",
              "      <td>6.384615</td>\n",
              "      <td>9.923077</td>\n",
              "      <td>0.095893</td>\n",
              "      <td>0.158475</td>\n",
              "      <td>0.200326</td>\n",
              "      <td>0.346903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Seattle</td>\n",
              "      <td>281450.0</td>\n",
              "      <td>public</td>\n",
              "      <td>['14a0b21d55a0415e5541be3389d27ee3b3232c90','f...</td>\n",
              "      <td>7</td>\n",
              "      <td>[]</td>\n",
              "      <td>0.0</td>\n",
              "      <td>[]</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2016-11-07 07:12:42</td>\n",
              "      <td>2021-01-18 23:13:14</td>\n",
              "      <td>2016-11-03 16:45:54</td>\n",
              "      <td>2021-01-21 14:56:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>53</td>\n",
              "      <td>13</td>\n",
              "      <td>188</td>\n",
              "      <td>99</td>\n",
              "      <td>89</td>\n",
              "      <td>1559.0</td>\n",
              "      <td>214.0</td>\n",
              "      <td>1345.0</td>\n",
              "      <td>0.37</td>\n",
              "      <td>0.40</td>\n",
              "      <td>732.769231</td>\n",
              "      <td>1140.692308</td>\n",
              "      <td>1.200000</td>\n",
              "      <td>1.20</td>\n",
              "      <td>3.272727</td>\n",
              "      <td>3.818182</td>\n",
              "      <td>0.105199</td>\n",
              "      <td>0.163762</td>\n",
              "      <td>0.334725</td>\n",
              "      <td>0.446625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>UnusAnnus</td>\n",
              "      <td>195890.0</td>\n",
              "      <td>quarantined/private</td>\n",
              "      <td>['f7fd9c68f804acda665d2ab082217bb1583318f2']</td>\n",
              "      <td>1</td>\n",
              "      <td>[]</td>\n",
              "      <td>0.0</td>\n",
              "      <td>[]</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-02-05 05:04:36</td>\n",
              "      <td>2020-11-14 05:24:35</td>\n",
              "      <td>2020-06-17 23:26:32</td>\n",
              "      <td>2020-11-13 09:04:06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>99</td>\n",
              "      <td>99</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GlobalOffensiveTrade</td>\n",
              "      <td>209007.0</td>\n",
              "      <td>public</td>\n",
              "      <td>['f78b47357eabba6f7580e47da7560a322287eaee','9...</td>\n",
              "      <td>22</td>\n",
              "      <td>[]</td>\n",
              "      <td>0.0</td>\n",
              "      <td>[]</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2016-12-02 06:33:48</td>\n",
              "      <td>2020-08-30 21:38:25</td>\n",
              "      <td>2017-04-05 12:44:22</td>\n",
              "      <td>2019-12-19 01:06:07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>122</td>\n",
              "      <td>99</td>\n",
              "      <td>23</td>\n",
              "      <td>86.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.05</td>\n",
              "      <td>2132.923077</td>\n",
              "      <td>17479.307692</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>19.00</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>0.108700</td>\n",
              "      <td>0.046884</td>\n",
              "      <td>0.108700</td>\n",
              "      <td>0.046884</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",
              "      <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>12982</th>\n",
              "      <td>atlanticdiscussions</td>\n",
              "      <td>1312.0</td>\n",
              "      <td>public</td>\n",
              "      <td>['bfcde1ace3566aca6c58b4e91864cf1dc3580838','2...</td>\n",
              "      <td>7</td>\n",
              "      <td>[]</td>\n",
              "      <td>0.0</td>\n",
              "      <td>[]</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2019-09-30 13:33:03</td>\n",
              "      <td>2020-10-02 14:24:23</td>\n",
              "      <td>2019-10-10 11:59:39</td>\n",
              "      <td>2020-12-21 12:56:28</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12983</th>\n",
              "      <td>AnkiVector</td>\n",
              "      <td>8162.0</td>\n",
              "      <td>public</td>\n",
              "      <td>['dba23e7b20b6aabac1013515b3966f5d8162dbac']</td>\n",
              "      <td>1</td>\n",
              "      <td>[]</td>\n",
              "      <td>0.0</td>\n",
              "      <td>[]</td>\n",
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              "\n",
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              "  "
            ],
            "text/plain": [
              "                  subreddit  ...  % QAnon Authors\n",
              "0                   Watches  ...         0.130146\n",
              "1                       MMA  ...         0.346903\n",
              "2                   Seattle  ...         0.446625\n",
              "3                 UnusAnnus  ...         0.000000\n",
              "4      GlobalOffensiveTrade  ...         0.046884\n",
              "...                     ...  ...              ...\n",
              "12982   atlanticdiscussions  ...         0.000000\n",
              "12983            AnkiVector  ...         0.000000\n",
              "12984         Modern_Family  ...         2.155887\n",
              "12985                 wacom  ...         0.542028\n",
              "12986             Lovecraft  ...         0.590171\n",
              "\n",
              "[12987 rows x 38 columns]"
            ]
          },
          "execution_count": 67,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "subreddit_stat"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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          "height": 424
        },
        "id": "_N9_vf1dO7XQ",
        "outputId": "dc65fd12-431c-424b-cbf8-aa7854160699"
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-95466446-788d-44a5-8ece-f2b490605d3c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                        QAuthor  ...  active_post_ban\n",
              "0      aa65b7dd5d5fa660d058e094669f884bf7d52299  ...            False\n",
              "1      2b1505f289338751829dfa129c0b52d145c9eceb  ...            False\n",
              "2      4eeddb9abeb3c4889f1b037016bf2aeb834bb66d  ...             True\n",
              "3      08a6fae5a56fcdb495b2de8a02625ea2b4abe32f  ...            False\n",
              "4      01301652214982c57efe894efe7e2c7d57df2801  ...            False\n",
              "...                                         ...  ...              ...\n",
              "13177  5ddab2d1ea3353cb0510f179223b285bb66eb124  ...            False\n",
              "13178  db80cc5bace90291c89e46788379da804a5efe5f  ...            False\n",
              "13179  bcde0448a3c0b584f218c01865a291036fa50d03  ...            False\n",
              "13180  eb6b2559a0745a82c3406f5c5d4b0bfd142a8314  ...             True\n",
              "13181  c039f11ff985f7d8547748c10ccd5c8416b8404d  ...             True\n",
              "\n",
              "[13182 rows x 7 columns]"
            ]
          },
          "execution_count": 68,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "author_status"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sFUZcbDL4Mq9"
      },
      "outputs": [],
      "source": [
        "subreddit_df = subreddit_stat[['subreddit', 'numSubscribers', 'status', 'allMods',\n",
        "       'qModNames', 'qMods']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "biLVWguB4Mq9",
        "outputId": "fe3c1489-04fc-4ab5-deb9-f92637c1237c"
      },
      "outputs": [],
      "source": [
        "subreddit_df['Q-enthusiactic Mods'] = subreddit_df.apply(lambda x: int(len([n for n in eval(x['qModNames'])\n",
        "                                                                        if n in q_enthusiastic]) > 0) if x['qMods'] else 0,\n",
        "                                                         axis=1)\n",
        "subreddit_df['Q-Interested Mods'] = subreddit_df.apply(lambda x: int(len([n for n in eval(x['qModNames'])\n",
        "                                                                      if n in q_interested])) > 0 if x['qMods'] else 0,\n",
        "                                                         axis=1) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gsCMsRSqAavp"
      },
      "outputs": [],
      "source": [
        "len(subreddit_df[(subreddit_df['Q-Interested Mods'] > 0)])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-7jg_B4L4Mq9",
        "outputId": "16076cd4-8720-4020-990c-616228b07c44"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "371"
            ]
          },
          "execution_count": 71,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(subreddit_df[subreddit_df['Q-enthusiactic Mods'] > 0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b63QlvIR4Mq-"
      },
      "outputs": [],
      "source": [
        "sub_df['date_created'] = sub_df['date_created'].dt.date"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Yyid8rK34Mq-"
      },
      "outputs": [],
      "source": [
        "activeSubs = set(subreddit_df['subreddit'].values)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d0Pik4rn4Mq-"
      },
      "outputs": [],
      "source": [
        "subreddit_q_submission_df = pd.merge(sub_df[sub_df.subreddit.isin(activeSubs)][['subreddit', 'id', 'author', \n",
        "                                                                       'date_created']],\n",
        "                                     author_status, left_on='author', right_on='QAuthor')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IUFjyE9S4Mq-",
        "outputId": "4d8a38e0-8fc4-4fdf-db90-d4e1b83946ab"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['subreddit', 'id', 'author', 'date_created', 'QAuthor', 'isUQ',\n",
              "       'status', 'first_post', 'last_post', 'active_pre_emergence',\n",
              "       'active_post_ban'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 75,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "subreddit_q_submission_df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0t_C9JCC4Mq_",
        "outputId": "195f43ea-1810-4570-8dcf-7cd9de0310d2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2466"
            ]
          },
          "execution_count": 76,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "subreddit_q_submission_df[(subreddit_q_submission_df.subreddit.isin(labeled)) & (subreddit_q_submission_df['isUQ'] == 1)]['author'].nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AxM64_CSMppk",
        "outputId": "ad3f79f9-2eca-4064-9945-031c065e52f6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "421908"
            ]
          },
          "execution_count": 77,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "subreddit_q_submission_df[(subreddit_q_submission_df.subreddit.isin(labeled)) & (subreddit_q_submission_df['isUQ'] == 0)]['id'].nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jVDA1jA44Mq_",
        "outputId": "877ab2d3-627c-4a9f-b5aa-e3cadb18be57"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "794423"
            ]
          },
          "execution_count": 78,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "subreddit_q_submission_df[(subreddit_q_submission_df.subreddit.isin(labeled)) & (subreddit_q_submission_df['isUQ'] == 1)]['id'].nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZkcWr-GTULxE",
        "outputId": "c3498647-0bcc-4b43-d875-79356c4c39fb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(397752, 13)"
            ]
          },
          "execution_count": 79,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "q_enth = author_status[author_status[\"isUQ\"]==1][\"QAuthor\"].tolist()\n",
        "sub_df[(sub_df[\"author\"].isin(q_enth)) & (sub_df[\"date_created\"] <= Q_ban) & (sub_df.subreddit.isin(labeled))].shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1dpY7ipmVpHD",
        "outputId": "ff13801e-794d-4711-ecf9-e17b8a190f8f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(396671, 13)"
            ]
          },
          "execution_count": 80,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sub_df[(sub_df[\"author\"].isin(q_enth)) & (sub_df[\"date_created\"] > Q_ban)& (sub_df.subreddit.isin(labeled))].shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Hzf82YW84Mq_"
      },
      "outputs": [],
      "source": [
        "qtypes = ['allq', 'uq', 'qauth']\n",
        "subreddit_q_sub_dfs = [subreddit_q_submission_df, \n",
        "                      subreddit_q_submission_df[subreddit_q_submission_df['isUQ'] == 1],\n",
        "                      subreddit_q_submission_df[subreddit_q_submission_df['isUQ'] == 0]]\n",
        "\n",
        "for ind, qtype in enumerate(qtypes):\n",
        "    curdf = subreddit_q_sub_dfs[ind]\n",
        "    tmp1 = curdf.groupby('subreddit')['id'].nunique().reset_index(name='{}_total_submissions'.format(qtype))\n",
        "    tmp2 = curdf[(curdf['date_created'] < Q_ban)].groupby('subreddit')['id'].nunique().reset_index(name='{}_pre_ban_submissions'.format(qtype))\n",
        "    tmp3 = curdf[(curdf['date_created'] > Q_ban)].groupby('subreddit')['id'].nunique().reset_index(name='{}_post_ban_submissions'.format(qtype))\n",
        "    subreddit_df = pd.merge(subreddit_df, tmp1, on='subreddit', how='left')\n",
        "    subreddit_df = pd.merge(subreddit_df, tmp2, on='subreddit', how='left')\n",
        "    subreddit_df = pd.merge(subreddit_df, tmp3, on='subreddit', how='left')\n",
        "\n",
        "    \n",
        "\n",
        "    tmp1 = curdf.groupby('subreddit')['author'].nunique().reset_index(name='{}_total_authors'.format(qtype))\n",
        "    tmp2 = curdf[(curdf['date_created'] < Q_ban)].groupby('subreddit')['author'].nunique().reset_index(name='{}_pre_ban_authors'.format(qtype))\n",
        "    tmp3 = curdf[(curdf['date_created'] > Q_ban)].groupby('subreddit')['author'].nunique().reset_index(name='{}_post_ban_authors'.format(qtype))\n",
        "    subreddit_df = pd.merge(subreddit_df, tmp1, on='subreddit', how='left')\n",
        "    subreddit_df = pd.merge(subreddit_df, tmp2, on='subreddit', how='left')\n",
        "    subreddit_df = pd.merge(subreddit_df, tmp3, on='subreddit', how='left')\n",
        "    subreddit_df = subreddit_df.fillna(0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "EB6w2tTE4MrA",
        "outputId": "d00da801-b8dc-47b6-a161-eb74e8449510"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-9e7bb934-2e53-4678-9688-01ad079288ba\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>subreddit</th>\n",
              "      <th>numSubscribers</th>\n",
              "      <th>status</th>\n",
              "      <th>allMods</th>\n",
              "      <th>qModNames</th>\n",
              "      <th>qMods</th>\n",
              "      <th>Q-enthusiactic Mods</th>\n",
              "      <th>Q-Interested Mods</th>\n",
              "      <th>allq_total_submissions</th>\n",
              "      <th>allq_pre_ban_submissions</th>\n",
              "      <th>allq_post_ban_submissions</th>\n",
              "      <th>allq_total_authors</th>\n",
              "      <th>allq_pre_ban_authors</th>\n",
              "      <th>allq_post_ban_authors</th>\n",
              "      <th>uq_total_submissions</th>\n",
              "      <th>uq_pre_ban_submissions</th>\n",
              "      <th>uq_post_ban_submissions</th>\n",
              "      <th>uq_total_authors</th>\n",
              "      <th>uq_pre_ban_authors</th>\n",
              "      <th>uq_post_ban_authors</th>\n",
              "      <th>qauth_total_submissions</th>\n",
              "      <th>qauth_pre_ban_submissions</th>\n",
              "      <th>qauth_post_ban_submissions</th>\n",
              "      <th>qauth_total_authors</th>\n",
              "      <th>qauth_pre_ban_authors</th>\n",
              "      <th>qauth_post_ban_authors</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>873</th>\n",
              "      <td>ChoosingBeggars</td>\n",
              "      <td>2054079.0</td>\n",
              "      <td>public</td>\n",
              "      <td>17</td>\n",
              "      <td>['4183eb50a6fae3dc3346221dea80c4cbab400d05']</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "      <td>131</td>\n",
              "      <td>29.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>44</td>\n",
              "      <td>17.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>77</td>\n",
              "      <td>3.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>10</td>\n",
              "      <td>2.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1612</th>\n",
              "      <td>Badfaketexts</td>\n",
              "      <td>368925.0</td>\n",
              "      <td>public</td>\n",
              "      <td>12</td>\n",
              "      <td>['4183eb50a6fae3dc3346221dea80c4cbab400d05']</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "      <td>27</td>\n",
              "      <td>17.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>19</td>\n",
              "      <td>12.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>6</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6.0</td>\n",
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              "    <tr>\n",
              "      <th>12271</th>\n",
              "      <td>QAnonCasualties</td>\n",
              "      <td>144992.0</td>\n",
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              "      <td>['ee57eb0cc28421c0caff91313467bb4f6549b8e1']</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "      <td>24</td>\n",
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              "      <td>7</td>\n",
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              "      <td>12.0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>12096</th>\n",
              "      <td>RachelCook</td>\n",
              "      <td>113055.0</td>\n",
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              "      <td>11</td>\n",
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              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>6855</th>\n",
              "      <td>antinatalism</td>\n",
              "      <td>101106.0</td>\n",
              "      <td>public</td>\n",
              "      <td>11</td>\n",
              "      <td>['d6093362c47839dca33618b52ed1c9783e61215c']</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>292</td>\n",
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              "      <td>4</td>\n",
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              "      <td>16.0</td>\n",
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              "      <td>9.0</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10483</th>\n",
              "      <td>TrueSkeptics</td>\n",
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              "      <td>public</td>\n",
              "      <td>5</td>\n",
              "      <td>['4946cc910a74f67f13ea879c5050ed0430258d7b','0...</td>\n",
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              "      <td>1</td>\n",
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              "      <td>2</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>3424</th>\n",
              "      <td>JustSoyBoyThings</td>\n",
              "      <td>111.0</td>\n",
              "      <td>public</td>\n",
              "      <td>3</td>\n",
              "      <td>['fab1eb2e251632265435cc80a6747fa002e234a6','c...</td>\n",
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              "      <td>1</td>\n",
              "      <td>True</td>\n",
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              "      <td>3.0</td>\n",
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              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>5983</th>\n",
              "      <td>islamic_immigration</td>\n",
              "      <td>108.0</td>\n",
              "      <td>public</td>\n",
              "      <td>1</td>\n",
              "      <td>['4183eb50a6fae3dc3346221dea80c4cbab400d05']</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
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              "      <td>3.0</td>\n",
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              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>11764</th>\n",
              "      <td>AussieConspiracy</td>\n",
              "      <td>107.0</td>\n",
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              "      <td>1</td>\n",
              "      <td>['e6c6b085cbec4c4e2ccd8e7ae692f58de52c108a']</td>\n",
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              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "      <td>149</td>\n",
              "      <td>149.0</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>149</td>\n",
              "      <td>149.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>7467</th>\n",
              "      <td>marktaylor</td>\n",
              "      <td>105.0</td>\n",
              "      <td>public</td>\n",
              "      <td>1</td>\n",
              "      <td>['33bd770bcd13a45ff5ce98b44f3f17d2f23690f3']</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "      <td>23</td>\n",
              "      <td>16.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>6</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>20</td>\n",
              "      <td>13.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>120 rows × 26 columns</p>\n",
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            ],
            "text/plain": [
              "                 subreddit  ...  qauth_post_ban_authors\n",
              "873        ChoosingBeggars  ...                    20.0\n",
              "1612          Badfaketexts  ...                     6.0\n",
              "12271      QAnonCasualties  ...                     8.0\n",
              "12096           RachelCook  ...                     0.0\n",
              "6855          antinatalism  ...                     9.0\n",
              "...                    ...  ...                     ...\n",
              "10483         TrueSkeptics  ...                     0.0\n",
              "3424      JustSoyBoyThings  ...                     0.0\n",
              "5983   islamic_immigration  ...                     0.0\n",
              "11764     AussieConspiracy  ...                     0.0\n",
              "7467            marktaylor  ...                     0.0\n",
              "\n",
              "[120 rows x 26 columns]"
            ]
          },
          "execution_count": 82,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "subreddit_df[(subreddit_df['Q-enthusiactic Mods'] > 0) & (subreddit_df['numSubscribers'] > 100)].sort_values(by='numSubscribers', ascending=False)"
      ]
    },
    {
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      "execution_count": null,
      "metadata": {
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        },
        "id": "P6VfTj624MrA",
        "outputId": "beaebaa6-8f24-46e9-a83e-4b02906bd9cb"
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      "outputs": [
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              "      <td>19</td>\n",
              "      <td>18.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8863</th>\n",
              "      <td>ReligiousFreedom</td>\n",
              "      <td>140.0</td>\n",
              "      <td>public</td>\n",
              "      <td>4</td>\n",
              "      <td>['dede0d83d76b788b973a74a27514b50c2beaef5c']</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>True</td>\n",
              "      <td>15</td>\n",
              "      <td>1.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10483</th>\n",
              "      <td>TrueSkeptics</td>\n",
              "      <td>112.0</td>\n",
              "      <td>public</td>\n",
              "      <td>5</td>\n",
              "      <td>['4946cc910a74f67f13ea879c5050ed0430258d7b','0...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3424</th>\n",
              "      <td>JustSoyBoyThings</td>\n",
              "      <td>111.0</td>\n",
              "      <td>public</td>\n",
              "      <td>3</td>\n",
              "      <td>['fab1eb2e251632265435cc80a6747fa002e234a6','c...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>4</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9981</th>\n",
              "      <td>DTubeVideos</td>\n",
              "      <td>105.0</td>\n",
              "      <td>public</td>\n",
              "      <td>1</td>\n",
              "      <td>['44e9f9e32c48c778c9e62b2aba5b127d2897b106']</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>True</td>\n",
              "      <td>9</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>106 rows × 26 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cdce18bf-97c0-448e-8b41-eede776b08bd')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cdce18bf-97c0-448e-8b41-eede776b08bd button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-cdce18bf-97c0-448e-8b41-eede776b08bd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "              subreddit  ...  qauth_post_ban_authors\n",
              "7130          worldnews  ...                   111.0\n",
              "7384      UpliftingNews  ...                    32.0\n",
              "3323           politics  ...                   294.0\n",
              "11964            trashy  ...                    79.0\n",
              "4637     CryptoCurrency  ...                    86.0\n",
              "...                 ...  ...                     ...\n",
              "1603         CBTS_Memes  ...                     0.0\n",
              "8863   ReligiousFreedom  ...                     1.0\n",
              "10483      TrueSkeptics  ...                     0.0\n",
              "3424   JustSoyBoyThings  ...                     0.0\n",
              "9981        DTubeVideos  ...                     1.0\n",
              "\n",
              "[106 rows x 26 columns]"
            ]
          },
          "execution_count": 83,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "subreddit_df[(subreddit_df['Q-Interested Mods'] > 0) & (subreddit_df['numSubscribers'] > 100)].sort_values(by='numSubscribers', ascending=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_9sxsRIV4MrA"
      },
      "outputs": [],
      "source": [
        "subreddit_df['participation'] = 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3W8i7bra4MrB"
      },
      "outputs": [],
      "source": [
        "subreddit_data = {}\n",
        "for _, row in subreddit_df.iterrows():\n",
        "    subreddit_data[row['subreddit']] = row"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4W1E_x9q4MrB",
        "outputId": "3a22e215-6ec4-46e9-b9ea-e6fcc714bbe1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "12987"
            ]
          },
          "execution_count": 86,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(subreddit_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pgl7vLbK4MrB",
        "outputId": "0ad4fea0-d9cb-446d-8a3e-1b83187202bb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "unrelated 629\n",
            "unconstricted behavior 73\n",
            "the unseen or unknown 83\n",
            "QAnon narrative 23\n",
            "current events 40\n",
            "politics - Trump opposing 25\n",
            "politics - Trump alignment unknown 33\n",
            "politics - Trump supporting 55\n",
            "constricted behavior 11\n",
            "unrelated 629\n",
            "sympathetic 145\n",
            "related-neutral 69\n",
            "QAnon 23\n",
            "related-misaligned 36\n",
            "related-aligned 56\n"
          ]
        }
      ],
      "source": [
        "labeled_sub_topics = set()\n",
        "for k, v in labeled_topic.items():\n",
        "    print(k, len(v))\n",
        "    for item in v:\n",
        "      labeled_sub_topics.add(item)\n",
        "    \n",
        "labeled_sub_relation = set()\n",
        "for k, v in labeled_relation.items():\n",
        "    print(k, len(v))\n",
        "    for item in v:\n",
        "      labeled_sub_relation.add(item)\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SvVidtCS4MrB"
      },
      "outputs": [],
      "source": [
        "labeled_topic_new = {'QAnon': labeled_topic['QAnon narrative'],\n",
        "                    'Current events': labeled_topic['current events'],\n",
        "                    'Anti-Trump': labeled_topic['politics - Trump opposing'],\n",
        "                    'Pro-Trump': labeled_topic['politics - Trump supporting'],\n",
        "                    'Trump (unknown)': labeled_topic['politics - Trump alignment unknown'],\n",
        "                    'Constricted behavior': labeled_topic['constricted behavior'],\n",
        "                    'Unconstricted behavior': labeled_topic['unconstricted behavior'],\n",
        "                    'Unseen/unknown': labeled_topic['the unseen or unknown'],\n",
        "                    'Unrelated': labeled_topic['unrelated']}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zHOmJHO34MrB"
      },
      "outputs": [],
      "source": [
        "labeled_relation_new = {'QAnon': labeled_relation['QAnon'],\n",
        "                    'Sympathetic': labeled_relation['sympathetic'],\n",
        "                    'Related~(aligned)': labeled_relation['related-aligned'],\n",
        "                    'Related~(neutral)': labeled_relation['related-neutral'],\n",
        "                    'Related~(misaligned)': labeled_relation['related-misaligned'],\n",
        "                    'Unrelated': labeled_topic['unrelated']}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bVF9G5564MrC",
        "outputId": "ffcdfec7-643b-4800-fa67-892ce8d3f8af"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "113"
            ]
          },
          "execution_count": 90,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(subreddit_df[(subreddit_df['Q-Interested Mods'] > 0)])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M8pr4zxd4MrC",
        "outputId": "c4257ffd-4b05-43a7-d944-ee88fb0c9e79"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "371"
            ]
          },
          "execution_count": 91,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(subreddit_df[(subreddit_df['Q-enthusiactic Mods'] > 0)])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6dM0Jnwg4MrC",
        "outputId": "ec0c7295-e870-4316-cd1f-e621729bad32"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[5, 9, 8, 11, 4, 2, 17, 20, 12]]\n",
            "(0.0, 1.0, 0, array([[ 5.,  9.,  8., 11.,  4.,  2., 17., 20., 12.]]))\n",
            "[[5, 35, 7, 13, 10, 12]]\n",
            "(0.0, 1.0, 0, array([[ 5., 35.,  7., 13., 10., 12.]]))\n"
          ]
        }
      ],
      "source": [
        "y_pos = [[], [], []]\n",
        "bar_names = [[], [], []]\n",
        "height = [[], [], []]\n",
        "types = [[], [], []]\n",
        "use_percentage = [False, False, True]\n",
        "labels = [['QAnon-enthusiastic users'], ['QAnon-enthusiastic users']]\n",
        "\n",
        "y_pos[0], bar_names[0], height[0], types[0] = plot_rq2(labeled_topic_new,\n",
        "                                                       ['Q-enthusiactic Mods'], subreddit_data,\n",
        "         ['participation'], 'Number of Subreddits',\n",
        "         'subscriber_numbers', use_percentage=False)\n",
        "\n",
        "y_pos[1], bar_names[1], height[1], types[1] = plot_rq2(labeled_relation_new,\n",
        "                                                       ['Q-enthusiactic Mods'], subreddit_data,\n",
        "         ['QAnon-enthusiastic', 'QAnon-interested'], 'Number of Mods',\n",
        "         'tmp', use_percentage=False)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UbkhQjSu4MrC",
        "outputId": "a0d9d47f-abb9-4cbc-848d-27aa85a1a915"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(171.8467567656926, 4.830041940342164e-38, 2, array([[ 60.23553719,  31.75206612,  21.01239669],\n",
            "       [197.76446281, 104.24793388,  68.98760331]]))\n"
          ]
        }
      ],
      "source": [
        "height[2], y_pos[2], bar_names[2] = paint_bar_plot2([100, 10000], \n",
        "                                          [subreddit_df[(subreddit_df['Q-Interested Mods'] > 0)]['numSubscribers'].values,\n",
        "                                           subreddit_df[(subreddit_df['Q-enthusiactic Mods'] > 0)]['numSubscribers'].values])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J7RuUe2YWwh-"
      },
      "source": [
        "### Fig. 4 Number of Subscribers & Labeled"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MzfQq5pe4MrD",
        "outputId": "d06fa8f0-f818-4a02-f750-dd3c92d5756d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[[7, 44, 62], [251, 92, 28]]"
            ]
          },
          "execution_count": 94,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "height[2]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1tuz8L1u4MrD",
        "outputId": "aa36b4de-51df-4db4-94e9-92f258b3b690"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "371"
            ]
          },
          "execution_count": 95,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "251 + 92 + 28"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mEflT4iD4MrD",
        "outputId": "137da2c8-a284-4f27-8339-9b903d26a3a2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "113"
            ]
          },
          "execution_count": 96,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "7 + 44 + 62"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "jGYAUpjl4MrE",
        "outputId": "d9bce59d-68d8-4f3c-9244-0c658de84fb7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[5, 9, 8, 11, 4, 2, 17, 20, 12]]\n",
            "[[5, 35, 7, 13, 10, 12]]\n"
          ]
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1800x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, axs = plt.subplots(1, 3, figsize=(25, 6), gridspec_kw={'width_ratios': [1, 1, 1]})\n",
        "\n",
        "hatches=[['', '/'], ['', '/'], ['', '/']]\n",
        "titles = ['(b)', '(c)']\n",
        "xlabels = ['Number of Subreddits', 'Number of Subreddits', 'Percentage of Subreddits']\n",
        "alphas = [0.3, 0.6, 0.8]\n",
        "plt.subplots_adjust(wspace = 0.8)\n",
        "\n",
        "for ind in range(2):\n",
        "    \n",
        "  if ind == 0:\n",
        "    ax = axs[1]\n",
        "  else:\n",
        "    ax = axs[2]\n",
        "  barwidth = 0.8 / len(types[ind])\n",
        "  pos_delta = -0.4 + barwidth / 2.\n",
        "  for tind, t in enumerate(types[ind]):\n",
        "    ax.barh(y_pos[ind] + pos_delta, height[ind][tind], barwidth, color=colors[1],\n",
        "           align='center', alpha=alphas[1], ecolor='black', hatch=hatches[ind][1])\n",
        "    pos_delta += barwidth\n",
        "  ax.invert_yaxis()  # labels read top-to-bottom\n",
        "  print(height[ind])\n",
        "\n",
        "  vals = ax.get_xticks()\n",
        "  if use_percentage[ind]:\n",
        "    ax.set_xticks(vals)\n",
        "    ax.set_xticklabels(['${:d}\\%$'.format(int(x * 100)) for x in vals], fontsize=25)\n",
        "  ax.tick_params(axis='x', labelsize=22)\n",
        "\n",
        "  ax.set_xlabel(xlabels[ind], fontsize=25)\n",
        "  #if len(labels[ind]) > 1:\n",
        "  #  ax.legend(labels=labels[ind], loc='upper right', fontsize=25)\n",
        "  ax.set_title(titles[ind])\n",
        "\n",
        "  ax.set_yticks(y_pos[ind])\n",
        "  ax.set_yticklabels(bar_names[ind], fontsize=25)\n",
        "#axs[2].set_visible(False)\n",
        "\n",
        "axs[0].bar(y_pos[2]-0.2, height[2][0], color=colors[1], align='center', alpha=0.3, ecolor='black', width=0.4)\n",
        "axs[0].bar(y_pos[2]+0.2, height[2][1], color=colors[1], align='center', alpha=0.6, ecolor='black',\n",
        "           width=0.4, hatch=\"/\")\n",
        "axs[0].tick_params(axis='y', labelsize=25)\n",
        "\n",
        "axs[0].set_xticks(y_pos[2])\n",
        "axs[0].set_xticklabels(['(1, 100]', '(100, 10K]', '$> 10$K'], fontsize=22)\n",
        "axs[0].set_ylabel('Subreddits with Q-users as Moderators', fontsize=25)\n",
        "axs[0].set_xlabel('Number of Subscribers', fontsize=25)\n",
        "\n",
        "axs[0].set_title('(a)', fontsize=25)\n",
        "\n",
        "\n",
        "  # Save the figure and show\n",
        "  #plt.tight_layout()\n",
        "for ax in [axs[0], axs[1], axs[2]]:\n",
        "  ax.xaxis.grid(color=\"#d3d3d3\", ls=\":\")\n",
        "  ax.yaxis.grid(color=\"#d3d3d3\", ls=\":\")\n",
        "axs[0].legend(labels=['QAnon-interested users', 'QAnon-enthusiastic users'], \n",
        "              fontsize=25, loc='lower right', bbox_to_anchor=(2.1, -0.36), ncol=2)\n",
        "\n",
        "# plt.savefig('figs/subreddit_mod.pdf', bbox_inches = 'tight')\n",
        "plt.savefig(outputPath + 'figs/subreddit_mod.pdf', bbox_inches = 'tight')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4CP52RAc4MrF",
        "outputId": "4bd72732-656b-4d00-d7c9-42f4cde0bdd9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "915"
            ]
          },
          "execution_count": 98,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(labeled)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lI7Bl7wrXAiH"
      },
      "source": [
        "### Fig. 2 Subreddits with different topic labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "DBd8qS5ofcDH",
        "outputId": "1952f8e6-71e0-490a-a172-4b91d702eae7"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-17a496f0-ec8e-4c4a-825c-4b8b4b646b8f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>subreddit</th>\n",
              "      <th>Codes</th>\n",
              "      <th>Final Topic Clusters</th>\n",
              "      <th>Final Relation Clusters</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2070vids</td>\n",
              "      <td>['unrelated']</td>\n",
              "      <td>['unrelated']</td>\n",
              "      <td>['unrelated']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3Dprinting</td>\n",
              "      <td>['unrelated']</td>\n",
              "      <td>['unrelated']</td>\n",
              "      <td>['unrelated']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3Dprintmything</td>\n",
              "      <td>['unrelated']</td>\n",
              "      <td>['unrelated']</td>\n",
              "      <td>['unrelated']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>49ers</td>\n",
              "      <td>['unrelated']</td>\n",
              "      <td>['unrelated']</td>\n",
              "      <td>['unrelated']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4KMobileWallpapers</td>\n",
              "      <td>['unrelated']</td>\n",
              "      <td>['unrelated']</td>\n",
              "      <td>['unrelated']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>910</th>\n",
              "      <td>xboxone</td>\n",
              "      <td>['unrelated']</td>\n",
              "      <td>['unrelated']</td>\n",
              "      <td>['unrelated']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>911</th>\n",
              "      <td>yokaiwatch</td>\n",
              "      <td>['unrelated']</td>\n",
              "      <td>['unrelated']</td>\n",
              "      <td>['unrelated']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>912</th>\n",
              "      <td>youngstown</td>\n",
              "      <td>['unrelated']</td>\n",
              "      <td>['unrelated']</td>\n",
              "      <td>['unrelated']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>913</th>\n",
              "      <td>zen</td>\n",
              "      <td>['metaphysical/wellness']</td>\n",
              "      <td>['the unseen or unknown']</td>\n",
              "      <td>['related-aligned']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>914</th>\n",
              "      <td>zorinos</td>\n",
              "      <td>['unrelated']</td>\n",
              "      <td>['unrelated']</td>\n",
              "      <td>['unrelated']</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>915 rows × 4 columns</p>\n",
              "</div>\n",
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              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "              subreddit  ... Final Relation Clusters\n",
              "0              2070vids  ...           ['unrelated']\n",
              "1            3Dprinting  ...           ['unrelated']\n",
              "2        3Dprintmything  ...           ['unrelated']\n",
              "3                 49ers  ...           ['unrelated']\n",
              "4    4KMobileWallpapers  ...           ['unrelated']\n",
              "..                  ...  ...                     ...\n",
              "910             xboxone  ...           ['unrelated']\n",
              "911          yokaiwatch  ...           ['unrelated']\n",
              "912          youngstown  ...           ['unrelated']\n",
              "913                 zen  ...     ['related-aligned']\n",
              "914             zorinos  ...           ['unrelated']\n",
              "\n",
              "[915 rows x 4 columns]"
            ]
          },
          "execution_count": 99,
          "metadata": {},
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        }
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      "source": [
        "subreddit_label_df"
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    {
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              "      <td>The_Donald</td>\n",
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              "      <td>/r/The_Donald/comments/ds1ywa/liberal_leo_putt...</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ],
            "text/plain": [
              "              subreddit      id  ...  date_created        type\n",
              "0        greatawakening  8xuv4i  ...    2018-07-11  submission\n",
              "1        greatawakening  8ydw3e  ...    2018-07-12  submission\n",
              "2        greatawakening  8ytwg0  ...    2018-07-14  submission\n",
              "3        greatawakening  8ytx4z  ...    2018-07-14  submission\n",
              "4        greatawakening  8yvgwt  ...    2018-07-14  submission\n",
              "...                 ...     ...  ...           ...         ...\n",
              "2099870      The_Donald  dqrnbl  ...    2019-11-02  submission\n",
              "2099871      The_Donald  drjrgx  ...    2019-11-04  submission\n",
              "2099872      The_Donald  drk6ad  ...    2019-11-04  submission\n",
              "2099873      The_Donald  ds0yc7  ...    2019-11-05  submission\n",
              "2099874      The_Donald  ds1ywa  ...    2019-11-05  submission\n",
              "\n",
              "[2099875 rows x 13 columns]"
            ]
          },
          "execution_count": 100,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sub_df"
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    },
    {
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        "outputId": "dee09071-ca60-4852-99aa-9217cc979948"
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        {
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          "text": [
            "[[23, 40, 25, 55, 33, 11, 73, 83, 629]]\n",
            "(0.0, 1.0, 0, array([[ 23.,  40.,  25.,  55.,  33.,  11.,  73.,  83., 629.]]))\n",
            "[[8245.0, 38224.0, 4540.0, 219754.0, 33778.0, 4004.0, 39623.0, 34939.0, 77468.0], [25871, 81383, 12570, 406607, 42502, 2029, 235045, 139941, 85155]]\n",
            "(82077.32399811488, 0.0, 8, array([[ 10533.75909546,  36930.21820058,   5282.93522463,\n",
            "        193397.11222864,  23552.44295351,   1862.76728289,\n",
            "         84807.32041365,  53996.47645135,  50211.96814929],\n",
            "       [ 23582.24090454,  82676.78179942,  11827.06477537,\n",
            "        432963.88777136,  52727.55704649,   4170.23271711,\n",
            "        189860.67958635, 120883.52354865, 112411.03185071]]))\n",
            "[[21184.0, 38983.0, 4680.0, 201765.0, 20728.0, 649.0, 91999.0, 68071.0, 40669.0], [4666.0, 42271.0, 7858.0, 204205.0, 21713.0, 1371.0, 142664.0, 71739.0, 44366.0]]\n",
            "(20398.209862724358, 0.0, 8, array([[ 12270.64096948,  38570.16098005,   5951.61688493,\n",
            "        192708.39900892,  20146.16144626,    958.86633495,\n",
            "        111391.31225615,  66365.89222218,  40364.94989709],\n",
            "       [ 13579.35903052,  42683.83901995,   6586.38311507,\n",
            "        213261.60099108,  22294.83855374,   1061.13366505,\n",
            "        123271.68774385,  73444.10777782,  44670.05010291]]))\n"
          ]
        }
      ],
      "source": [
        "y_pos = [[], [], []]\n",
        "bar_names = [[], [], []]\n",
        "height = [[], [], []]\n",
        "types = [[], [], []]\n",
        "use_percentage = [False, True, True]\n",
        "tmp_subreddit_data = {k:v for k, v in subreddit_data.items() if k in labeled_sub_topics}\n",
        "labels = [['participation'], ['QAnon-interested users', 'QAnon-enthusiastic users'], ['Pre-ban', 'Post-ban']]\n",
        "\n",
        "y_pos[0], bar_names[0], height[0], types[0] = plot_rq2(labeled_topic_new, ['participation'], subreddit_data,\n",
        "         ['participation'], 'Number of Subreddits',\n",
        "         'subscriber_numbers', use_percentage=False)\n",
        "\n",
        "y_pos[1], bar_names[1], height[1], types[1] = plot_rq2(labeled_topic_new,\n",
        "         ['qauth_total_submissions', 'uq_total_submissions'], tmp_subreddit_data,\n",
        "         ['Q-enthusiastic', 'Q-interested'], 'Number of Mods',\n",
        "         'tmp', use_percentage=True)\n",
        "\n",
        "y_pos[2], bar_names[2], height[2], types[2] = plot_rq2(labeled_topic_new, \n",
        "         ['uq_pre_ban_submissions', 'uq_post_ban_submissions'], tmp_subreddit_data,\n",
        "         ['pre ban', 'post ban'], 'Average Number of Subscribers',\n",
        "         'tmp', use_percentage=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "pCiKhLDP4MrG",
        "outputId": "15cdd4a4-c952-42b8-f3a7-3f93075f5a4d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[23, 40, 25, 55, 33, 11, 73, 83, 629]]\n",
            "[[0.01954217507134257, 0.09059795026403861, 0.010760639760326896, 0.520857627729268, 0.0800601078908198, 0.009490220616816937, 0.09391383903599837, 0.08281189264010164, 0.1836134891966969], [0.03256577415306455, 0.10244290510219367, 0.01582280472745628, 0.5118268227380124, 0.053500465117450026, 0.0025540549556092914, 0.29586882555011623, 0.1761542654228289, 0.10719100529566743]]\n",
            "[[0.053409575098390706, 0.09828481240844811, 0.011799320782688279, 0.5086944354100642, 0.05225989768879544, 0.0016362733307616864, 0.23194993860823482, 0.1716221292731568, 0.10253559335708325], [0.01176289670785109, 0.10656438206977571, 0.019809867623294872, 0.5147968971767535, 0.05473805748340564, 0.0034562647634941803, 0.35965321387245347, 0.1808526461475631, 0.1118458369782515]]\n"
          ]
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1800x576 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, axs = plt.subplots(1, 3, figsize=(25, 8), gridspec_kw={'width_ratios': [1, 1, 1]})\n",
        "plt.subplots_adjust(wspace = 0.1)\n",
        "\n",
        "hatches=[[''], ['', '/'], ['', '/']]\n",
        "titles = ['(a)', '(b)', '(c)']\n",
        "xlabels = ['Number of Subreddits', 'Percentage of Submissions', 'Percentage of Submissions']\n",
        "alphas = [0.3, 0.6, 0.8]\n",
        "\n",
        "for ind in range(3):\n",
        "  barwidth = 0.8 / len(types[ind])\n",
        "  pos_delta = -0.4 + barwidth / 2.\n",
        "  for tind, t in enumerate(types[ind]):\n",
        "    axs[ind].barh(y_pos[ind] + pos_delta, height[ind][tind], barwidth, color=colors[ind],\n",
        "           align='center', alpha=alphas[tind], ecolor='black', hatch=hatches[ind][tind])\n",
        "    pos_delta += barwidth\n",
        "  axs[ind].invert_yaxis()  # labels read top-to-bottom\n",
        "  print(height[ind])\n",
        "\n",
        "  vals = axs[ind].get_xticks()\n",
        "  if use_percentage[ind]:\n",
        "    axs[ind].set_xticks(vals)\n",
        "    axs[ind].set_xticklabels(['${:d}\\%$'.format(int(x * 100)) for x in vals], fontsize=25)\n",
        "  axs[ind].tick_params(axis='x', labelsize=25)\n",
        "\n",
        "  axs[ind].set_xlabel(xlabels[ind], fontsize=25)\n",
        "  if len(labels[ind]) > 1:\n",
        "    axs[ind].legend(labels=labels[ind], loc='upper right', fontsize=25)\n",
        "  axs[ind].set_title(titles[ind])\n",
        "  if ind:\n",
        "    axs[ind].set_yticklabels([], fontsize=20)\n",
        "\n",
        "\n",
        "axs[0].set_yticks(y_pos[ind])\n",
        "axs[0].set_yticklabels(bar_names[ind], fontsize=25)\n",
        "\n",
        "for ax in axs:\n",
        "  ax.xaxis.grid(color=\"#d3d3d3\", ls=\":\")\n",
        "  ax.yaxis.grid(color=\"#d3d3d3\", ls=\":\")\n",
        "\n",
        "plt.savefig(outputPath + 'figs/subreddit_topic.pdf', bbox_inches = 'tight')\n",
        "# plt.savefig('figs/subreddit_topic.pdf', bbox_inches = 'tight')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3vPlLG0gXGqy"
      },
      "source": [
        "### Fig. 3 Subreddits with different relation labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-gBfwEFo9kJP",
        "outputId": "ceee5a5f-86dc-4c1c-dfc5-5e2fcc30912d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1444019.0"
            ]
          },
          "execution_count": 103,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "8245.0+270756.0+18394.0+ 55285.0+8544.0+ 77468.0 + 25871 + 605456+155783+118463+14599+ 85155"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F2NvtMAc-bTw",
        "outputId": "42cb18df-2141-4d49-9997-ad3dc67c0765"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1003887.0"
            ]
          },
          "execution_count": 104,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "21184.0+ 298771.0+ 57723.0+ 56395.0+ 5329.0+ 40669.0+4666.0+ 305823.0+ 97838.0+ 61894.0+ 9229.0+ 44366.0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3FVXT-dA4MrH",
        "outputId": "dbe0ef14-e009-4d1d-cdfa-c925c8ac25c8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[23, 145, 56, 69, 36, 629]]\n",
            "(0.0, 1.0, 0, array([[ 23., 145.,  56.,  69.,  36., 629.]]))\n",
            "[[8245.0, 270756.0, 18394.0, 55285.0, 8544.0, 77468.0], [25871, 605456, 155783, 118463, 14599, 85155]]\n",
            "(56617.7481864486, 0.0, 5, array([[ 10364.41783107, 266192.61568165,  52914.85533362,\n",
            "         52784.52542245,   7030.82781875,  49404.75791247],\n",
            "       [ 23751.58216893, 610019.38431835, 121262.14466638,\n",
            "        120963.47457755,  16112.17218125, 113218.24208753]]))\n",
            "[[21184.0, 298771.0, 57723.0, 56395.0, 5329.0, 40669.0], [4666.0, 305823.0, 97838.0, 61894.0, 9229.0, 44366.0]]\n",
            "(20575.749914161355, 0.0, 5, array([[ 12361.78509135, 289124.22032958,  74391.16636733,\n",
            "         56567.2416507 ,   6961.81305067,  40664.77351037],\n",
            "       [ 13488.21490865, 315469.77967042,  81169.83363267,\n",
            "         61721.7583493 ,   7596.18694933,  44370.22648963]]))\n"
          ]
        }
      ],
      "source": [
        "y_pos = [[], [], []]\n",
        "bar_names = [[], [], []]\n",
        "height = [[], [], []]\n",
        "types = [[], [], []]\n",
        "use_percentage = [False, True, True]\n",
        "tmp_subreddit_data = {k: v for k, v in subreddit_data.items() if k in labeled_sub_relation}\n",
        "labels = [['participation'], ['QAnon-interested users', 'QAnon-enthusiastic users'], ['Pre-ban', 'Post-ban']]\n",
        "\n",
        "y_pos[0], bar_names[0], height[0], types[0] = plot_rq2(labeled_relation_new, ['participation'], subreddit_data,\n",
        "         ['participation'], 'Number of Subreddits',\n",
        "         'subscriber_numbers', use_percentage=False)\n",
        "\n",
        "y_pos[1], bar_names[1], height[1], types[1] = plot_rq2(labeled_relation_new,['qauth_total_submissions', 'uq_total_submissions'],\n",
        "                                                       tmp_subreddit_data,\n",
        "         ['Q-enthusiastic', 'Q-interested'], 'Number of Mods',\n",
        "         'tmp', use_percentage=True)\n",
        "\n",
        "y_pos[2], bar_names[2], height[2], types[2] = plot_rq2(labeled_relation_new, ['uq_pre_ban_submissions', 'uq_post_ban_submissions'],\n",
        "                                                       tmp_subreddit_data,\n",
        "         ['pre ban', 'post ban'], 'Average Number of Subscribers',\n",
        "         'tmp', use_percentage=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "id": "VG4ueDYg4MrH",
        "outputId": "8466a5d1-6ac6-42a8-8418-d7017b2ce310"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[23, 145, 56, 69, 36, 629]]\n",
            "[[0.01954217507134257, 0.6417418015301914, 0.04359718232410857, 0.13103567602415692, 0.020250860377143833, 0.1836134891966969], [0.03256577415306455, 0.762133019814381, 0.1960957827253239, 0.14911829088533438, 0.01837685968306557, 0.10719100529566743]]\n",
            "[[0.053409575098390706, 0.7532681345223418, 0.1455325199869905, 0.14218433665378322, 0.013435594113449965, 0.10253559335708325], [0.01176289670785109, 0.7709739305368933, 0.24664772569711424, 0.15603358954902175, 0.02326613238678905, 0.1118458369782515]]\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1800x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, axs = plt.subplots(1, 3, figsize=(25, 6), gridspec_kw={'width_ratios': [1, 1.1, 1]})\n",
        "plt.subplots_adjust(wspace = 0.1)\n",
        "\n",
        "hatches=[[''], ['', '/'], ['', '/']]\n",
        "titles = ['(a)', '(b)', '(c)']\n",
        "xlabels = ['Number of Subreddits', 'Percentage of Submissions', 'Percentage of Submissions']\n",
        "alphas = [0.3, 0.6, 0.8]\n",
        "\n",
        "for ind in range(3):\n",
        "  barwidth = 0.8 / len(types[ind])\n",
        "  pos_delta = -0.4 + barwidth / 2.\n",
        "  for tind, t in enumerate(types[ind]):\n",
        "    axs[ind].barh(y_pos[ind] + pos_delta, height[ind][tind], barwidth, color=colors[ind],\n",
        "           align='center', alpha=alphas[tind], ecolor='black', hatch=hatches[ind][tind])\n",
        "    pos_delta += barwidth\n",
        "  axs[ind].invert_yaxis()  # labels read top-to-bottom\n",
        "\n",
        "  vals = axs[ind].get_xticks()\n",
        "  if use_percentage[ind]:\n",
        "    axs[ind].set_xticks(vals)\n",
        "\n",
        "    axs[ind].set_xticklabels(['${:d}\\%$'.format(int(x * 100)) for x in vals], fontsize=25)\n",
        "  axs[ind].tick_params(axis='x', labelsize=25)\n",
        "\n",
        "  axs[ind].set_xlabel(xlabels[ind], fontsize=25)\n",
        "  if len(labels[ind]) > 1:\n",
        "    axs[ind].legend(labels=labels[ind], loc='lower right', fontsize=25)\n",
        "  axs[ind].set_title(titles[ind])\n",
        "  print(height[ind])\n",
        "  if ind:\n",
        "    axs[ind].set_yticklabels([], fontsize=25)\n",
        "\n",
        "\n",
        "\n",
        "axs[0].set_yticks(y_pos[ind])\n",
        "axs[0].set_yticklabels(bar_names[ind], fontsize=25)\n",
        "\n",
        "for ax in axs:\n",
        "  ax.xaxis.grid(color=\"#d3d3d3\", ls=\":\")\n",
        "  ax.yaxis.grid(color=\"#d3d3d3\", ls=\":\")\n",
        "\n",
        "plt.savefig(outputPath + 'figs/subreddit_relation.pdf', bbox_inches = 'tight')\n",
        "# plt.savefig('figs/subreddit_relation.pdf', bbox_inches = 'tight')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BULyC3U64MrH"
      },
      "source": [
        "## QANON-ENTHUSIASTIC USERS’ ACTIVITIES IN OTHER COMMUNITIES"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QUAwOaMYXXAZ"
      },
      "source": [
        "### Fig. 5 Types of submissions in subreddits"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d05k1Sht4MrI"
      },
      "outputs": [],
      "source": [
        "submission_df= []\n",
        "# datadir = \"/data/yiqing/QReddit/RQ3_Labelling Results\"\n",
        "datadir = inputPath + \"RQ3_LabellingResults\"\n",
        "for filename in os.listdir(datadir):\n",
        "    with open(os.path.join(datadir, filename), \"r\") as r:\n",
        "        tmp = pd.read_csv(r)\n",
        "    submission_df.append(tmp)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vCbZAoP_4MrI"
      },
      "outputs": [],
      "source": [
        "submission_df = pd.concat(submission_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FpH_1L4y4MrI",
        "outputId": "c00ec0a0-d48e-4a84-c362-6da8dcd9d13b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1571"
            ]
          },
          "execution_count": 109,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(submission_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NCEZ74De4MrL",
        "outputId": "9d07a9ca-2310-4ade-edac-314d98b63388"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "233"
            ]
          },
          "execution_count": 110,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "submission_df.subreddit.nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HZH0l_IR4MrN"
      },
      "outputs": [],
      "source": [
        "def plot_rq3(labeled, types, subreddit_data, totalcnt, use_percentage=True):\n",
        "  bar_names = list(labeled.keys())\n",
        "  height = [[] for t in types]\n",
        "  yerr = [[] for t in types]\n",
        "  total = {}\n",
        "  \n",
        "  for bind, b in enumerate(bar_names):\n",
        "    total[bind] = 0\n",
        "    for subreddit in labeled[b]:\n",
        "      if subreddit not in totalcnt:\n",
        "        continue\n",
        "      total[bind] += totalcnt[subreddit] \n",
        "    #print(b, total[bind])\n",
        "    for tind, t in enumerate(types):\n",
        "      tmp = 0\n",
        "      for subreddit in labeled[b]:\n",
        "          if subreddit not in subreddit_data:\n",
        "            continue\n",
        "          tmp = tmp + subreddit_data[subreddit][t]\n",
        "      height[tind].append(tmp)\n",
        "  #print(height)\n",
        "  print(chi2_contingency(height))\n",
        "  for tind, t in enumerate(types):\n",
        "    if use_percentage:\n",
        "      height[tind] = [h / total[bind] for bind, h in enumerate(height[tind])]\n",
        "  y_pos = np.arange(len(bar_names))\n",
        "  return y_pos, bar_names, height, types"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oAwtbFR54MrO"
      },
      "outputs": [],
      "source": [
        "submission_data = {}\n",
        "total = defaultdict(int)\n",
        "labels = set()\n",
        "for _, row in submission_df.iterrows():\n",
        "    if row['subreddit'] not in submission_data:\n",
        "        submission_data[row['subreddit']] = defaultdict(int)\n",
        "    for entryind in range(1, 5):\n",
        "        entry = 'Reconcile Code{}'.format(entryind)\n",
        "        if type(row[entry]) is str:\n",
        "          submission_data[row['subreddit']][row[entry]] += 1\n",
        "          labels.add(row[entry])\n",
        "    total[row['subreddit']] += 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tDcciPTR4MrO",
        "outputId": "feaed5ff-eb69-4bd5-acf2-918601edb92b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'Conspiracy-Other',\n",
              " 'Conspiracy-Politics',\n",
              " 'Hate Speech',\n",
              " 'Low-quality Source',\n",
              " 'Negative Interaction',\n",
              " 'QAnon',\n",
              " 'Unrelated'}"
            ]
          },
          "execution_count": 114,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Ug6fclK4MrP",
        "outputId": "35029d83-7abd-4f22-8a6f-ced1e4efa494"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "dict_keys(['Conspiracy-Politics', 'Unrelated', 'Conspiracy-Other', 'QAnon', 'Low-quality Source', 'Negative Interaction'])"
            ]
          },
          "execution_count": 115,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "submission_data['CoincidenceTheorist'].keys()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pq8wmRQs4MrP",
        "outputId": "e813d94b-c469-4fe6-b09c-b25e8d354f1a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(897.2245330737379, 2.3425718910858582e-169, 30, array([[  9.44173192,  22.83740212,  11.69138646,  12.64578535,\n",
            "          8.82818977,   8.55550438],\n",
            "       [ 14.67296177,  35.49055735,  18.16904652,  19.65223399,\n",
            "         13.71948411,  13.29571626],\n",
            "       [ 17.35237218,  41.97144173,  21.48687241,  23.24090281,\n",
            "         16.22478121,  15.72362966],\n",
            "       [  1.65868263,   4.01197605,   2.05389222,   2.22155689,\n",
            "          1.5508982 ,   1.50299401],\n",
            "       [103.60386918, 250.59419622, 128.28926762, 138.76186089,\n",
            "         96.87148779,  93.87931829],\n",
            "       [ 18.75587287,  45.3661907 ,  23.22478121,  25.12068171,\n",
            "         17.53707969,  16.99539383],\n",
            "       [111.51450944, 269.72823584, 138.08475357, 149.35697835,\n",
            "        104.26807923, 101.04744357]]))\n",
            "(1086.0509606131359, 4.722364781425742e-196, 48, array([[  8.87978629,   8.81567231,   6.15494212,  13.11130899,\n",
            "          4.77649154,   2.14781834,  15.80409617,   4.2635797 ,\n",
            "          8.04630454],\n",
            "       [ 14.67631345,  14.57034728,  10.17275156,  21.67008014,\n",
            "          7.89447907,   3.54986643,  26.12065895,   7.04674978,\n",
            "         13.29875334],\n",
            "       [ 17.63624221,  17.50890472,  12.22439893,  26.04051647,\n",
            "          9.48664292,   4.26580588,  31.38869101,   8.46794301,\n",
            "         15.98085485],\n",
            "       [  1.60329475,   1.59171861,   1.11130899,   2.36731968,\n",
            "          0.86242208,   0.38780053,   2.85351736,   0.769813  ,\n",
            "          1.45280499],\n",
            "       [102.36420303, 101.62511131,  70.95280499, 151.14425646,\n",
            "         55.06233304,  24.75957257, 182.18610864,  49.14959929,\n",
            "         92.75601069],\n",
            "       [ 18.25289403,  18.12110419,  12.65182547,  26.95102404,\n",
            "          9.81834372,   4.41495993,  32.48619768,   8.76402493,\n",
            "         16.539626  ],\n",
            "       [113.58726625, 112.76714159,  78.73196794, 167.71549421,\n",
            "         61.09928762,  27.47417631, 202.16073019,  54.53829029,\n",
            "        102.92564559]]))\n"
          ]
        }
      ],
      "source": [
        "y_pos = [[], [], [], []]\n",
        "bar_names = [[], [], [], []]\n",
        "height = [[], [], [], []]\n",
        "types = [[], [], [], []]\n",
        "use_percentage = [True, True, True, True]\n",
        "labels = [['Conspiracy-Other',\n",
        " 'Conspiracy-Politics',\n",
        " 'Negative Interaction','Hate Speech', 'Low-quality Source', \n",
        " 'QAnon', 'Unrelated'], ['Conspiracy-Other',\n",
        " 'QAnon'], ['Conspiracy-Other',\n",
        " 'Conspiracy-Politics',\n",
        " 'Negative Interaction','Hate Speech','Low-quality Source',\n",
        " 'QAnon']]\n",
        "\n",
        "y_pos[0], bar_names[0], height[0], types[0] = plot_rq3(labeled_relation_new, labels[0], submission_data,\n",
        "                                                       total, use_percentage=True)\n",
        "\n",
        "y_pos[1], bar_names[1], height[1], types[1] = plot_rq3(labeled_topic_new, labels[0], submission_data,\n",
        "                                                       total, use_percentage=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rd6c3Wnn4MrP",
        "outputId": "1d33e87d-7555-4d4e-8c90-6a1123944af1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[[0.11952191235059761,\n",
              "  0.02821316614420063,\n",
              "  0.0425531914893617,\n",
              "  0.01662049861495845,\n",
              "  0.01968503937007874,\n",
              "  0.004],\n",
              " [0.12350597609561753,\n",
              "  0.050156739811912224,\n",
              "  0.04559270516717325,\n",
              "  0.038781163434903045,\n",
              "  0.08661417322834646,\n",
              "  0.004],\n",
              " [0.08764940239043825,\n",
              "  0.08150470219435736,\n",
              "  0.0547112462006079,\n",
              "  0.055401662049861494,\n",
              "  0.07874015748031496,\n",
              "  0.016],\n",
              " [0.01593625498007968,\n",
              "  0.007836990595611285,\n",
              "  0.0060790273556231,\n",
              "  0.002770083102493075,\n",
              "  0.003937007874015748,\n",
              "  0.0],\n",
              " [0.13147410358565736,\n",
              "  0.5532915360501567,\n",
              "  0.6474164133738601,\n",
              "  0.42382271468144045,\n",
              "  0.1732283464566929,\n",
              "  0.064],\n",
              " [0.3426294820717131,\n",
              "  0.047021943573667714,\n",
              "  0.030395136778115502,\n",
              "  0.0332409972299169,\n",
              "  0.015748031496062992,\n",
              "  0.02],\n",
              " [0.28286852589641437,\n",
              "  0.28213166144200624,\n",
              "  0.21580547112462006,\n",
              "  0.45706371191135736,\n",
              "  0.6417322834645669,\n",
              "  0.896]]"
            ]
          },
          "execution_count": 117,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "height[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jIt0y7_74MrP"
      },
      "outputs": [],
      "source": [
        "bar_names_new = [['QAnon',\n",
        " 'Sympathetic',      \n",
        " 'Related\\n(aligned)',\n",
        " 'Related\\n(neutral)',\n",
        " 'Related\\n(misaligned)',\n",
        " 'Unrelated'],\n",
        " ['QAnon',\n",
        " 'Current\\nevents',\n",
        " 'Anti-\\nTrump',\n",
        " 'Pro-\\nTrump',\n",
        " 'Trump\\nunknown',\n",
        " 'Constricted\\nbehavior',\n",
        " 'Unconstricted\\nbehavior',\n",
        " 'Unseen\\n/unknown',\n",
        " 'Unrelated']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "id": "SsnA-O4O4MrP",
        "outputId": "2adb1b26-56a7-4c13-a659-82d0a7fb4df3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[0.11952191235059761, 0.02821316614420063, 0.0425531914893617, 0.01662049861495845, 0.01968503937007874, 0.004], [0.12350597609561753, 0.050156739811912224, 0.04559270516717325, 0.038781163434903045, 0.08661417322834646, 0.004], [0.08764940239043825, 0.08150470219435736, 0.0547112462006079, 0.055401662049861494, 0.07874015748031496, 0.016], [0.01593625498007968, 0.007836990595611285, 0.0060790273556231, 0.002770083102493075, 0.003937007874015748, 0.0], [0.13147410358565736, 0.5532915360501567, 0.6474164133738601, 0.42382271468144045, 0.1732283464566929, 0.064], [0.3426294820717131, 0.047021943573667714, 0.030395136778115502, 0.0332409972299169, 0.015748031496062992, 0.02], [0.28286852589641437, 0.28213166144200624, 0.21580547112462006, 0.45706371191135736, 0.6417322834645669, 0.896]]\n",
            "[[0.11952191235059761, 0.014814814814814815, 0.0213903743315508, 0.012919896640826873, 0.013888888888888888, 0.014925373134328358, 0.02127659574468085, 0.11627906976744186, 0.004], [0.12350597609561753, 0.037037037037037035, 0.11229946524064172, 0.04392764857881137, 0.027777777777777776, 0.014925373134328358, 0.03829787234042553, 0.12403100775193798, 0.004], [0.08764940239043825, 0.03333333333333333, 0.0855614973262032, 0.10335917312661498, 0.09722222222222222, 0.05970149253731343, 0.06382978723404255, 0.031007751937984496, 0.016], [0.01593625498007968, 0.0, 0.0, 0.007751937984496124, 0.006944444444444444, 0.014925373134328358, 0.006382978723404255, 0.007751937984496124, 0.0], [0.13147410358565736, 0.45925925925925926, 0.12834224598930483, 0.6124031007751938, 0.2777777777777778, 0.29850746268656714, 0.6680851063829787, 0.17054263565891473, 0.064], [0.3426294820717131, 0.014814814814814815, 0.016042780748663103, 0.020671834625323, 0.05555555555555555, 0.014925373134328358, 0.03829787234042553, 0.11627906976744186, 0.02], [0.28286852589641437, 0.45925925925925926, 0.6631016042780749, 0.2558139534883721, 0.5555555555555556, 0.582089552238806, 0.2127659574468085, 0.46511627906976744, 0.896]]\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1800x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, axs = plt.subplots(1, 2, figsize=(25, 5), gridspec_kw={'width_ratios': [1,1.7]}, sharey=True)\n",
        "plt.subplots_adjust(wspace = 0.1)\n",
        "\n",
        "#hatches=[['', '-', '/', '+'], ['', '-', '/', '+']]\n",
        "titles = ['(a)', '(b)', '(c)']\n",
        "xlabels = ['Number of Subreddits', 'Percentage of Submissions', 'Percentage of Submissions']\n",
        "alphas = [0.1, 0.25, 0.4, 0.55, 0.7, 0.9, 1]\n",
        "\n",
        "for ind in range(2):\n",
        "  barwidth = 0.8 / len(types[ind])\n",
        "  pos_delta = -0.4 + barwidth / 2.\n",
        "  for tind, t in enumerate(types[ind]):\n",
        "    axs[ind].bar(y_pos[ind] + pos_delta, height[ind][tind], barwidth, color=colors[3],\n",
        "           align='center', alpha=alphas[tind], ecolor='black')#, hatch=hatches[ind][tind])\n",
        "    pos_delta += barwidth\n",
        "  #axs[ind].invert_yaxis()  # labels read top-to-bottom\n",
        "\n",
        "  vals = axs[ind].get_yticks()\n",
        "  if use_percentage[ind]:\n",
        "    axs[ind].set_yticks(vals)\n",
        "\n",
        "    axs[ind].set_yticklabels(['${:d}\\%$'.format(int(x * 100)) for x in vals], fontsize=25)\n",
        "  axs[ind].tick_params(axis='y', labelsize=25)\n",
        "\n",
        "  #if len(labels[ind]) > 1:\n",
        "  #  axs[ind].legend(labels=labels[ind], loc='upper right', fontsize=25)\n",
        "  axs[ind].set_title(titles[ind])\n",
        "  print(height[ind])\n",
        "\n",
        "  axs[ind].set_xticks(y_pos[ind])\n",
        "  axs[ind].set_xticklabels(bar_names_new[ind], fontsize=15)\n",
        "axs[0].set_ylabel('Percentage of Submissions', fontsize=25)\n",
        "axs[1].legend(labels=['Conspiracy-other',\n",
        " 'Conspiracy-politics',\n",
        " 'Negative interaction', 'Hate speech', 'Low-quality source', \n",
        " 'QAnon conspiracy', 'Unrelated'], loc='upper right', fontsize=19, ncol=7, bbox_to_anchor=(1.03, -0.16))\n",
        "for ax in axs:\n",
        "  ax.xaxis.grid(color=\"#d3d3d3\", ls=\":\")\n",
        "  ax.yaxis.grid(color=\"#d3d3d3\", ls=\":\")\n",
        "plt.savefig(outputPath + 'figs/subreddit_submission.pdf', bbox_inches = 'tight')\n",
        "# plt.savefig('figs/subreddit_submission.pdf', bbox_inches = 'tight')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "idDr-x48o0vN"
      },
      "outputs": [],
      "source": [
        "count = 0\n",
        "\n",
        "for subreddit, values in submission_data.items():\n",
        "    count += values[\"Hate Speech\"]\n",
        "    # for label in values.keys():\n",
        "    #     count += values[label]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FoIBFHrzWANG",
        "outputId": "c988eb04-c1a6-44db-89ee-4c320848294e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "execution_count": 121,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "count"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ymWPZKiA4DEO",
        "outputId": "91c9c874-ac93-46f8-8c82-1d22e7001b0c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.006365372374283896"
            ]
          },
          "execution_count": 122,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "10/1571"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "uBTerNpQ8i6o",
        "7uWFtQAQ8qIz",
        "HNRRGbFI9WJV",
        "_M8sc6xnE7Na",
        "UAfo7yLC4Mqr",
        "BULyC3U64MrH"
      ],
      "machine_shape": "hm",
      "name": "QReddit plots.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
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      "file_extension": ".py",
      "mimetype": "text/x-python",
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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