chatting_users.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chatting users\n",
    "\n",
    "Chatting users are defined as users which use many of the tweets to chat with other users. These chatting tweets can be identified by their contents, for example pronouns like *I* and *you* and specific verb forms like *am*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import re\n",
    "from IPython.display import clear_output\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATADIR = \"../data/text/\"\n",
    "QUERY_USERS = r'\\b(ik|mij|me|jij|je|jou|jouw|jullie|heb|ben)\\b'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def squeal(text=None):\n",
    "    clear_output(wait=True)\n",
    "    if not text is None: print(text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 1. Select users (skip)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "YEAR= \"2021\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20211231-23.out.gz\n"
     ]
    }
   ],
   "source": [
    "files = sorted(os.listdir(DATADIR))\n",
    "pattern = f\"^{YEAR}\"\n",
    "totals = {}\n",
    "matches = {}\n",
    "for file_name in files:\n",
    "    if re.search(pattern, file_name):\n",
    "        squeal(file_name)\n",
    "        df = pd.read_csv(DATADIR+file_name, index_col=\"id_str\")\n",
    "        user_counts = df[\"user\"].value_counts()\n",
    "        for user in user_counts.keys():\n",
    "            if user in totals:\n",
    "                totals[user] += user_counts[user]\n",
    "            else:\n",
    "                totals[user] = user_counts[user]\n",
    "        matching_rows = df[\"text\"].str.contains(QUERY_USERS, flags=re.IGNORECASE)\n",
    "        matching_counts = df[matching_rows][\"user\"].value_counts()\n",
    "        for user in matching_counts.keys():\n",
    "            if user in matches:\n",
    "                matches[user] += matching_counts[user]\n",
    "            else:\n",
    "                matches[user] = matching_counts[user]        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "chatting_users = {} \n",
    "for user in matches:\n",
    "    if matches[user] >= 0.5 * totals[user] and matches[user] > 10:\n",
    "        chatting_users[user] = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "141676"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(chatting_users)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(chatting_users.keys()).to_csv(f\"csv/chatting_users_{YEAR}.csv\", index=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Count tweets (skip)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "TOPIC_HAPPY = \"blij\"\n",
    "QUERY_TOPIC_HAPPY = r'(blij|blije)\\b'\n",
    "TOPIC_LONESOME = \"eenzaam\"\n",
    "QUERY_TOPIC_LONESOME = r'\\b(eenzaam|eenzam)'\n",
    "TOPIC_STRESS = \"stress\"\n",
    "QUERY_TOPIC_STRESS = r'moe\\b|depress|depri|bang|angst|paniek|stress'\n",
    "QUERY_I = r'\\b(ik|mij|me)\\b'\n",
    "\n",
    "TOPIC = TOPIC_LONESOME\n",
    "QUERY_TOPIC = QUERY_TOPIC_LONESOME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process(df_merged, date, hours_matched, tweets, query_topic):\n",
    "    matching_rows_topic = df_merged[\"text\"].str.contains(query_topic, flags=re.IGNORECASE)\n",
    "    matching_rows_i = df_merged[\"text\"].str.contains(QUERY_I, flags=re.IGNORECASE)\n",
    "    matches_df = df_merged[matching_rows_topic & matching_rows_i]\n",
    "    matches = len(matches_df)\n",
    "    misses = len(df_merged[(matching_rows_topic == False) | (matching_rows_i == False)])\n",
    "    hours_matched[date] = (matches, misses)\n",
    "    tweets.extend(matches_df.values.tolist())\n",
    "    \n",
    "date = \"20200301-00\"\n",
    "hours_matched = {}\n",
    "tweets = []\n",
    "df_merged = pd.DataFrame([{\"user\":\"test\", \"text\": \"ik\"}, {\"user\":\"test\", \"text\": \"blij\"}])\n",
    "process(df_merged, date, hours_matched, tweets, QUERY_TOPIC_LONESOME)\n",
    "assert hours_matched[date] == (0, 2)\n",
    "assert len(tweets) == 0\n",
    "hours_matched = {}\n",
    "tweets = []\n",
    "df_merged = pd.DataFrame([{\"user\":\"test\", \"text\": \"ik blij\"}, {\"user\":\"test\", \"text\": \"\"}])\n",
    "process(df_merged, date, hours_matched, tweets, QUERY_TOPIC_HAPPY)\n",
    "assert hours_matched[date] == (1, 1)\n",
    "assert len(tweets) == 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "YEAR = \"2021\"\n",
    "pattern = f\"^{YEAR}\"\n",
    "chatting_users = { user: True for user in list(pd.read_csv(f\"csv/chatting_users_{YEAR}.csv\", index_col=None).iloc[:,0])}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20211231-23.out.gz\n"
     ]
    }
   ],
   "source": [
    "hours_matched = {}\n",
    "tweets = []\n",
    "files = sorted(os.listdir(DATADIR))\n",
    "for file_name in files:\n",
    "    if re.search(pattern, file_name):\n",
    "        squeal(file_name)\n",
    "        date = re.sub(\".out.gz\", \"\", file_name)\n",
    "        df = pd.read_csv(DATADIR+file_name)\n",
    "        df_merged = pd.merge(df, pd.DataFrame(chatting_users.keys(), columns=[\"user\"]), on=[\"user\"])\n",
    "        process(df_merged, date, hours_matched, tweets, QUERY_TOPIC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(tweets, columns=[\"id_str\", \"in_reply_to_status_id_str\", \"user\", \"verified\", \"text\", \"location\"]).to_csv(f\"csv/hours_matched_{YEAR}_{TOPIC}_tweets.csv\", index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame([ [hour] + list(hours_matched[hour]) for hour in hours_matched ], columns=[\"hour\", \"matched\", \"missed\"]).to_csv(f\"csv/hours_matched_{YEAR}_{TOPIC}.csv\", index=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Make graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def moving_median(number_list, size):\n",
    "    new_list = []\n",
    "    start_offset = int(size/2)\n",
    "    for i in range(0, len(number_list)):\n",
    "        start = max(0, i-start_offset)\n",
    "        end = min(len(number_list), i+start_offset+1)\n",
    "        new_list.append(np.median(number_list[start:end]))\n",
    "    return new_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_date_data_counts(hours_matched):\n",
    "    date_data_freqs = {}\n",
    "    date_data_counts = {}\n",
    "    for hour in hours_matched:\n",
    "        date = re.sub(\"-[0-9][0-9]$\", \"\", hour)\n",
    "        if date not in date_data_freqs:\n",
    "             date_data_freqs[date] = [0, 0]\n",
    "        date_data_freqs[date][0] += int(hours_matched[hour][0])\n",
    "        date_data_freqs[date][1] += int(hours_matched[hour][1])\n",
    "        if date_data_freqs[date][0] + date_data_freqs[date][1] > 0:\n",
    "            date_data_counts[date] = date_data_freqs[date][0] / ( date_data_freqs[date][0] + date_data_freqs[date][1] )\n",
    "    return date_data_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_plot_data(date_data_counts):\n",
    "    x = [datetime.datetime.strptime(date, f\"%Y%m%d\") for date in date_data_counts]\n",
    "    y = moving_median(list(date_data_counts.values()), WINDOW_SIZE)\n",
    "    return x, y\n",
    "\n",
    "\n",
    "def make_date_data_counts(year, topic):\n",
    "    hours_matched_list = pd.read_csv(f\"csv/hours_matched_{year}_{topic}.csv\", index_col=None).to_dict(orient=\"records\")\n",
    "    hours_matched = {}\n",
    "    for row in hours_matched_list:\n",
    "        hours_matched[row[\"hour\"]] = [row[\"matched\"], row[\"missed\"]]\n",
    "    date_data_counts = extract_date_data_counts(hours_matched)\n",
    "    return date_data_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "WINDOW_SIZE = 60\n",
    "\n",
    "TOPIC = \"eenzaam\"\n",
    "TOPIC_EN = {\"blij\": \"happiness\", \"eenzaam\": \"loneliness\"}[TOPIC]\n",
    "\n",
    "date_data_counts_2018 = make_date_data_counts(\"2018\", TOPIC)\n",
    "date_data_counts_2019 = make_date_data_counts(\"2019\", TOPIC)\n",
    "date_data_counts_2020 = make_date_data_counts(\"2020\", TOPIC)\n",
    "date_data_counts_2021 = make_date_data_counts(\"2021\", TOPIC)\n",
    "date_data_counts = dict(date_data_counts_2018)\n",
    "date_data_counts.update(dict(date_data_counts_2019))\n",
    "date_data_counts.update(dict(date_data_counts_2020))\n",
    "date_data_counts.update(dict(date_data_counts_2021))\n",
    "\n",
    "x, y = make_plot_data(date_data_counts)\n",
    "x_2018 = x[:len(date_data_counts_2018)]\n",
    "x_2019 = x[len(date_data_counts_2018):len(date_data_counts_2018)+len(date_data_counts_2019)]\n",
    "x_2020 = x[len(date_data_counts_2018)+len(date_data_counts_2019):len(date_data_counts_2018)+len(date_data_counts_2019)+len(date_data_counts_2020)]\n",
    "x_2021 = x[len(date_data_counts_2018)+len(date_data_counts_2019)+len(date_data_counts_2020):]\n",
    "y_2018 = y[:len(date_data_counts_2018)]\n",
    "y_2019 = y[len(date_data_counts_2018):len(date_data_counts_2018)+len(date_data_counts_2019)]\n",
    "y_2020 = y[len(date_data_counts_2018)+len(date_data_counts_2019):len(date_data_counts_2018)+len(date_data_counts_2019)+len(date_data_counts_2020)]\n",
    "y_2021 = y[len(date_data_counts_2018)+len(date_data_counts_2019)+len(date_data_counts_2020):]\n",
    "\n",
    "font = {'family' : 'DejaVu Sans',\n",
    "        'weight' : 'normal',\n",
    "        'size'   : 16}\n",
    "matplotlib.rc('font', **font)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter(\"%b\"))\n",
    "#ax.plot_date(x_2020[:len(y_2018)], y_2018, fmt=\"-\", label=\"2018\")\n",
    "ax.plot_date(x_2020[:len(y_2019)], [ 1000*y for y in y_2019 ], fmt=\"-\", label=\"2019\")\n",
    "ax.plot_date(x_2020, [ 1000*y for y in y_2020 ], fmt=\"-\", label=\"2020\")\n",
    "ax.plot_date(x_2020[:len(y_2021)], [ 1000*y for y in y_2021 ], fmt=\"-\", label=\"2021\")\n",
    "plt.title(f\"{TOPIC_EN} (relative frequency per 1000 tweets)\")\n",
    "plt.xlabel(\"month\")\n",
    "plt.ylabel(\"\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig(f\"{TOPIC}.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(365, 366, 365, 365, 366, 365, 1461, 1461, 365, 366, 365)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x_2019), len(x_2020), len(x_2021), len(y_2019), len(y_2020), len(y_2021), len(x), len(y), len(date_data_counts_2019), len(date_data_counts_2020), len(date_data_counts_2021),"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.Process annotations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_FILE = \"../data/annotation/eenzaam_2020.csv\"\n",
    "df_data = pd.read_csv(DATA_FILE, index_col=['id_str'])\n",
    "df_annotation = pd.read_csv(DATA_FILE + \".human-labels.txt\", header=None, sep=\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>in_reply_to_status_id_str</th>\n",
       "      <th>user</th>\n",
       "      <th>verified</th>\n",
       "      <th>text</th>\n",
       "      <th>location</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id_str</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1212168318007812096</td>\n",
       "      <td>1.212167e+18</td>\n",
       "      <td>Yazid_NL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>@Orihime_Inoue27 Dat is toch lief van mij.. \\n...</td>\n",
       "      <td>The woods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1212306665040683009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MylifewithF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Onze Facebook groep heb ik gister opgestart en...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1212326477569634305</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ekkendonk</td>\n",
       "      <td>NaN</td>\n",
       "      <td>#delen Ik vind het moeilijk om openlijk te ver...</td>\n",
       "      <td>Noord-Brabant, Nederland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1213658296000880640</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WebCamBabesNL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hello Lieverd van mij. Ik ben een lekkere jong...</td>\n",
       "      <td>WorldWide</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1214256611608453120</td>\n",
       "      <td>1.213743e+18</td>\n",
       "      <td>J12011193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>@EenzameH Volgende keer maar met mij😳😊</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1343954639633473536</td>\n",
       "      <td>1.343954e+18</td>\n",
       "      <td>ThomasPruijsen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ik sta nog steeds 100% achter mijn blog en ope...</td>\n",
       "      <td>Nijmegen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1343975651477512195</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ellavangassen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Jaaroverzicht kijken: blijten bij BLM-beelden,...</td>\n",
       "      <td>Antwerpen, België</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1343979622522900480</td>\n",
       "      <td>1.343949e+18</td>\n",
       "      <td>1903George</td>\n",
       "      <td>NaN</td>\n",
       "      <td>@TheAllSeeing4 @JoostNiemoller @Lies69616541 I...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1344626935905873921</td>\n",
       "      <td>1.344608e+18</td>\n",
       "      <td>Hendrikje01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>@Inge_Happe 🤞maar ook als de test negatief is(...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1344644290438451203</td>\n",
       "      <td>1.344644e+18</td>\n",
       "      <td>SimoneTanner10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>@BenWoldring Verpleegkundigen die ‘t rustig he...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>718 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     in_reply_to_status_id_str            user  verified  \\\n",
       "id_str                                                                     \n",
       "1212168318007812096               1.212167e+18        Yazid_NL       NaN   \n",
       "1212306665040683009                        NaN     MylifewithF       NaN   \n",
       "1212326477569634305                        NaN       ekkendonk       NaN   \n",
       "1213658296000880640                        NaN   WebCamBabesNL       NaN   \n",
       "1214256611608453120               1.213743e+18       J12011193       NaN   \n",
       "...                                        ...             ...       ...   \n",
       "1343954639633473536               1.343954e+18  ThomasPruijsen       NaN   \n",
       "1343975651477512195                        NaN   ellavangassen       NaN   \n",
       "1343979622522900480               1.343949e+18      1903George       NaN   \n",
       "1344626935905873921               1.344608e+18     Hendrikje01       NaN   \n",
       "1344644290438451203               1.344644e+18  SimoneTanner10       NaN   \n",
       "\n",
       "                                                                  text  \\\n",
       "id_str                                                                   \n",
       "1212168318007812096  @Orihime_Inoue27 Dat is toch lief van mij.. \\n...   \n",
       "1212306665040683009  Onze Facebook groep heb ik gister opgestart en...   \n",
       "1212326477569634305  #delen Ik vind het moeilijk om openlijk te ver...   \n",
       "1213658296000880640  Hello Lieverd van mij. Ik ben een lekkere jong...   \n",
       "1214256611608453120             @EenzameH Volgende keer maar met mij😳😊   \n",
       "...                                                                ...   \n",
       "1343954639633473536  Ik sta nog steeds 100% achter mijn blog en ope...   \n",
       "1343975651477512195  Jaaroverzicht kijken: blijten bij BLM-beelden,...   \n",
       "1343979622522900480  @TheAllSeeing4 @JoostNiemoller @Lies69616541 I...   \n",
       "1344626935905873921  @Inge_Happe 🤞maar ook als de test negatief is(...   \n",
       "1344644290438451203  @BenWoldring Verpleegkundigen die ‘t rustig he...   \n",
       "\n",
       "                                     location  \n",
       "id_str                                         \n",
       "1212168318007812096                 The woods  \n",
       "1212306665040683009                       NaN  \n",
       "1212326477569634305  Noord-Brabant, Nederland  \n",
       "1213658296000880640                 WorldWide  \n",
       "1214256611608453120                       NaN  \n",
       "...                                       ...  \n",
       "1343954639633473536                  Nijmegen  \n",
       "1343975651477512195         Antwerpen, België  \n",
       "1343979622522900480                       NaN  \n",
       "1344626935905873921                       NaN  \n",
       "1344644290438451203                       NaN  \n",
       "\n",
       "[718 rows x 5 columns]"
      ]
     },
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       "..               ...             ...                  ...  ...         ...\n",
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       "\n",
       "[620 rows x 5 columns]"
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