3.3. Analysis_Q3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "649acef3",
   "metadata": {},
   "source": [
    "# 3. Analysis\n",
    "## 3.3. Q3. Frequency of catchy slogans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8c5806f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "333937c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "%run \"0.1. Intro_Visualization style.ipynb\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c4280ec9",
   "metadata": {},
   "outputs": [],
   "source": [
    "%run \"3.0. Analysis_Functions.ipynb\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf947e32",
   "metadata": {},
   "source": [
    "## Data loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "844cad86",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Path to tokenized data\n",
    "file = 'data/df_tokenized.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1069c8d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tokenized = pd.read_csv(file)\n",
    "df_tokenized.drop(columns=['Unnamed: 0'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b8d59e07",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare columns with dates\n",
    "df_tokenized['dat'] = pd.to_datetime(df_tokenized['dat'])\n",
    "df_tokenized['datetime'] = pd.to_datetime(df_tokenized['date'])\n",
    "df_tokenized['year_month'] = pd.to_datetime(df_tokenized['year_month'].astype(str), \n",
    "                                            format='%Y-%m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5feb6311",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make all columns lower case\n",
    "for col in ['tags_top', 'title', 'body', 'tags_bottom', 'body_token_final', 'title_token_final']:\n",
    "    df_tokenized[col] = df_tokenized[col].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d032ffcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tokenized['video_duration_min'] = df_tokenized['video_duration_seconds'] / 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0bc583f0",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_in_source_file</th>\n",
       "      <th>date</th>\n",
       "      <th>tags_top</th>\n",
       "      <th>title</th>\n",
       "      <th>url</th>\n",
       "      <th>body</th>\n",
       "      <th>tags_bottom</th>\n",
       "      <th>video_duration_seconds</th>\n",
       "      <th>file_key</th>\n",
       "      <th>datetime</th>\n",
       "      <th>dat</th>\n",
       "      <th>year</th>\n",
       "      <th>year_month</th>\n",
       "      <th>hour</th>\n",
       "      <th>weekday</th>\n",
       "      <th>whole newscast</th>\n",
       "      <th>body_token_final</th>\n",
       "      <th>title_token_final</th>\n",
       "      <th>video_duration_min</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>2023-01-27 21:45:00</td>\n",
       "      <td>общество</td>\n",
       "      <td>выпуск программы «время» в 21:00 от 27.01.2023</td>\n",
       "      <td>https://www.1tv.ru/news/2023-01-27/446104-vypu...</td>\n",
       "      <td>смотрите в этом выпуске: 79 лет назад была пол...</td>\n",
       "      <td>общество</td>\n",
       "      <td>2422.0</td>\n",
       "      <td>0-15</td>\n",
       "      <td>2023-01-27 21:45:00</td>\n",
       "      <td>2023-01-27</td>\n",
       "      <td>2023</td>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>['смотреть', 'выпуск', '79', 'год', 'полностью...</td>\n",
       "      <td>['выпуск', 'программа', 'время', '21:00', '27....</td>\n",
       "      <td>40.366667</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>2023-01-27 21:35:00</td>\n",
       "      <td>культура</td>\n",
       "      <td>в москве проходит церемония вручения премии «з...</td>\n",
       "      <td>https://www.1tv.ru/news/2023-01-27/446121-v_mo...</td>\n",
       "      <td>событие в мире отечественного кино. в москве в...</td>\n",
       "      <td>культура,кино,марина зудина,екатерина климова,...</td>\n",
       "      <td>290.0</td>\n",
       "      <td>0-15</td>\n",
       "      <td>2023-01-27 21:35:00</td>\n",
       "      <td>2023-01-27</td>\n",
       "      <td>2023</td>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>['событие', 'мир', 'отечественный', 'кино', 'м...</td>\n",
       "      <td>['москва', 'проходить', 'церемония', 'вручение...</td>\n",
       "      <td>4.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>2023-01-27 21:34:00</td>\n",
       "      <td>спорт</td>\n",
       "      <td>в туле появился новый футбольный манеж</td>\n",
       "      <td>https://www.1tv.ru/news/2023-01-27/446120-v_tu...</td>\n",
       "      <td>футбольный манеж в туле — искусственный газон ...</td>\n",
       "      <td>спорт,регионы,футбол,алексей дюмин,тульская об...</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0-15</td>\n",
       "      <td>2023-01-27 21:34:00</td>\n",
       "      <td>2023-01-27</td>\n",
       "      <td>2023</td>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>['футбольный', 'манеж', 'тула', 'искусственный...</td>\n",
       "      <td>['тула', 'появиться', 'новый', 'футбольный', '...</td>\n",
       "      <td>0.800000</td>\n",
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      "text/plain": [
       "   id_in_source_file                 date  tags_top  \\\n",
       "0                 15  2023-01-27 21:45:00  общество   \n",
       "1                 16  2023-01-27 21:35:00  культура   \n",
       "2                 20  2023-01-27 21:34:00     спорт   \n",
       "\n",
       "                                               title  \\\n",
       "0     выпуск программы «время» в 21:00 от 27.01.2023   \n",
       "1  в москве проходит церемония вручения премии «з...   \n",
       "2             в туле появился новый футбольный манеж   \n",
       "\n",
       "                                                 url  \\\n",
       "0  https://www.1tv.ru/news/2023-01-27/446104-vypu...   \n",
       "1  https://www.1tv.ru/news/2023-01-27/446121-v_mo...   \n",
       "2  https://www.1tv.ru/news/2023-01-27/446120-v_tu...   \n",
       "\n",
       "                                                body  \\\n",
       "0  смотрите в этом выпуске: 79 лет назад была пол...   \n",
       "1  событие в мире отечественного кино. в москве в...   \n",
       "2  футбольный манеж в туле — искусственный газон ...   \n",
       "\n",
       "                                         tags_bottom  video_duration_seconds  \\\n",
       "0                                           общество                  2422.0   \n",
       "1  культура,кино,марина зудина,екатерина климова,...                   290.0   \n",
       "2  спорт,регионы,футбол,алексей дюмин,тульская об...                    48.0   \n",
       "\n",
       "  file_key            datetime        dat  year year_month  hour  weekday  \\\n",
       "0     0-15 2023-01-27 21:45:00 2023-01-27  2023 2023-01-01    21        4   \n",
       "1     0-15 2023-01-27 21:35:00 2023-01-27  2023 2023-01-01    21        4   \n",
       "2     0-15 2023-01-27 21:34:00 2023-01-27  2023 2023-01-01    21        4   \n",
       "\n",
       "   whole newscast                                   body_token_final  \\\n",
       "0            True  ['смотреть', 'выпуск', '79', 'год', 'полностью...   \n",
       "1           False  ['событие', 'мир', 'отечественный', 'кино', 'м...   \n",
       "2           False  ['футбольный', 'манеж', 'тула', 'искусственный...   \n",
       "\n",
       "                                   title_token_final  video_duration_min  \n",
       "0  ['выпуск', 'программа', 'время', '21:00', '27....           40.366667  \n",
       "1  ['москва', 'проходить', 'церемония', 'вручение...            4.833333  \n",
       "2  ['тула', 'появиться', 'новый', 'футбольный', '...            0.800000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tokenized.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cab0b37",
   "metadata": {},
   "source": [
    "## Estimating the duration of news stories featuring catchy slogans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8a26fbad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of catchy slogans for analysis\n",
    "slogans = [\n",
    "    ['коллективный запад', 'коллективном западе', 'коллективному западу', 'коллективного запада'],\n",
    "    ['своих не бросаем'],\n",
    "    ['мы вместе', 'мывместе'],\n",
    "    ['все для победы', 'всё для победы'],\n",
    "    ['традиционные ценности', 'традиционных ценностях', 'традиционным ценностям', 'традиционных ценностей'],\n",
    "    ['грязная бомба', 'грязную бомбу', 'грязной бомбы', 'грязной бомбе']\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ca4ec68f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def slogans_duration(df, slogans):\n",
    "    \"\"\"\n",
    "    Calculates the duration of news featuring slogans\n",
    "    Args:\n",
    "    df - dataframe\n",
    "    slogans - list of lists\n",
    "    \n",
    "    Returns:\n",
    "    df_duration - dataframe with total daily duration\n",
    "                  of news containing slogans\n",
    "    \"\"\"\n",
    "    \n",
    "    # Filter newscasts, so we don't double duration of news releases\n",
    "    df = df[df['whole newscast']==False].copy()\n",
    "    \n",
    "    # Analysis for the first slogan\n",
    "    slogan = slogans[0]\n",
    "    # Mark news stories containing slogans\n",
    "    token_dict = {'body': slogan,\n",
    "                  'title': slogan}\n",
    "    slogan_df = find_tokens(df, token_dict)\n",
    "    # Calculate duration of news\n",
    "    slogan_df['video_duration_slogan'] = slogan_df['video_duration_min'] * slogan_df['contain_token'] \n",
    "    duration_df = slogan_df.groupby('year_month')[['video_duration_slogan']].sum()\n",
    "    duration_df.rename(columns={'video_duration_slogan': 'slogan0'}, inplace=True)\n",
    "    duration_df['EMA_slogan0'] = duration_df['slogan0'].ewm(alpha=0.3).mean()\n",
    "    \n",
    "    # Analysis for the rest of slogans\n",
    "    indx=1\n",
    "    for slogan in slogans[1:]:\n",
    "        \n",
    "        # Mark news stories containing slogans\n",
    "        # 1 - contains; 0 - doesn't contain\n",
    "        token_dict = {'body': slogan,\n",
    "                      'title': slogan}\n",
    "        \n",
    "        slogan_df = find_tokens(df, token_dict)\n",
    "        \n",
    "        # Calculate duration of news\n",
    "        slogan_df['video_duration_slogan'] = slogan_df['video_duration_min'] * slogan_df['contain_token'] \n",
    "        duration_df['slogan'+str(indx)] = slogan_df.groupby('year_month')[['video_duration_slogan']].sum()\n",
    "        duration_df['EMA_slogan'+str(indx)] = duration_df['slogan'+str(indx)].ewm(alpha=0.3).mean()\n",
    "        indx += 1\n",
    "    \n",
    "    return duration_df       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "66fa84d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "slogan_duration_df = slogans_duration(df_tokenized, slogans)\n",
    "slogan_duration_df['year'] = slogan_duration_df.index.year "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9934fc43",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "279c993f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get year >=2013 for pictures\n",
    "slogan_duration_df = slogan_duration_df[slogan_duration_df['year']>=2013]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3bdc2cb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x350 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create plots\n",
    "fig, ax = plt.subplots(1, 2, figsize=(9,3.5))\n",
    "\n",
    "ax[0].plot(slogan_duration_df.index, \n",
    "           slogan_duration_df['slogan0'], \n",
    "           color=color_2,\n",
    "           label='monthly total news duration')\n",
    "ax[0].plot(slogan_duration_df.index, \n",
    "           slogan_duration_df['EMA_slogan0'], \n",
    "           color=color_1,\n",
    "           label='exponential moving average')\n",
    "\n",
    "ax[1].plot(slogan_duration_df.index, \n",
    "           slogan_duration_df['slogan1'], \n",
    "           color=color_2,\n",
    "           label='monthly total news duration')\n",
    "ax[1].plot(slogan_duration_df.index, \n",
    "           slogan_duration_df['EMA_slogan1'], \n",
    "           color=color_1,\n",
    "           label='exponential moving average')\n",
    "# Annotation\n",
    "ax[1].annotate('Annexation\\nof Crimea\\n2014-03', \n",
    "             xy=(0.15, 0.94), xycoords='axes fraction', \n",
    "             xytext=(0.3, 0.8), textcoords='axes fraction',\n",
    "             fontsize=8, color=color_txt_labels, horizontalalignment='center',\n",
    "             arrowprops=dict(arrowstyle='->, head_width=0.2', \n",
    "                             color=color_txt_labels, linewidth=0.8))\n",
    "\n",
    "ax[1].annotate('Annexation of\\nUkrainian regions\\n2022-09', \n",
    "             xy=(0.92, 0.5), xycoords='axes fraction', \n",
    "             xytext=(0.84, 0.6), textcoords='axes fraction',\n",
    "             fontsize=8, color=color_txt_labels, horizontalalignment='center',\n",
    "             arrowprops=dict(arrowstyle='->, head_width=0.2', \n",
    "                             color=color_txt_labels, linewidth=0.8))\n",
    "\n",
    "ax[0].title.set_text(\"'Collective West'\")\n",
    "ax[1].title.set_text(\"'Never abandon our people'\")\n",
    "\n",
    "# Add more space between subplots\n",
    "plt.subplots_adjust(left=0.1,\n",
    "                    bottom=0.22,\n",
    "                    right=0.9,\n",
    "                    top=0.83,\n",
    "                    wspace=0.14)\n",
    "\n",
    "# Add a legend\n",
    "lines_labels = ax[0].get_legend_handles_labels()\n",
    "lines = lines_labels[0]\n",
    "labels = lines_labels[1]\n",
    "plt.figlegend(lines, labels, loc = 'lower center', frameon=False)\n",
    "\n",
    "\n",
    "\n",
    "plt.suptitle('Monthly Total Duration of News Stories Featuring Slogans (minutes)',\n",
    "             size=12, weight='bold');\n"
   ]
  }
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