Opinions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b5785ff3-637e-4e95-8364-d5ecfcc3d9df",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "# Impacts of the Citizens' Opinions on Government's Roles\n",
    "\n",
    "Date: 07.12.2021\n",
    "\n",
    "Elmira Mansur\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65e012f9-a4ce-4aa1-a5d8-9d90e31e7615",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Code for Part 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4f4d578-75cd-4e2a-8a2d-7692d47fe145",
   "metadata": {},
   "source": [
    "## Set-Up "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b177c6b5-88fd-4548-998d-ee551d24d739",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing packages\n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "import numpy as np\n",
    "import psycopg2\n",
    "import pandas as pd\n",
    "import random\n",
    "import seaborn as sns\n",
    "import statsmodels.formula.api as smf\n",
    "from IPython.display import Image\n",
    "\n",
    "# Connecting to covid19db.org\n",
    "conn = psycopg2.connect(\n",
    "    host='covid19db.org',\n",
    "    port=5432,\n",
    "    dbname='covid19',\n",
    "    user='covid19',\n",
    "    password='covid19')\n",
    "cur = conn.cursor()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57395205-e948-4445-92e6-7bf5eae91691",
   "metadata": {},
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9a85dc46-abcb-41ee-828b-1c0dc7b823e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cases_by_govresp(govresp,data):\n",
    "    '''The mean total confirmed cases per million at specified government response index'''\n",
    "\n",
    "    return data[abs(data[\"gov_resp\"]-govresp)<1][\"total confirmed/million\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2f603fe9-b5d9-4d89-aa9e-677ed4e16fa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def random_low_high_countries(data):\n",
    "    '''Returns six random countries; three with lower initial responses and three with higher. \n",
    "        These countries are more than half a standard deviation away from the mean'''\n",
    "\n",
    "    i_response= data[\"initial_response\"]\n",
    "    low= i_response.mean()-(i_response.std()/2)\n",
    "    high= i_response.mean()+(i_response.std()/2)\n",
    "       \n",
    "    low_countries= list(data[data[\"initial_response\"]<low].country.unique())\n",
    "    high_countries= list(data[data[\"initial_response\"]>high].country.unique())\n",
    "\n",
    "    random_lowitems = random.sample(low_countries, 3)\n",
    "    random_highitems = random.sample(high_countries, 3)\n",
    "    \n",
    "    low_high= random_lowitems + random_highitems\n",
    "    \n",
    "    return low_high"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6546d89-d3c8-4cb6-adae-5f59177a8330",
   "metadata": {},
   "source": [
    "## Data Colletion "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35560631-30bf-46e2-ae98-64bc08352566",
   "metadata": {},
   "source": [
    "**Epidemiology Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d4ec2e2d-3e1a-4ff6-8f35-e6dbf5f10224",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Collecting country-level data\n",
    "sql_command = \"\"\"SELECT date,country,countrycode,confirmed,dead FROM epidemiology WHERE \n",
    "               adm_area_1 IS NULL AND\n",
    "               adm_area_2 IS NULL AND\n",
    "               adm_area_3 IS NULL AND \n",
    "               source= 'WRD_WHO'        \n",
    "               ORDER BY \n",
    "                   date\"\"\"\n",
    "\n",
    "df_epidemiology = pd.read_sql(sql_command, conn)\n",
    "df_epidemiology[\"date\"]=pd.to_datetime(df_epidemiology['date'], format='%Y-%m-%d')\n",
    "df_epidemiology[\"confirmed\"]=df_epidemiology[\"confirmed\"].astype(int)\n",
    "df_epidemiology[\"dead\"]=df_epidemiology[\"dead\"].astype(int)\n",
    "\n",
    "# Creating a new column for daily confirmed cases \n",
    "for country in df_epidemiology.country.unique():\n",
    "    mask = df_epidemiology[\"country\"]==country\n",
    "    df_epidemiology.loc[mask,\"confirmed/day\"] = df_epidemiology[mask]['confirmed'].diff()\n",
    "    df_epidemiology.loc[mask,\"dead/day\"] = df_epidemiology[mask]['dead'].diff()\n",
    "df_epidemiology['confirmed/day'].fillna(0,inplace=True)\n",
    "df_epidemiology['dead/day'].fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e8ebba3-c300-4b56-9bfb-3765cad198d2",
   "metadata": {},
   "source": [
    "**Population Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3b26b693-be27-4d2a-ab6d-370631e519f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "sql_command = \"\"\"SELECT value, country, countrycode FROM world_bank WHERE  \n",
    "                   indicator_name = 'Population, total' AND \n",
    "                   year=2019\"\"\"\n",
    "\n",
    "df_pop = pd.read_sql(sql_command, conn) \n",
    "df_pop.rename(columns={\"value\": \"population\"},inplace=True)\n",
    "df_pop[\"population(m)\"]=df_pop[\"population\"]/1000000\n",
    "\n",
    "# Merging with epidemiology data\n",
    "df_epi=pd.merge(df_epidemiology,df_pop,on=[\"country\",\"countrycode\"], how='left')\n",
    "\n",
    "# Exluding countries without population data\n",
    "df_epi.dropna(inplace=True)\n",
    "\n",
    "# Creating new columns for epidemiology data normalized by population\n",
    "df_epi[\"confirmed/million\"]= df_epi[\"confirmed/day\"]/df_epi[\"population(m)\"]\n",
    "df_epi[\"dead/million\"]= df_epi[\"dead/day\"]/df_epi[\"population(m)\"]\n",
    "df_epi[\"total confirmed/million\"]= df_epi[\"confirmed\"]/df_epi[\"population(m)\"]\n",
    "df_epi[\"total dead/million\"]= df_epi[\"dead\"]/df_epi[\"population(m)\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8246157b-557f-4156-9e99-3eead57a22c8",
   "metadata": {},
   "source": [
    "**Government Response Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "215bfc79-0ed8-4ac2-a915-2c3ecf687afe",
   "metadata": {},
   "outputs": [],
   "source": [
    "sql_command =\"\"\"SELECT date, country, countrycode, government_response_index_for_display\n",
    "               FROM government_response \n",
    "               WHERE adm_area_1 IS NULL\n",
    "               ORDER BY date \"\"\"\n",
    "    \n",
    "df_govtrack= pd.read_sql(sql_command, conn) \n",
    "df_govtrack.rename(columns={\"government_response_index_for_display\": \"gov_resp\"},inplace=True)\n",
    "df_govtrack[\"date\"]=pd.to_datetime(df_govtrack['date'], format='%Y-%m-%d')\n",
    "df_govtrack.dropna(inplace=True)\n",
    "\n",
    "#Merge with epidemiology data\n",
    "gov_epi = pd.merge(df_govtrack, df_epi, on=['country','countrycode','date'])#, right_on = ['country','countrycode','date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f8c3b31-ede3-4e92-9489-619665ac9ff6",
   "metadata": {},
   "source": [
    "**Survey Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1979fe48-0e42-4ec5-99a3-ab13862d0557",
   "metadata": {},
   "outputs": [],
   "source": [
    "# E037: The question on Government Responsibility\n",
    "questions=[\"E037\"]\n",
    "wave = \"2008-2010\"\n",
    "\n",
    "sql_command = \"\"\"SELECT * FROM surveys WHERE wave=%(wave)s AND adm_area_1 IS NULL AND adm_area_2 IS NULL AND adm_area_3 IS NULL\"\"\"\n",
    "df_surveys = pd.read_sql(sql_command, conn, params={'wave': wave})\n",
    "\n",
    "# Cleaning Data \n",
    "survey= pd.DataFrame()\n",
    "survey[\"country\"]=np.zeros(len(df_surveys))\n",
    "survey[\"countrycode\"]=np.zeros(len(df_surveys))\n",
    "\n",
    "for i in range(len(df_surveys)):\n",
    "    survey[\"country\"].loc[i]=df_surveys[\"country\"].loc[i]\n",
    "    survey[\"countrycode\"].loc[i]=df_surveys[\"countrycode\"].loc[i]\n",
    "    for j in questions:\n",
    "        title= df_surveys.properties[i][j][\"Label\"]\n",
    "        liste=[]\n",
    "        for k,v in df_surveys.properties[i][j][\"Frequencies\"].items():\n",
    "            if k[-2]!=\"-\":\n",
    "                varie= int(k[-2:].replace(\"_\",\"\"))*v \n",
    "                liste.append(varie)\n",
    "                survey.loc[i,title]=sum(liste)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b2434a7-fd96-4d6e-8af1-9312a8bfca97",
   "metadata": {},
   "source": [
    "## Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "71e7667a-0266-4cae-a379-1b538ccaef76",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initial responses for the first hundred cases\n",
    "aggr= {\"gov_resp\":[np.ptp,min,max], \"date\":np.ptp, \"confirmed\":np.ptp}\n",
    "\n",
    "initial=gov_epi[(gov_epi[\"confirmed\"]>0.1)&(gov_epi[\"confirmed\"]<100)].groupby(\"country\").agg(aggr)\n",
    "initial.reset_index(inplace=True)\n",
    "initial[\"daily case rate\"]=initial[\"confirmed\"][\"ptp\"]/initial[\"date\"][\"ptp\"].dt.days\n",
    "initial[\"initial_response\"]=(initial[\"gov_resp\"][\"min\"]+1)*(initial[\"gov_resp\"][\"ptp\"]+1)/(initial[\"daily case rate\"]+1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6a007152-db92-4909-8034-4fdd35f85cac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merging the responses with survey data \n",
    "response_data= initial[[\"country\",\"initial_response\"]].droplevel(1,axis=1)\n",
    "all_df= pd.merge(survey,response_data,on=\"country\")\n",
    "all_df.rename(columns={\"Government responsibility\":\"Government_responsibility\"},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "99fd112d-600c-49b6-82dd-7652d3c70c6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Regression model for initial responses and government responsibility \n",
    "form_1 = 'initial_response ~ Government_responsibility'\n",
    "fit_1 = smf.ols(formula = form_1, data = all_df).fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afa653fa-f9fd-4705-908d-04f74e40a61b",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Figures "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c9db3ac-7332-4097-aae6-376122d0ed16",
   "metadata": {},
   "source": [
    "**Figure 1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "704dc4ce-cb02-41a8-a71c-0035d3592f5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Daily Confirmed Cases per Million\n",
    "def figure_1():\n",
    "    subset= ['Luxembourg', 'Estonia', 'Belarus','Italy','Romania','Russia', 'Turkey', 'Portugal']\n",
    "    color_list = [\"r\",\"b\",\"g\",\"c\",\"m\",\"y\",\"tab:orange\",\"tab:purple\"]\n",
    "    epi=df_epi.groupby('country').resample('W-MON', on='date').mean().reset_index().sort_values(by='date')\n",
    "    i=0\n",
    "    plt.figure(figsize=(10,10))  \n",
    "    for country in subset:\n",
    "        mask= epi[\"country\"]==country\n",
    "        plt.plot(epi[mask][\"date\"],epi[mask]['confirmed/million'],linestyle='-', color=color_list[i])\n",
    "        plt.legend(subset)\n",
    "        i+=1\n",
    "    plt.ylabel(\"Daily confirmed cases per million\")\n",
    "    plt.title(\"Figure 1: Daily Confirmed Cases per Million\",fontsize=25)\n",
    "    plt.savefig(\"Figure_1.png\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b826196-c488-4203-a933-fcf8b17acc96",
   "metadata": {},
   "source": [
    "**Figure 2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "745f8862-befc-47c5-94e9-042beca581df",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Initial Responses of Several Countries\n",
    "def figure_2():\n",
    "    countries= random_low_high_countries(initial)\n",
    "    \n",
    "    fig, axs = plt.subplots(2, 3,figsize=(15,15),sharey=True)\n",
    "    fig.suptitle('Figure 2: Initial Responses of Several Countries', fontsize=25)\n",
    "    fig.subplots_adjust(wspace=0.23,hspace=0.3)\n",
    "    rows = ['Lower initial response score','Higher initial response score']\n",
    "    for ax, row in zip(axs[:,0], rows):\n",
    "        ax.set_ylabel(row, rotation=90, fontsize=25,color='xkcd:dark maroon')\n",
    "    for ax in axs[0,:]:\n",
    "        ax.set_facecolor('xkcd:ice blue')\n",
    "    for ax in axs[1,:]:\n",
    "        ax.set_facecolor('xkcd:pale lilac')\n",
    "    \n",
    "    filtered_data=gov_epi[(gov_epi[\"confirmed\"]<100)].sort_values(\"date\")\n",
    "    for idx, country in enumerate(countries):\n",
    "        data = filtered_data[filtered_data['country'] == country]\n",
    "        date = data['date']\n",
    "        total_confirmed = data['confirmed']\n",
    "        gov_response = data['gov_resp']\n",
    "        argmin = total_confirmed.ne(0).idxmax()\n",
    "        axs[idx // 3, idx % 3].plot(date, total_confirmed, label=\"Total Confirmed\", color=\"xkcd:prussian blue\")\n",
    "        \n",
    "        first_case = (date[argmin], int(total_confirmed[argmin]))\n",
    "        axs[idx // 3, idx % 3].plot([first_case[0]], [first_case[1]], marker=\"*\", ls=\"\", c=\"green\", markersize=10)\n",
    "        axs[idx // 3, idx % 3].annotate(\"First case\", (first_case[0]-dt.timedelta(days=5), first_case[1]-6),color=\"green\")\n",
    "        \n",
    "        axis = axs[idx // 3, idx % 3].twinx()\n",
    "        axis.set_ylim([-3,70])\n",
    "        axis.plot(date, gov_response, label=\"Gov Resp\", color=\"xkcd:dirty orange\")\n",
    "        axis.tick_params(axis='y', labelcolor=\"xkcd:dirty orange\")\n",
    "        \n",
    "        lines, labels = axs[idx // 3, idx % 3].get_legend_handles_labels()\n",
    "        lines2, labels2 = axis.get_legend_handles_labels()\n",
    "        axis.legend(lines + lines2, labels + labels2, loc='upper right')\n",
    "    \n",
    "        axs[idx // 3, idx % 3].set_title(country.upper(),fontsize=15,color=\"xkcd:dark maroon\")\n",
    "        axs[idx // 3, idx % 3].tick_params(axis='y', labelcolor=\"xkcd:prussian blue\")\n",
    "        axs[idx // 3, idx % 3].tick_params(labelrotation=35)\n",
    "        axs[idx // 3, idx % 3].yaxis.set_tick_params(which='both', labelbottom=True)\n",
    "    \n",
    "    plt.savefig(\"Figure 2.png\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0d5b258-767b-46cd-9a2a-ef5cbae67a2c",
   "metadata": {},
   "source": [
    "**Figure 3**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "8f8762a4-a67c-49fa-85f7-ae24d152a1ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Regression model for initial response and government responsibility\n",
    "def figure_3():\n",
    "    g = sns.lmplot(x=\"Government_responsibility\", y=\"initial_response\", data=all_df)\n",
    "    g.fig.suptitle(\"Figure 3: Regression Model for Government Responsibility and Initial Responses by Country\",\n",
    "                      fontsize=15, fontdict={\"weight\": \"bold\"})\n",
    "    \n",
    "    g.set(xlabel='Government Responsibility', ylabel='Initial Response')\n",
    "    plt.savefig(\"Figure 3.png\",bbox_inches='tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5e0db5b-a801-40b6-86bd-0bd7e37a048e",
   "metadata": {},
   "source": [
    "## Tables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a290265f-4dd0-4aa2-9be4-5178940123b4",
   "metadata": {},
   "source": [
    "**Table 1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9f85cb83-740d-43be-ae6e-76ffc87e7368",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Responses at the date of the first positive cases \n",
    "non_zero= gov_epi[gov_epi[\"confirmed\"]!= 0]\n",
    "i = non_zero.groupby(\"country\")[\"confirmed\"].idxmin()\n",
    "first_case=non_zero.loc[i]\n",
    "\n",
    "table_1 = pd.DataFrame([],columns=[\"Mean\",\"Std Dev.\",\"Min.\",\"Max.\"],\n",
    "                           index=[\"Government Response\"])\n",
    "\n",
    "table_1.loc[\"Government Response\"]= [first_case[\"gov_resp\"].mean(),first_case[\"gov_resp\"].std(),first_case[\"gov_resp\"].min(),\n",
    "                      first_case[\"gov_resp\"].max()]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99dcf6c3-285f-4310-aa3d-187ac3647fdd",
   "metadata": {},
   "source": [
    "**Table 2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "15d01b58-5f01-45cd-b6fe-a7df8b13cb11",
   "metadata": {},
   "outputs": [],
   "source": [
    "# World Values Survey \"Government Responsibility\" answers \n",
    "table_2 = pd.DataFrame([],columns=[\"Mean\",\"Std Dev.\",\"Min.\",\"Max.\"],\n",
    "                           index=[\"Government Responsibility\"])\n",
    "\n",
    "table_2.loc[\"Government Responsibility\"]= [survey[\"Government responsibility\"].mean(),survey[\"Government responsibility\"].std(),\n",
    "                                           survey[\"Government responsibility\"].min(),survey[\"Government responsibility\"].max()]\n",
    "\n",
    "\n",
    "# Countries with minimum and maximum values \n",
    "minim = survey[\"Government responsibility\"].idxmin()\n",
    "maxim = survey[\"Government responsibility\"].idxmax()\n",
    "\n",
    "min_country=survey.loc[minim,\"country\"]\n",
    "max_country=survey.loc[maxim,\"country\"]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9562e95-c86c-4c26-a6a0-291b9e68af38",
   "metadata": {},
   "source": [
    "**Table 3**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "796e6dff-dd62-4244-92fa-0090d536b6a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Important Parameters for the Regression Model for initial response and government responsibility\n",
    "table_3 = pd.DataFrame([],columns=[\"R squared\",\"Coefficient for Gov. Resp.\",\"p-value for Gov. Resp.\"],\n",
    "                           index=[\"Regression Model\"])\n",
    "\n",
    "table_3.loc[\"Regression Model\"]= [fit_1.rsquared,fit_1.params[1],fit_1.pvalues[1]]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "404d638f-448a-4da4-96a8-5a860a92a066",
   "metadata": {},
   "source": [
    "# Code for Part 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39e8bf63-be4b-4aa1-ae62-b6c5d192366f",
   "metadata": {},
   "source": [
    "## Set-Up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3982fa0d-7377-472c-9603-c3f23f223212",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing packages\n",
    "import apikeys\n",
    "import requests\n",
    "import bs4\n",
    "import time\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "383176f5-7169-4bca-9bea-a85f8e904407",
   "metadata": {},
   "source": [
    "## Data Collection and Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "781e0f78-2972-4db1-951a-8b11485988cf",
   "metadata": {},
   "source": [
    "**Wikipedia and Information about Presidents**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "08359300-b3f3-4fe3-a84f-e356424c0415",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Head of States\n",
    "DOMAIN = \"https://en.wikipedia.org\" \n",
    "\n",
    "s = requests.Session()\n",
    "s.headers.update({'User-agent': 'Python/Elmira wikicrawl0.2'})\n",
    "\n",
    "article = \"List_of_current_heads_of_state_and_government\"\n",
    "req = s.get(f\"{DOMAIN}/wiki/{article}\") \n",
    "soup = bs4.BeautifulSoup(req.content, 'html.parser')\n",
    "indiatable=soup.find('table',{'wikitable plainrowheaders'})\n",
    "pre=pd.read_html(str(indiatable))\n",
    "pre=pd.DataFrame(pre[0])\n",
    "\n",
    "# Only the countries with World Values Survey data\n",
    "for i in range(len(pre)):\n",
    "    if pre.loc[i,\"State\"] not in survey[\"country\"].unique():\n",
    "        pre.drop([i],inplace=True)\n",
    "\n",
    "pre.reset_index(inplace=True,drop=True)\n",
    "pre.drop(columns=\"Head of government\",inplace=True)\n",
    "\n",
    "# Drop repetative rows\n",
    "pre.drop([5,6,8,12,19,43,44,46],inplace=True)\n",
    "pre.reset_index(inplace=True,drop=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "13898e08-c009-475b-9973-5646f6dcea70",
   "metadata": {},
   "outputs": [],
   "source": [
    "twitter_accounts= np.array(['ilirmetazyrtar','Arm_President','vanderbellen', 0,0,0,'PresidentOfBg',0,\n",
    "                'AnastasiadesCY',0,0,'AlarKaris','niinisto','EmmanuelMacron',0,\n",
    "                0,0,0,'PresidentISL','PresidentIRL','Quirinale','valstsgriba','GitanasNauseda',0,'presidentmt',\n",
    "                'sandumaiamd','predsjednik_cg',0,0,'AndrzejDuda','presidencia','KlausIohannis','KremlinRussia_E',\n",
    "                'avucic','ZuzanaCaputova','BorutPahor',0, 0,'ParmelinG','RTErdogan','ZelenskyyUa',0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "45c5a2e1-ff70-407a-b4aa-210ad0320b40",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Presidents' twitter usernames as new column\n",
    "pre[\"accounts\"]=twitter_accounts\n",
    "pre.reset_index(inplace=True, drop=True)\n",
    "\n",
    "# Merging with World Values survey data\n",
    "pres=pd.merge(pre,survey,left_on=\"State\",right_on=\"country\")\n",
    "\n",
    "# Drop monarchs\n",
    "pres.drop([4,10,23,27,28,36,37,41],inplace=True)\n",
    "pres.reset_index(inplace=True)\n",
    "\n",
    "# New Column for categorizing those with active accounts and those without\n",
    "for i in range(len(pres)):\n",
    "    if pres.loc[i,\"accounts\"]==\"0\":\n",
    "        pres.loc[i,\"Active Twitter Account\"]= \"No\"\n",
    "    else:\n",
    "        pres.loc[i,\"Active Twitter Account\"]=\"Yes\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d119fc7c-a810-48ca-aea8-f556e047858c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting those with active Twitter accounts\n",
    "actives= pre.copy()\n",
    "for i in range(len(actives)):\n",
    "    if '0' in actives.loc[i,\"accounts\"]:\n",
    "        actives.drop([i],inplace=True)\n",
    "actives.reset_index(inplace=True,drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90a0428c-5d0c-41c8-a723-88b580ac6a06",
   "metadata": {},
   "source": [
    "**Twitter Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "efce8a5c-5743-477b-b889-2aaf7a2d6d55",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Retrieving id numbers and number of followers with usernames\n",
    "ids=pd.DataFrame()\n",
    "headers = {\"Authorization\": \"Bearer {}\".format(apikeys.BEARER_TOKEN)}\n",
    "for account in actives[\"accounts\"].unique():\n",
    "    url = f\"https://api.twitter.com/2/users/by?usernames={account}&tweet.fields=author_id&user.fields=public_metrics\"\n",
    "    response = requests.request(\"GET\", url, headers=headers)\n",
    "    tweets = response.json()\n",
    "    tweets=pd.json_normalize(tweets[\"data\"])\n",
    "    ids=ids.append(tweets)\n",
    "    ids.reset_index(drop=True, inplace=True)\n",
    "    time.sleep(2)\n",
    "\n",
    "ids.rename(columns={\"public_metrics.followers_count\":\"followers\"},inplace=True)\n",
    "ids=ids[[\"id\",\"username\",\"followers\"]]\n",
    "\n",
    "# Merge for the country names\n",
    "ids_pre=pd.merge(ids,pre,how=\"left\",left_on=\"username\",right_on=\"accounts\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "a63ff3a6-0516-4df5-b462-6ccfb0daffd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Retrieving last 100 mentions with id numbers\n",
    "tweets_all=pd.DataFrame()\n",
    "n = datetime.datetime.now(dt.timezone.utc)\n",
    "end_time = n.isoformat()\n",
    "\n",
    "headers = {\"Authorization\": \"Bearer {}\".format(apikeys.BEARER_TOKEN)}\n",
    "for i in ids[\"id\"].unique():\n",
    "    url=f\"https://api.twitter.com/2/users/{i}/mentions\"\n",
    "    params={\n",
    "        \"end_time\":end_time,\n",
    "        \"max_results\":100,\n",
    "        \"tweet.fields\":\"created_at,lang,public_metrics,text\",\n",
    "        \"expansions\":\"author_id\",\n",
    "        \"user.fields\":\"created_at,description,entities,id,name,public_metrics,username\"}\n",
    "    response = requests.request(\"GET\", url, headers=headers,params=params)\n",
    "    tweets = response.json()\n",
    "    tweet=pd.json_normalize(tweets[\"data\"])\n",
    "    tweet[\"users\"]=i\n",
    "    tweets_all=tweets_all.append(tweet)\n",
    "    tweets_all.reset_index(drop=True, inplace=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "07cc3ea0-0090-4c29-80af-252b3271645a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merging and sorting\n",
    "tweets_df=pd.merge(tweets_all,ids, right_on=[\"id\",\"username\"],left_on=[\"users\",\"username\"])\n",
    "tweets_df['created_at']=pd.to_datetime(tweets_df['created_at'])\n",
    "tweets_df=tweets_df.sort_values(by=['created_at'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "91c4c696-322e-4c61-bcd4-ab2906b779f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating the frequency of mentions\n",
    "means= tweets_df.groupby('username')['created_at'].apply(lambda x: x.diff().mean())\n",
    "means=means.to_frame()\n",
    "means.reset_index(inplace=True)\n",
    "for i in range(len(means)):\n",
    "    means.loc[i,\"seconds\"]= means.loc[i,\"created_at\"].total_seconds()\n",
    "    \n",
    "# Merge to collect all information together\n",
    "mean=pd.merge(means,ids_pre[[\"username\",\"State\",\"followers\"]],how=\"left\",left_on=\"username\",right_on=\"username\")\n",
    "mean.rename(columns={\"State\":\"country\"},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "02263945-5896-4c20-a524-2282c300ab9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge with population data\n",
    "mean_pop=pd.merge(mean,df_pop, on=\"country\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "650c336f-a0a4-40b4-9bea-7550a09fdf06",
   "metadata": {},
   "source": [
    "## Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2d966d2b-5aab-482d-a49f-d73064bb8d9b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Calculate mention score\n",
    "mean[\"mention_score\"]=1/(mean[\"followers\"]*mean[\"seconds\"])\n",
    "\n",
    "# Merge with World Values Survey data\n",
    "mean_survey=pd.merge(mean,survey,on=\"country\")\n",
    "\n",
    "# Transforming to log-scale\n",
    "mean_survey[\"ln_mention\"]=np.log(mean_survey[\"mention_score\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0335fc99-97b5-4b4a-bfbd-4ab3b2d04aed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Regression model for mention score and government responsibility\n",
    "mean_survey.rename(columns={\"Government responsibility\":\"Government_responsibility\"},inplace=True)\n",
    "\n",
    "form_3 = 'ln_mention ~ Government_responsibility'\n",
    "fit_3 = smf.ols(formula = form_3, data = mean_survey).fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5e13775-7e6b-4fbd-bfce-92cf259394aa",
   "metadata": {},
   "source": [
    "## Figures"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cce2db0e-e566-417a-b9ab-55d4b26ce0aa",
   "metadata": {},
   "source": [
    "**Figure 4**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "3d6b2bc7-2ee4-44eb-ba28-fd91b3f9988a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Regression model with log-log scale\n",
    "def figure_4():\n",
    "    mean_pop[\"ln_pop\"]=np.log(mean_pop[\"population(m)\"])\n",
    "    mean_pop[\"ln_seconds\"]=np.log(mean_pop[\"seconds\"])     \n",
    "    \n",
    "    g = sns.lmplot(x=\"ln_pop\", y=\"ln_seconds\", data=mean_pop)\n",
    "    g.fig.suptitle(\"Figure 4: Regression Model for Frequency of Mentions and Population in millions\",\n",
    "                      fontsize=15, fontdict={\"weight\": \"bold\"})\n",
    "    \n",
    "    g.set(xlabel='Log(Population(m))', ylabel='Log(Average seconds between consecutive mentions)')\n",
    "    plt.savefig(\"Figure 4.png\",bbox_inches='tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd21dc95-894a-4a6e-b934-0bd67c9f899c",
   "metadata": {},
   "source": [
    "**Figure 5**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "1e3c3665-01a9-4add-acfa-fe38d95ed8aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Violin plot for presidents with an active Twitter account and those without one\n",
    "def figure_5():\n",
    "    g = sns.violinplot(y=pres[\"Government responsibility\"],x=pres[\"Active Twitter Account\"]) \n",
    "    g.set_title(\"Figure 5: Differences of Having an Active Twitter Account\",\n",
    "                      fontsize=15, fontdict={\"weight\": \"bold\"})\n",
    "    \n",
    "    g.set(xlabel='Active Twitter Account', ylabel='Government responsibility')\n",
    "    plt.savefig(\"Figure 5.png\",bbox_inches='tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afe7a443-be97-4da3-8e36-329e5260f4aa",
   "metadata": {},
   "source": [
    "**Figure 6**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "4a657920-5eeb-4658-8b01-a10fbd703e1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Regression model for mention score and government responsibility\n",
    "def figure_6():\n",
    "    g = g = sns.lmplot(x=\"Government_responsibility\",y=\"ln_mention\",data=mean_survey)\n",
    "    g.fig.suptitle(\"Figure 6: Regression Model for Government Responsibility and Mention Scores by Country\",\n",
    "                      fontsize=15, fontdict={\"weight\": \"bold\"})\n",
    "    \n",
    "    g.set(xlabel='Government Responsibility', ylabel='Log(Mention Score)')\n",
    "    plt.savefig(\"Figure 6.png\",bbox_inches='tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "806fb58b-b5c0-4ca9-8293-7e53d7d311ff",
   "metadata": {},
   "source": [
    "## Tables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0846cdc4-ee88-4702-b78d-6b6c1cb183d2",
   "metadata": {},
   "source": [
    "**Table 4**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0d482c3c-5946-4eb8-9c2d-ba69c7877452",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Summary statistics for frequency and followers\n",
    "table_4 = pd.DataFrame([],columns=[\"Mean\",\"Std Dev.\",\"Min.\",\"Max.\"],index=[\"Seconds\",\"Followers\"])\n",
    "\n",
    "table_4.loc[\"Seconds\"]= [mean[\"seconds\"].mean(),mean[\"seconds\"].std(),mean[\"seconds\"].min(),mean[\"seconds\"].max()]\n",
    "\n",
    "table_4.loc[\"Followers\"]= [mean[\"followers\"].mean(),mean[\"followers\"].std(),mean[\"followers\"].min(),mean[\"followers\"].max()]\n",
    "\n",
    "# Countries with minimum and maximum values \n",
    "min_fo = mean[\"followers\"].idxmin()\n",
    "min_freq = mean[\"seconds\"].idxmax()\n",
    "\n",
    "min_followers=mean.loc[min_fo,\"country\"]\n",
    "min_freq=mean.loc[min_freq,\"country\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "814b899c-5d0b-4511-9052-e9a093e0b079",
   "metadata": {},
   "source": [
    "**Table 5**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "0fc943a6-18f7-4df9-99a5-f843c54d5f17",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Important parameters for the Regression Model of seconds and population\n",
    "form_2 = 'ln_seconds ~ ln_pop'\n",
    "fit_2 = smf.ols(formula = form_2, data = mean_pop).fit()\n",
    "\n",
    "table_5 = pd.DataFrame([],columns=[\"R squared\",\"Coefficient\"],\n",
    "                           index=[\"Regression Model\"])\n",
    "\n",
    "table_5.loc[\"Regression Model\"]= [fit_2.rsquared,fit_2.params[1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99d6b26f-3230-4e97-9e25-38eae09ad77d",
   "metadata": {},
   "source": [
    "**Table 6**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "08a98843-b679-4bbd-8e4d-b63ae3eedd33",
   "metadata": {},
   "outputs": [],
   "source": [
    "# T-test for comparing the differences of having an active Twitter account or not\n",
    "test=stats.ttest_ind(pres[\"Government responsibility\"][pres[\"Active Twitter Account\"]==\"Yes\"], \n",
    "                pres[\"Government responsibility\"][pres[\"Active Twitter Account\"]==\"No\"])\n",
    "p_value=test[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3675a13-d6bb-4d83-9348-8ceb181a2ffd",
   "metadata": {},
   "source": [
    "**Table 7**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9b93c66d-51b0-4fbd-8a6c-ca9f5144e1cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Important Parameters for the Regression Model for mention score and government responsibility\n",
    "table_7 = pd.DataFrame([],columns=[\"R squared\",\"Coefficient for Gov. Resp.\",\"p-value for Gov. Resp.\"],\n",
    "                           index=[\"Regression Model\"])\n",
    "\n",
    "table_7.loc[\"Regression Model\"]= [fit_3.rsquared,fit_3.params[1],fit_3.pvalues[1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22be196e-7cc9-4686-a6ef-75713314392e",
   "metadata": {},
   "source": [
    "# Part 1: Introduction\n",
    "  The COVID-19, after its emergence in China in late 2019, spread swiftly and was declared a global pandemic by mid-March the following year by the World Health Organization (WHO). The rapid spread and the gravity of COVID-19 caused governments to start taking precautionary measures to decelerate the transmission. Some measures include wearing compulsory masks, closing schools and workplaces, lockdowns, cancellations of public events and gatherings, and contact tracing.  However, the policies implemented, the speed of first responses, and their enforcement standards varied among governments. These variations were moulded by many structural elements of respective societies, including the countries' overall resources and institutional structures. (Egger et al., 2021; Maor & Howlett, 2020) The cultural elements such as institutional trust and individualism further diversified the response to the pandemic. (Chen et al., 2021; Maaravi et al., 2021)\n",
    "   \n",
    "  Individualism and collectivism have been seen as key cultural features to explain cross-cultural differences in societies. (Hofstede, 1980; Hui&Triandis,1986; Parsons, 1951) \"The relationship between the individual and the collectivity in human society is not only a matter of ways of living together, it is intimately linked with societal norms. It, therefore, affects both people's mental programming and the structure and functioning of many institutions.\" (Hofstede, 2001) Individualism emphasizes individual autonomy and control, whereas collectivism prioritizes collective action and responsibility. These societal norms directly influence the organization and functioning of governance. The individualistic norms lay the foundations for liberalism as individualism emphasized \"the right to individual freedom and self-realization.\" (Wood, 1972) The limitation of state intervention in liberal theory protects both individual liberty and the rule of law. \"Limiting arbitrary power encourages confidence that the law will be fair and thereby increases the state's ability to secure cooperation without the imposition of force.\" (Starr, 2007) Therefore, one might question whether the individualistic nature of a society affects the government's response to COVID-19 by understating government action and responsibility.\n",
    "   Subsequently, the first part of this paper explores the following research question:\n",
    "    \n",
    "   > $RQ_1$: Do the opinions of citizens towards the responsibility of governments affect the initial governmental responses to COVID-19 in different countries? \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d525e45-9ab6-43da-9414-6991c014c4e2",
   "metadata": {},
   "source": [
    "## Methods\n",
    "The data I use to address my research question is from the OxCOVID19 Database. (Mahdi et al., 2020) To analyze the different courses of the pandemic in several countries, I use both the epidemiology table and the country statistics table. In particular, I calculate the daily cases from the epidemiology table and merge it with population data from the World Bank dataset to be able to evaluate daily confirmed cases per million through time. To analyze different governments' responses to COVID-19, I use data from the government response table, specifically the \"government response index.\" (Hale et al., 2020) The index is calculated by combining 16 policy actions including but not limited to the closure of schools, cancellation of public events and stay at home requirements. To explore the differences in the attribution of responsibility to governments, I use data from World Values Survey (Inglehart et al., 2014); specifically, the question on government responsibility where an answer value of 1 means \"People should take more responsibility\" and the value 10 means \"The government should take more responsibility\", and the values in between indicate intermediary answers. \n",
    "\n",
    "To be able to compare various initial responses to COVID-19, I construct an initial response score.\n",
    "\n",
    "$ Initial\\:Response\\:Score= \\frac{(GR_{t}+1)(GR_{T}- GR_{t}+1)}{Case\\:Rate}$   where; $Case\\:Rate=\\frac{(Cases_{T}- Cases_{t})}{T-t}$\n",
    "\n",
    "The small \"t\" refers to the date first cases are reported in a country and they big \"T\" refers to the date total cases go over 100. The government response index at the date first case seen in the country is multiplied by the increase in the government response index at the date in which the total cases reach a hundred.It is then divided by the average daily rate of new positive COVID-19 cases during the first hundred cases. The values in the numerator are all increased by 1 to avoid reduction to zero. \n",
    "           I take the answers to the government responsibility question and correlate it to the countries' different initial response scores to explore the relationship between these two variables."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46c54c4e-2541-4f80-a950-e27b7423dd1c",
   "metadata": {},
   "source": [
    "## Results\n",
    "After the first cases were confirmed in Wuhan, China, in late 2019, COVID-19 started spreading across the world. By March 11th, there were over 120,000 cases worldwide when WHO declared it a global pandemic. By the time this paper is written, after two years since the first confirmed case, there has been a total of over 260 million positive cases and 5 million deaths across the world. Yet, although some similarities exist, all countries' experiences with the virus were different."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "edee9284-dbde-4c01-b3f0-95d2f61cbacc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure_1()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9531b06c-9c84-46ac-baa7-68c3681c5b28",
   "metadata": {},
   "source": [
    "Figure 1 shows daily confirmed cases per million over time for a subset of countries. Although there are differences in the volume of daily new cases per million, most countries have experienced three waves of peaks. The severity of the waves and the overall course vary for many reasons, including healthcare capacities and standards and the demographic distribution in respective countries. Another critical factor affecting the severity of the effects of the disease is the policies governments implement to control and slow down the transmission. However, the speed and the intensity of responses varied amongst countries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3b04143c-898f-460b-81e5-a8eb9abd5fc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Table 1'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Mean</th>\n",
       "      <th>Std Dev.</th>\n",
       "      <th>Min.</th>\n",
       "      <th>Max.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Government Response</th>\n",
       "      <td>19.326201</td>\n",
       "      <td>15.446196</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Mean   Std Dev. Min.   Max.\n",
       "Government Response  19.326201  15.446196  0.0  68.23"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average total confirmed cases per million for a government response index of 68.23 is 20503.1 cases.\n"
     ]
    }
   ],
   "source": [
    "display(\"Table 1\")\n",
    "display(table_1)\n",
    "value= cases_by_govresp(68.23,gov_epi)\n",
    "print(\"The average total confirmed cases per million for a government response index of 68.23 is \"+ str(\"%.1f\" % value)+\" cases.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea9ab596-771d-4bef-b52d-7a74e99d221c",
   "metadata": {},
   "source": [
    "\n",
    "Table 1 illustrates the range of different responses at the date the first positive case was seen. The relatively high standard deviation indicates the high variance in the responses. There were countries where no response policies were implemented, but there were also countries whose response index was 68.23, a response score usually reported at a total of approximately 20,503 confirmed cases per million.\n",
    "\n",
    "The initial response score was derived to quantify both the speed and the intensity of the initial responses of countries at the very first 100 confirmed cases. It is normalized by the rate of new daily confirmed cases. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "2f5ba2fe-3434-4eef-ad15-b3d08b4933f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure_2()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cfa82e0-a24e-4d82-8937-be88083c27c6",
   "metadata": {},
   "source": [
    "The first row of plots in Figure 2 shows three random examples of countries with relatively lower initial response scores, their total confirmed cases, and the government response index. The second row of figures plots the same variables for countries with relatively higher initial response scores. These countries were more than half a standard deviation away from the mean of initial response scores. Countries with higher initial response scores either had higher government response values at the date of the first case or increased their responses to higher levels than countries with lower initial response scores or both. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8e780e57-10f2-4eb0-b2d2-21853e8ec3c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Table 2'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Mean</th>\n",
       "      <th>Std Dev.</th>\n",
       "      <th>Min.</th>\n",
       "      <th>Max.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Government Responsibility</th>\n",
       "      <td>4.809436</td>\n",
       "      <td>0.586923</td>\n",
       "      <td>3.742163</td>\n",
       "      <td>6.417677</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Mean  Std Dev.      Min.      Max.\n",
       "Government Responsibility  4.809436  0.586923  3.742163  6.417677"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The country with the minimum value is United Kingdom.\n",
      "The country with the maximum value is Georgia.\n"
     ]
    }
   ],
   "source": [
    "display(\"Table 2\")\n",
    "display(table_2) \n",
    "print(\"The country with the minimum value is \" + min_country + \".\")\n",
    "print(\"The country with the maximum value is \" + max_country +\".\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dad668cd-0521-4303-9aa8-304f93d93859",
   "metadata": {},
   "source": [
    "Table 2 describes the government responsibility answers from World Values Survey (Inglehart et al., 2014). The mean and the standard deviation suggests a tendency towards neutral answers, a value of 5 with a slight lean towards the answer \"People should take more responsibility.\" The minimum value where individual responsibility is valued more than governmental responsibility belongs to the United Kingdom, whereas the maximum value belongs to Georgia. These values also map onto the individualism scores of Hofstede (2011), where the United Kingdom with a score of 89 is one of the countries with the highest individualist scores.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "7f055c5e-81aa-4382-adfa-cd8d6a80748b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Table 3'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>R squared</th>\n",
       "      <th>Coefficient for Gov. Resp.</th>\n",
       "      <th>p-value for Gov. Resp.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Regression Model</th>\n",
       "      <td>0.123415</td>\n",
       "      <td>29.288303</td>\n",
       "      <td>0.026231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 R squared Coefficient for Gov. Resp. p-value for Gov. Resp.\n",
       "Regression Model  0.123415                  29.288303               0.026231"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure_3()\n",
    "display(\"Table 3\")\n",
    "display(table_3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "326ac332-019f-4a18-bc27-a6f409b84baf",
   "metadata": {},
   "source": [
    "Figure 3 and Table 3 explore the relationship between the amount of responsibility attributed to governments and the initial response scores. The low R<sup>2</sup> indicates a high amount of unexplained variance, which is understandable considering other factors affecting initial response scores, such as the overall resources of the respective countries. The variance in the initial responses cannot only be explained by the opinions on government responsibility. However, the low p-value suggests that the null hypothesis can be rejected and that there is indeed a relationship between these independent and dependent variables. Thus, the opinions on government responsibility did have an impact on the initial responses. By looking at both the regression model and the coefficient, one can conclude that this relationship is positive. Countries whose citizens think governments should take more responsibility than individuals had higher initial responses as the virus started transmitting in their territories."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85b91449-2e31-4eb3-9851-6a6aabac1aa5",
   "metadata": {},
   "source": [
    "# Part 2: Introduction\n",
    "\n",
    "Twitter has been a platform for users to share information and interact with each other through likes, retweets, and mentions. However, some accounts and their tweets receive more interaction than others. This popularity is usually referred to as \"influence.\" (Anger & Kittl, 2011; Bakshy et al., 2011; Cha et al., 2010) There are many methods to measure influence on Twitter. The number of followers, likes, retweets and mentions can all be factors in measuring the influence a user has on Twitter. There can be many underlying reasons to account for the level of influence users have, ranging from content to the social status of the person who owns the account.\n",
    "\n",
    "Some of the most influential figures on Twitter have been politicians and presidents of countries. Presidents have increasingly been using Twitter as a medium of communication. Twitter creates many opportunities for presidents; it provides citizens with political information, allows presidents to communicate directly with citizens, unlike the traditional unidirectional way, and allows them to respond to other politicians and recent events instantaneously. (Fontaine & Gomez, 2020) Citizens similarly benefit from this interactive nature of Twitter. They can start political discussions in a public space and include the politicians in the conversation. Therefore, the mentions of a president's account are a valuable resource of information. Other users mention the presidents' accounts for many reasons, including showing appreciation or critique. Although the content of the mentions is very important, the volume of mentions received can also be an important indicator. It might illustrate the amount of political discussion or perceptions of a president's approachability.\n",
    "\n",
    "In this part of the paper, I will compare the influence of presidents of several countries on Twitter through the frequency of mentions they receive. However, the number of followers an account has can affect the frequency of mentions received in various ways. The number of followers might simply show that more people are interested in the person's account, and therefore more people might mention the account. The frequency of mentions can also increase with more posts, and accounts with more followers tend to post more. (Huberman et al., 2009) Therefore in this paper, the frequency of mentions received will be normalized by the number of followers. By merging the Twitter data with the answers to the World Values Survey (Inglehart et al., 2014) question asking whether governments or individuals should take more responsibility, I will explore whether these opinions impact the frequency of mentions received by the presidents of the respective countries. Like explained in part one, the answers about government responsibility shed light on opinions on governments' roles. In this part, I will investigate whether these political opinions influence the frequency of mentions received by presidents.\n",
    "\n",
    "In sum, the second part of this paper investigates the following research question:\n",
    "\n",
    " > $RQ_2$: Do citizens' opinions towards the responsibility of governments affect the volume of mentions the countries' presidents receive on Twitter?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "981c8edb-a44a-4587-a190-514091211d4c",
   "metadata": {},
   "source": [
    "## Methods\n",
    "To address the second research question, I use the answers to the World Value Survey (Inglehart et al., 2014) asking about the opinions on government responsibility. For Twitter data, I use Twitter API v2 endpoints through the academic research access. I retrieved the id numbers and number of followers using usernames through the \"Users lookup\" endpoints. Then, I use the id numbers for the \"User mention timeline\" endpoint to retrieve the last 100 mentions of presidents with active Twitter accounts. I calculate the frequency of mentions by taking the mean of the differences in times of consecutive mentions. \n",
    "\n",
    "I, then, construct an equation for a mention score in an attempt to normalize these average frequencies in seconds with the number of followers the account has. The formulation of the score is similar to that of the \"interactor ratio\" (Anger & Kittl, 2011), which is an indicator of influence that calculates the number of mentions received per follower. Considering the mean of seconds between mentions is inversely proportional with the amount of interaction an account receives, we calculate the mention score as follows:\n",
    "\n",
    "\n",
    "$ Mention\\:Score= \\frac{1}{(Seconds)(Followers)}$\n",
    "\n",
    "Therefore, if both the seconds' value and the number of followers is high, the score takes its minimum value. This refers to the scenario in which the account has a high number of followers, but the frequency of mentions is low. For this scenario, the mention score is at its minimum. The mention score will be used as the response variable for the regression with the government responsibility answers of each country.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c497a46c-6a20-4877-8e8e-ee976fc1944f",
   "metadata": {},
   "source": [
    "## Results\n",
    "The influence that presidents of different countries have on Twitter vary. Although influence can be measured in many different ways with different indicators, in this paper, the focus will be on the number of followers and the frequency of mentions received."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "a90b0617-4ca4-4284-83d3-030b0240367a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Table 4'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Mean</th>\n",
       "      <th>Std Dev.</th>\n",
       "      <th>Min.</th>\n",
       "      <th>Max.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Seconds</th>\n",
       "      <td>3423.467818</td>\n",
       "      <td>4550.01908</td>\n",
       "      <td>2.285714</td>\n",
       "      <td>17906.020408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Followers</th>\n",
       "      <td>1183344.692308</td>\n",
       "      <td>3760066.176765</td>\n",
       "      <td>4653</td>\n",
       "      <td>18179525</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Mean        Std Dev.      Min.          Max.\n",
       "Seconds       3423.467818      4550.01908  2.285714  17906.020408\n",
       "Followers  1183344.692308  3760066.176765      4653      18179525"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The country with the least number of followers is Estonia.\n",
      "The country whose president is least frequently mentioned is Malta.\n"
     ]
    }
   ],
   "source": [
    "display(\"Table 4\")\n",
    "display(table_4)\n",
    "print(\"The country with the least number of followers is \" + min_followers + \".\")\n",
    "print(\"The country whose president is least frequently mentioned is \" + min_freq + \".\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34a28d9c-f952-4526-8f43-05f49f964d6f",
   "metadata": {},
   "source": [
    "The 26 presidents have a mean of over a million followers. The standard deviation and the range of the number of followers they have are very high, confirming that presidents have different influence levels on Twitter. The variation is also valid with the range and standard deviation of average seconds between mentions. The most frequently mentioned account receives a mention every 2 seconds, whereas the least frequently mentioned one receives every 5 hours on average. These two variables do not directly map onto each other. Although the president of Estonia is the one with the least followers, the least frequently mentioned president is Malta's. \n",
    "\n",
    "The levels of influence of presidents' accounts on Twitter vary for many reasons. Some countries have been known to receive more media and social media attention than others. One of the most important but challenging to unpack cause of this would be the social-political-economic status. However, a more direct and straightforward cause of the variety in the influence of presidents on Twitter is the size of that country's population. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "eea4d0ec-a6f2-4ca7-bafd-2b51e1237aa2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Table 5'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>R squared</th>\n",
       "      <th>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Regression Model</th>\n",
       "      <td>0.577477</td>\n",
       "      <td>-1.177215</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 R squared Coefficient\n",
       "Regression Model  0.577477   -1.177215"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure_4()\n",
    "display(\"Table 5\")\n",
    "display(table_5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6410d62-fbd4-42c0-9c09-dce5c2f46a57",
   "metadata": {},
   "source": [
    "The size of the country's population directly affects the frequency of mentions received. The relatively high R<sup>2</sup>  illustrates the strength of the relationship. The more populated a country is the less seconds between consecutive mentions on average. \n",
    "\n",
    "Could there be other ideological reasons to account for the variation in influence levels? Do perceptions of governments and the roles attached to them have effects on the Twitter presences of the head of those states? To address the research question, I use the accounts of presidents with active Twitter accounts. Of the 40 countries that responded to World Values Survey (Inglehart et al., 2014), only 26 had presidents with active Twitter accounts. I start the analysis by first investigating whether having an active account on Twitter relates to different opinions on government responsibility?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "7f41af00-2d29-4fd2-a719-549ce86f3f6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The p-value for the t-test is 0.26.\n"
     ]
    }
   ],
   "source": [
    "figure_5()\n",
    "print(\"The p-value for the t-test is \"+ str(\"%.2f\" % p_value) + \".\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36f9b257-bdad-4f79-81d3-ff12bf72daa1",
   "metadata": {},
   "source": [
    "Of the 14 heads of state with no active Twitter account, the 8 monarchs were excluded since almost all royals choose not to have a personal Twitter account. To visually compare the government responsibility answers of countries with a president with an active Twitter account and those that do not, I choose a violin plot. Then, I conduct a t-test to test if there is a significant difference between the means. The violin plot shows, and the t-test concludes with the high p-value that there is no significant relationship between a country's president having an active Twitter account and the responsibility attributed to that government by its citizens. \n",
    "\n",
    "By limiting the analysis to the presidents with an active Twitter account, we can test whether the amount of responsibility attributed to governments has a significant relationship with the volume of interactions received by the presidents' accounts, calculated by the mention score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "059b4ffb-34dd-407f-a464-6bf5a462acc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Table 7'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>R squared</th>\n",
       "      <th>Coefficient for Gov. Resp.</th>\n",
       "      <th>p-value for Gov. Resp.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Regression Model</th>\n",
       "      <td>0.231233</td>\n",
       "      <td>0.995061</td>\n",
       "      <td>0.012891</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 R squared Coefficient for Gov. Resp. p-value for Gov. Resp.\n",
       "Regression Model  0.231233                   0.995061               0.012891"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure_6()\n",
    "display(\"Table 7\")\n",
    "display(table_7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f5f0641-0d41-4ea8-abf4-a7c0c0f34e10",
   "metadata": {},
   "source": [
    "When the mention score is in its linear scale, the residuals are not uniformly scattered. Also, to be able to integrate the very large values to the model, I prefer a log scale. The relatively low R<sup>2</sup> suggests that the survey responses cannot explain much of the variance in the mention scores. Yet, the low p-value concludes that there is indeed a statistically significant effect of the amount of responsibility attributed to the governments on the mention scores of presidents' Twitter accounts. The positive coefficient indicates that the correlation is positive and that the countries in which citizens think governments should take more responsibility than the individuals have their presidents' Twitter accounts mentioned more even though they have fewer followers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dce2bb17-c2f6-416b-a3e2-d9c0d53046f8",
   "metadata": {},
   "source": [
    "# Limitations and Conclusions\n",
    "The claims made in the first part of this paper are dependent on the validity of the data used. The epistemology data is composed of information collected from different countries, which might have different transparencies about reporting data. The survey data from World Values Survey (Inglehart et al., 2014) might also be biased since a question about the government and politics in general may cause response bias by creating pressure to comply with the social norms, even more so in some countries than others. The claim that the countries where citizens think governments should take more responsibility than the individuals have a higher initial response rate raises questions about directionality. Do governments react to COVID-19 more swiftly and extensively because they are attributed more responsibility by their societies, or do citizens think that governments should take more responsibility because their governments actually have the means, capacity and intentions to act effectively in a time of emergence? To address this problem, future work can focus on the citizens' opinions on government responsibility and how those relate to the politics in a society. Does citizens' attribution of responsibility legitimize more government action, or are those opinions the very building blocks of politics in those societies?\n",
    "\n",
    "The results of the second part of this paper are limited by the nature of the data collected. Most importantly, the size of the data is limited. The size of the data is especially important for the claims made because this data only captures a limited amount of time. Therefore, the volume of mentions received might easily be affected by recent news or events in the countries. In addition, the time I retrieved these mentions corresponds to different times of the day in different countries, which would affect the frequency of mentions. These would complicate generalizations about the accounts' influence and interaction levels. Future work should integrate more data to comment on the bigger picture and spot patterns in a wider time range. The results from the second part suggest that there is indeed a significant relationship between the citizens' opinions on governments' responsibilities and the volume of mentions their presidents receive. This suggests that people's perception of governments' roles affects how much their politicians receive interaction on Twitter. But how can this result be interpreted? Are citizens who think governments should take more responsibility more actively participating in political debate? Or perhaps in countries where people think governments should take more responsibility, governments actually do take more responsibility, and therefore there are more political actions to debate on. A broader study addressing these questions can explore the social and political implications of the citizens' opinions on government roles. Methods like sentiment analysis can further effectively detect whether the mentions are predominantly approvals or critiques. Revealing the attitudes in the mentions would clarify what kind of political discussions the different perceptions of governments initiate.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "096672ce-86f4-445f-b11a-f665278ca286",
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   "source": [
    "# References\n",
    "Anger, I. & Kittl, C. (2011). Measuring influence on Twitter. I-KNOW. 31. 10.1145/2024288.2024326.\n",
    "\n",
    "Bakshy, E., Hofman, J., Mason, W. & Watts, D. (2011). Everyone's an Influencer: Quantifying Influence on Twitter. Proceedings of the 4th ACM International Conference on Web Search and Data Mining, WSDM 2011. 65-74. 10.1145/1935826.1935845.\n",
    "\n",
    "Cha, M., Haddadi, H., Benevenuto, F., & Gummadi, K. (2010). Measuring User Influence in Twitter: The Million Follower Fallacy. Proceedings of the International AAAI Conference on Web and Social Media, 4(1), 10-17. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/14033\n",
    "\n",
    "Fontaine, S.A., Gomez, D.M. (2020). Going Social: A Comparative Analysis of Presidents' Official and Social Media Messages. Presidential Studies Quarterly, 50: 507-538. https://doi.org/10.1111/psq.12676\n",
    "\n",
    "Thomas, H., Webster S., Petherick, A., Phillips, T., & Kira, B. (2020). Oxford COVID-19 Government Response Tracker. Blavatnik School of Government.\n",
    "\n",
    "Hofstede, G. (2001). Culture's consequences: Comparing values, behaviors, institutions, and organizations across nations. Thousand Oaks, Calif: Sage Publications.\n",
    "\n",
    "Hofstede, G. (2011). Dimensionalizing cultures: The Hofstede model in context. Online Readings in Psychology and Culture 2(1): 8.\n",
    "\n",
    "Huberman, B., Romero, D. & Wu, F. (2009). Social Networks that Matter: Twitter Under the Microscope. First Monday. 14. 10.2139/ssrn.1313405.\n",
    "\n",
    "Hui, C. H., & Triandis, H. C. (1986). Individualism-collectivism: A study of cross-cultural researchers. Journal of Cross-Cultural Psychology, 17(2), 225–248. https://doi.org/10.1177/0022002186017002006\n",
    "\n",
    "Inglehart, R., Haerpfer, C., Moreno, A., Welzel, C., Kizilova, K., Diez-Medrano, J., Lagos, M., Norris, P., Ponarin, E. & Puranen B. (Eds.). (2014). World Values Survey: Round Six - Country-Pooled Datafile Version: http://www.worldvaluessurvey.org/WVSDocumentationWV6.jsp. Madrid: JD Systems Institute.\n",
    " \n",
    "Egger, C., Magni-Berton, R., Roché, S. & Aarts, K. (2020). I do it my way. Understanding policy variation in pandemic response across Europe. 10.31219/osf.io/mscb8.\n",
    "\n",
    "Mahdi, A., Błaszczyk, P., Dłotko, P., Salvi, D., Chan, T., Harvey, J., Gurnari, D., Wu, Y., Farhat, A., Hellmer, N., Zarebski, A., Hogan, B. & Tarassenko. L. (2020). Oxford COVID-19 Database: a multimodal data repository for better understanding the global impact of COVID-19. University of Oxford. medRxiv (doi: https://doi.org/10.1101/2020.08.18.20177147).\n",
    "\n",
    "Maor, M. & Howlett, M. (2020). Explaining variations in state COVID-19 responses: psychological, institutional, and strategic factors in governance and public policy-making. Policy Design and Practice. 3:3. 228-241. DOI: 10.1080/25741292.2020.1824379\n",
    "\n",
    "Starr, P. (2007). Freedom's power: The true force of liberalism. New York: Basic Books.\n",
    "\n",
    "Parsons, T. (1951). The social system. Glencoe. Ill: Free Press.\n",
    "\n",
    "Wood, E. M. (1972). Mind and Politics: An Approach to the Meaning of Liberal and Socialist Individualism. University of California Press. ISBN 0-520-02029-4 pp. 6–7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "201dd581-7743-4f01-ad30-6b07be04b96c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Word Count \n",
    "from notebook import notebookapp\n",
    "from ipykernel import get_connection_file\n",
    "from pathlib import Path\n",
    "import requests \n",
    "\n",
    "def notebook_path():\n",
    "    \n",
    "    connection_file = Path(get_connection_file())\n",
    "    kernel_id = connection_file.stem.split('-',1)[1]\n",
    "\n",
    "    for srv in notebookapp.list_running_servers():\n",
    "        if srv['token']=='' and not srv['password']:  \n",
    "            req = requests.get(srv['url']+'api/sessions')\n",
    "        else:                \n",
    "            req = requests.get(srv['url']+\n",
    "                'api/sessions?token='+srv['token'])\n",
    "\n",
    "        sessions = req.json()\n",
    "\n",
    "        for sess in sessions:\n",
    "            if sess['kernel']['id'] == kernel_id:\n",
    "                return Path(srv['notebook_dir']) / sess['notebook']['path']\n",
    "\n",
    "    return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "0a60984f-4a3f-42d1-a9b3-d392affeb881",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The word count, excluding references, is 3250\n"
     ]
    }
   ],
   "source": [
    "import nbformat \n",
    "\n",
    "with open(\"Sum.ipynb\", encoding='utf-8') as f:\n",
    "    nb = nbformat.read(f,4)\n",
    "    \n",
    "word_count = 0\n",
    "for cell in nb.cells:\n",
    "    if cell.cell_type == \"markdown\" and cell['source'][:12] != \"# References\":\n",
    "        word_count += len(cell['source'].replace('#','').lstrip().split(' '))\n",
    "\n",
    "print(f\"The word count, excluding references, is {word_count}\")"
   ]
  }
 ],
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