ct-2db-1kw.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7ff7febb-bc3f-4b60-a6ee-439916086df0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "eee6512f-ce2c-4ed0-985c-4a345e693ec3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Search results from last 12 months (from July 2024), \n",
    "# gaza-pot.csv inlcude posts where the page admin is in Palestine Occupied Territories\n",
    "# gaza-us inlcude posts where the page admin is in USA\n",
    "# Page Categories filter: Media News Company, Newsagent Newsstand, News Personality, News Site, Newspaper, \n",
    "# Political Candidate, Political Party, Politician, Political Organisation \n",
    "\n",
    "\n",
    "db1 = pd.read_csv('gaza-pot.csv')\n",
    "db2 = pd.read_csv('gaza-us.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "657176f2-8af0-4448-9051-8947fc0b0191",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert object-type columns to string, except integer columns\n",
    "for col in db1.columns:\n",
    "    if db1[col].dtype == 'object':\n",
    "        db1[col] = db1[col].astype(str)\n",
    "\n",
    "for col in db2.columns:\n",
    "    if db2[col].dtype == 'object':\n",
    "        db2[col] = db2[col].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5557c029-9912-45a5-9c79-4537c5db0b9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the keyword to search for\n",
    "# Users can use different keywords here just run code without any chances for new output\n",
    "keyword = 'genocide'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "16bfaa0d-777d-4d4c-ab2f-4851d37a1f39",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to find occurrences of keywords in the \"Message\" column\n",
    "def find_keyword_occurrences(df, keyword):\n",
    "    occurrences = df[df['Message'].str.contains(keyword, case=False)].index.tolist()\n",
    "    return occurrences\n",
    "\n",
    "# Function to find the page with the most mentions of a keyword\n",
    "def find_top_page(df, keyword):\n",
    "    occurrences = find_keyword_occurrences(df, keyword)\n",
    "    page_counts = Counter(df.loc[occurrences, 'Page Name'])\n",
    "    return page_counts.most_common(1)[0][0]\n",
    "\n",
    "# Function to find the top 5 most shared URLs\n",
    "def find_top_shared_urls(df):\n",
    "    url_shares = df.groupby('URL')['Shares'].sum().sort_values(ascending=False)\n",
    "    return url_shares.head(5).index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "107d8541-000e-4125-8b7b-8789afe2cdf2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keyword occurrences in Database 1: 2203\n",
      "Page with most mentions in Database 1: Palestine International Broadcast\n",
      "Keyword occurrences in Database 2: 382\n",
      "Page with most mentions in Database 2: AJ+\n",
      "\n",
      "Top 5 most shared URLs:\n",
      "Database 1:\n",
      "https://www.facebook.com/100069351265250/posts/652534593734910\n",
      "https://www.facebook.com/100069351265250/posts/641634944824875\n",
      "https://www.facebook.com/100063488323012/posts/858420086284261\n",
      "https://www.facebook.com/100069351265250/posts/641570688164634\n",
      "https://www.facebook.com/100069351265250/posts/651321983856171\n",
      "\n",
      "Database 2:\n",
      "https://www.facebook.com/100064529598989/posts/712940567533590\n",
      "https://www.facebook.com/100064529598989/posts/714530150707965\n",
      "https://www.facebook.com/100064529598989/posts/781787500648896\n",
      "https://www.facebook.com/100064529598989/posts/714437300717250\n",
      "https://www.facebook.com/100064529598989/posts/709291277898519\n"
     ]
    }
   ],
   "source": [
    "# Find keyword occurrences and top page for Database 1\n",
    "db1_occurrences = find_keyword_occurrences(db1, keyword)\n",
    "db1_top_page = find_top_page(db1, keyword)\n",
    "print(f\"Keyword occurrences in Database 1: {len(db1_occurrences)}\")\n",
    "print(f\"Page with most mentions in Database 1: {db1_top_page}\")\n",
    "\n",
    "# Find keyword occurrences and top page for Database 2\n",
    "db2_occurrences = find_keyword_occurrences(db2, keyword)\n",
    "db2_top_page = find_top_page(db2, keyword)\n",
    "print(f\"Keyword occurrences in Database 2: {len(db2_occurrences)}\")\n",
    "print(f\"Page with most mentions in Database 2: {db2_top_page}\")\n",
    "\n",
    "# Find top 5 most shared URLs for each database\n",
    "db1_top_shared_urls = find_top_shared_urls(db1)\n",
    "db2_top_shared_urls = find_top_shared_urls(db2)\n",
    "\n",
    "print(\"\\nTop 5 most shared URLs:\")\n",
    "print(\"Database 1:\")\n",
    "for url in db1_top_shared_urls:\n",
    "    print(url)\n",
    "\n",
    "print(\"\\nDatabase 2:\")\n",
    "for url in db2_top_shared_urls:\n",
    "    print(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "777faf54-4951-4f17-be95-8c78f2649f49",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming you have the keyword occurrences data from the previous code\n",
    "labels = ['Database 1', 'Database 2']\n",
    "sizes = [len(db1_occurrences), len(db2_occurrences)]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "ax.pie(sizes, labels=labels, autopct='%1.1f%%')\n",
    "ax.axis('equal')  # Equal aspect ratio ensures that pie is circular.\n",
    "ax.set_title(f'Occurrence of the keyword {keyword} in databases')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3bf219e8-7f04-45be-adbc-4663f5800a4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export rows containing the keyword to CSV\n",
    "def export_keyword_rows(df, keyword, filename):\n",
    "    occurrences = find_keyword_occurrences(df, keyword)\n",
    "    keyword_rows = df.loc[occurrences, ['Page Name', 'Post Created', 'Shares', 'Message', 'URL']]\n",
    "    keyword_rows.to_csv(filename, index=False)\n",
    "\n",
    "export_keyword_rows(db1, keyword, f'db1_rows_with_{keyword}.csv')\n",
    "export_keyword_rows(db2, keyword, f'db2_rows_with_{keyword}.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e4f6697-b6b5-4ee3-a9d6-06c644549e64",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.8"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}