data_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "from statsmodels.stats.inter_rater import fleiss_kappa, aggregate_raters\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "Terry = []\n",
    "Zilu = []\n",
    "Feng = []\n",
    "with open('labelled_users_by3.csv', newline='') as csvfile:\n",
    "    spamreader = csv.DictReader(csvfile)\n",
    "    for row in spamreader:\n",
    "        Terry.append(row['Terry'])\n",
    "        Zilu.append(row['Zilu'])\n",
    "        Feng.append(row['Feng'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8270464594513035"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohen_kappa_score(Terry, Zilu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7730572368293015"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohen_kappa_score(Terry, Feng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8499356685935886"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohen_kappa_score(Feng, Zilu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8172310471840346"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contingency_table, sum_ratings = aggregate_raters(np.transpose([Terry,Zilu,Feng]))\n",
    "fleiss_kappa(contingency_table, method='fleiss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Contingency Table:\n",
      " [[0 3]\n",
      " [2 1]\n",
      " [3 0]\n",
      " ...\n",
      " [3 0]\n",
      " [3 0]\n",
      " [3 0]]\n",
      "Sum of Ratings for Each Item: ['0' '1']\n"
     ]
    }
   ],
   "source": [
    "print(\"Contingency Table:\\n\", contingency_table)\n",
    "print(\"Sum of Ratings for Each Item:\", sum_ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# all aggregated, for data visual\n",
    "real_total_posts = []\n",
    "fake_total_posts = []\n",
    "with open('for_analysis.csv', newline='') as csvfile:\n",
    "    spamreader = csv.DictReader(csvfile)\n",
    "    for row in spamreader:\n",
    "        if row['fake'] == '1':\n",
    "            fake_total_posts.append(int(row['total_posts']))\n",
    "        else:\n",
    "            real_total_posts.append(int(row['total_posts'])) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4210"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(fake_total_posts)+len(real_total_posts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15584.245721271394"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statistics.mean(fake_total_posts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, \"histogram for fake users's total posts number\")"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 4))\n",
    "plt.hist(fake_total_posts, bins=150, color='red')\n",
    "# plt.xticks(rotation='vertical')\n",
    "plt.xlabel('number of posts')\n",
    "plt.ylabel('frequency')\n",
    "plt.title(\"histogram for fake users's total posts number\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, \"histogram for real users's total posts number\")"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 4))\n",
    "plt.hist(real_total_posts, bins=150, color='blue')\n",
    "# plt.xticks(rotation='vertical')\n",
    "plt.xlabel('number of posts')\n",
    "plt.ylabel('frequency')\n",
    "plt.title(\"histogram for real users's total posts number\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('for_analysis.csv')\n",
    "df['description_length'] = df['description'].astype(str).apply(lambda x: len(x.split()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     4210\n",
       "unique       2\n",
       "top       True\n",
       "freq      3240\n",
       "Name: allow_question, dtype: object"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['allow_question'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12.837767220902613, 0, 242)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statistics.mean(df['description_length']), min(df['description_length']), max(df['description_length'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      818\n",
       "unique       2\n",
       "top       True\n",
       "freq       482\n",
       "Name: allow_question, dtype: object"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['fake'] == 1]['allow_question'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pie chart for question and question_anon for fake users\n",
    "plt.pie([482, 818-482], labels=['True', 'False'], colors=['blue', 'red'])\n",
    "\n",
    "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "plt.title('Allow Question for Fake users')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     3387\n",
       "unique       2\n",
       "top       True\n",
       "freq      2755\n",
       "Name: allow_question, dtype: object"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['fake'] == 0]['allow_question'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pie chart for question and question_anon for real users\n",
    "plt.pie([2755, 3387-2755], labels=['True', 'False'], colors=['blue', 'red'])\n",
    "\n",
    "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "plt.title('Allow Question for Real users')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.9.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}