all_tweets.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 根据all_tweets产生的数据结果进行分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "from my_weapon import *\n",
    "from collections import defaultdict, Counter\n",
    "from IPython.display import display\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "import matplotlib\n",
    "matplotlib.rcParams[\"font.size\"] = 14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading all tweets_csv ...\n",
      "finished!\n"
     ]
    }
   ],
   "source": [
    "print(\"loading all tweets_csv ...\")\n",
    "all_tweets = pd.read_csv(\"disk/all-tweets.csv\", dtype=str)\n",
    "all_tweets = all_tweets.astype(\n",
    "    {\"is_IRA\": int, \"is_source\": int, \"dt\": datetime})\n",
    "print(\"finished!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "mbfc_labels = [\n",
    "    \"fake\",\n",
    "    \"right\",\n",
    "    \"right leaning\",\n",
    "    \"center\",\n",
    "    \"left leaning\",\n",
    "    \"left\",     \n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13453040"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_tweets[all_tweets.is_source==1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ira_tweets = all_tweets[all_tweets.is_IRA==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "fake_tweets = all_tweets[all_tweets.c_sci_f!=\"-1\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "746 2873\n",
      "2022254 4263734\n"
     ]
    }
   ],
   "source": [
    "print(len(fake_tweets[(fake_tweets.is_IRA==1) & (fake_tweets.is_source==1)]), \\\n",
    "len(fake_tweets[(fake_tweets.is_IRA==1) & (fake_tweets.is_source==0)]))\n",
    "\n",
    "print(len(fake_tweets[(fake_tweets.is_IRA==0) & (fake_tweets.is_source==1)]), \\\n",
    "len(fake_tweets[(fake_tweets.is_IRA==0) & (fake_tweets.is_source==0)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "left leaning     0.325800\n",
       "left             0.200568\n",
       "fake             0.188303\n",
       "right            0.151647\n",
       "center           0.070091\n",
       "right leaning    0.063590\n",
       "Name: c_mbfc, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5]), <a list of 6 Text xticklabel objects>)"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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++ugjm++oUqVKpKenF3khT0lJwWw233TB4qlTp+jTpw8mk4kvvviCcuXKFTnWpUuXivS/dOkSFSpUsLkeERER+RebAsHXX3/NpEmT6Nmzp/Vdup+fH/PmzfvDcwncyN/fH1dX1yIL//bu3UtAQAAuLkUnKq5evcoLL7yAl5cXX3zxRbGPJYKCgti7d691+9y5c5w9e9bmBY4iIiJSlE2BYMGCBYwbN47Bgwfj7OwMwDPPPMPEiROJjY216Y48PDzo2bMnUVFRJCYmsnnzZhYsWEC/fv2A67MFOTk5AMyYMYMrV67w7rvvkp+fT0pKCikpKVy7ds16399++y2xsbEkJyczcuRIWrVqRc2aNW93/CIiIoKNawhOnDhx03ffwcHBt7Wyf/To0UyYMIH+/ftTtmxZhg0bRkREBABhYWFMnjyZyMhI1q1bR0ZGBj179ixy++7duzN16lQaN27MxIkTmTVrFlevXqVFixZMnDjR5jpERESkKJsCQZUqVUhKSqJGjRpF2nfu3EmVKlVsvjMPDw+mTJly09MdJycnW//9ww8//MdjPf744zz++OM237eIiIjcmk2B4MUXX2TChAmkpKRgGAa7d+9m5cqVfPbZZ7z++uv2rlFERETszKZA8NRTT5GXl8ecOXPIyclh7NixVKpUiZEjR9K7d2971ygiIiJ2ZlMgSEtLo0+fPvTp04fU1FTMZjOenp72rk1EREQcxKZvGbRo0YK+ffvy2WefkZGRoTBwAyO3dJ8dsbSPT0RErrNphmDp0qVs376ddevWER0dTc2aNWnXrh3t2rWjSZMm9q7xjmZyNXNqaK+SLsNuany8oqRLEBERB7ApEDRq1IhGjRoxbNgwrl69Snx8PBs3bqRv377cf//9fP/99/auU0REROzI5msZ5Ofnc+DAAXbv3s0PP/zAvn37cHNzIzAw0J71iYiIiAPYFAheeukl9u3bB0DDhg1p3rw5w4YNIygoyHrmQhEREbl72RQIjh8/Tk5ODi1atODRRx/l4Ycfxs/Pz961iYiIiIPYFAi+++47Tpw4wY4dO9i5cyezZ8/GZDLRrFkzHn74Yfr06WPvOkVERMSObF5D8NBDD/HQQw/xzDPPcO7cORYsWMDy5cvZuHGjAoGIiMhdzqZAkJmZye7du/n+++/ZuXMnx48fp169ejz//PO0bdvW3jWKiIiIndkUCJo3b46LiwuhoaH06dOHdu3a3dZFjUREROTOZlMgeP/992nevHmxMxRaLBa2bdtGeHi4XYoTERERx7Dp1MUvv/wyFkvxU9ieOXOGN954408vSkRERBzrljMES5Ys4dNPPwXAMAx69eqFk1PR/JCenk6tWrXsW6GIiIjY3S0DQWRkJOnp6RQUFDBr1iy6detGmTJlrPtNJhNly5alY8eODilURERE7OeWgcDd3Z3BgwcDUKVKFbp27YrZbHZYYSIiIuI4Ni0qfPzxx+1dh4iIiJQgmxYVioiISOmmQCAiIiK3DgTR0dGkpaUBcPbsWQzDcFhRIiIi4li3DAQxMTFcu3YNgPbt23PlyhWHFSUiIiKOdctFhdWrV+fll1/G398fwzB4++23cXNzu2nfyZMn261AERERsb9bBoKpU6cyZ84cLly4gMlk4uLFi7i6ujqyNhEREXGQWwaCBg0a8P777wPQrl07PvjgA8qVK+ewwkRERMRxbDoPwZYtWzAMg23btnHkyBFcXFyoV68eDz/8MM7OzvauUUREROzMpkBw9epVXnzxRX755RfKlStHQUEBaWlpNGjQgAULFnD//ffbu04RERGxI5vOQzB58mTy8/NZs2YNO3fu5IcffuDbb7/FMAymTp1q7xpFRETEzmwKBFu3bmX8+PHUqVPH2la3bl3Gjh3L5s2b7VaciIiIOIZNgcAwDLy9vYu133///WRnZ//pRYmIiIhj2RQIgoODmTdvHvn5+da2/Px85s6dS6NGjexWnIiIiDiGTYsK33zzTfr06UN4eLg1ACQmJpKRkcGCBQvsWqCIiIjYn00zBPXr12f16tV07tyZrKws8vPzeeyxx1i7di2BgYH2rlFERETszKYZAoBq1aoxYsQIe9YiIiIiJcShlz+2WCyMGzeO0NBQWrZsybx58/7jbfbs2UObNm2KtXfq1AlfX98iP4cOHbJD1SIiIqWfzTMEf4bo6GgSEhJYuHAh58+fZ8SIEVStWpWuXbvetH9ycjKvvvpqsbMhWiwWTp06xdKlS6lRo4a1XadWFhER+e84bIYgKyuL2NhYxowZQ2BgIB06dGDAgAHExMTctP+yZcvo3bs35cuXL7bv+PHjmEwmGjZsSIUKFaw/Li4OzTciIiKlhsMCQVJSEhaLhZCQEGtbSEgIBw4cIC8vr1j/HTt2EB0dzfPPP19s37Fjx6hevbquvngHM/KL/5+WJqV9fCJy77H5LXVcXByLFi3i8OHDODk54e/vz4ABA2jdurVNt09JScHb2xs3Nzdrm4+PD7m5uaSmplKxYsUi/WfNmgXAypUrix3r6NGjODs7M2DAAA4dOkStWrX461//SlBQkK3DETszObuQszy6pMuwG/entcBWREoXm2YIli1bxl//+lceeugh3nzzTf7v//6PihUrMmzYMNauXWvTHWVnZ2M2m4u0FW5bLJbbKvrYsWOkp6fTp08f5s6dS506dejfvz+nT5++reOIiIjIdTbNEMyfP58xY8bw7LPPWtuee+45goKC+PDDD+nSpct/PIabm1uxF/7CbQ8Pj9upmWnTpvH777/j6ekJwIQJE9i3bx9ff/01L7/88m0dS0RERGycIUhJSaFFixbF2lu1amXzu/JKlSqRnp5eJBSkpKRgNptvep2EP+Lq6moNAwAmk4natWtz8eLF2zqOiIiIXGdTIGjdujXLly8v1r5hwwYeffRRm+7I398fV1dXEhISrG179+4lICDgtr8d8MQTTzB37lzrdkFBAcnJydSuXfu2jiMiIiLX2fRKXKNGDb744gt+/PFHmjVrhouLCz///DM7d+4kPDyccePGWftOnDjxpsfw8PCgZ8+eREVF8e6775KSksKCBQus/VNSUvDy8sLd3f0/1tOmTRs+/fRTfH19qVGjBp999hlpaWn06tXLluGIiIjIv7EpECQmJlpX8B88eNDa3rRpU65cucKVK1eA61P3f2T06NFMmDCB/v37U7ZsWYYNG0ZERAQAYWFhTJ48mcjIyP9Yz5AhQygoKOCtt94iNTWVoKAgPvvsM7y8vGwZjoiIiPwbmwLBF1988afcmYeHB1OmTGHKlCnF9iUnJ9/0NpGRkcVCgrOzM6+88gqvvPLKn1KXiIjIvc7mD+/Pnj3L4sWLOXLkCC4uLtSrV4+nnnqKatWq2bM+ERERcQCbFhUeOnSI7t27ExcXh4eHB87OzqxevZoePXqQlJRk7xpFRETEzmyaIZgyZQqtWrUiOjraerrg3NxcRo0axXvvvcenn35q1yJFRETEvmyaIfjpp58YOnRokWsHuLq6MmjQoCJfIxQREZG7k02B4L777iMzM7NYe0ZGhq4wKCIiUgrYFAjatGnD3//+d06ePGlt++2333jnnXdsvriRiIiI3Llsenv/+uuv88ILL9CpUyfKlSsHwJUrVwgKCmL06NF2LVBERETsz6ZAkJ+fz4oVK9i+fTtHjhzBzc2NunXr8sgjj9i7PhEREXEAmwJBr169+OCDD2jdurU+IhARESmFbFpDYBgGZrPZ3rWIiIhICbF5hmDAgAFERkZSvXr1Yhcg6t69u12KExEREcewKRB8/PHHAMyZM6fYPpPJpEAgIiJyl7MpEOj0xCIiIqXbHwaCrKwsdu3ahZubG8HBwZQtW9ZRdYmIiIgD3TIQJCUlMWDAAC5dugRAxYoV+fDDD2nUqJHDihMRERHHuOW3DKZNm0aNGjVYtmwZsbGx1KpVi6ioKEfWJiIiIg5yyxmCn376iUWLFuHv7w/A22+/TadOncjKyqJMmTIOK1BERETs75YzBJmZmfj4+Fi3a9SogbOzM1evXnVIYSIiIuI4twwEBQUFODkV3e3i4kJ+fr7dixIRERHHsulMhSIiIlK6/eHXDhctWoSHh4d1Oz8/nyVLluDt7V2k3+DBg+1TnYiIiDjELQNB1apV+cc//lGkzcfHh/Xr1xdpM5lMCgQiIiJ3uVsGgi1btjiyDhERESlBWkMgIiIiCgQiIiKiQCAiIiIoEIiIiAgKBCIiIoICgYiIiKBAICIiIigQiIiICAoEIiIiggKBiIiIoEAgIiIiKBCIiIgICgQiIiKCgwOBxWJh3LhxhIaG0rJlS+bNm/cfb7Nnzx7atGlTrH3Xrl10796doKAg+vbty4kTJ+xQsYiIyL3BoYEgOjqahIQEFi5cSFRUFLNnz2bNmjW37J+cnMyrr75KQUFBkfZz584xZMgQevTowYoVK/Dx8WHo0KHF+omIiIhtHBYIsrKyiI2NZcyYMQQGBtKhQwcGDBhATEzMTfsvW7aM3r17U758+WL7YmNj8fPzY+DAgdStW5dJkyZx7tw5du3aZe9hiIiIlEoOCwRJSUlYLBZCQkKsbSEhIRw4cIC8vLxi/Xfs2EF0dDTPP/98sX379+8nNDTUuu3h4UFAQAAJCQl2qV1ERKS0c1ggSElJwdvbGzc3N2ubj48Pubm5pKamFus/a9YswsPDb3msihUrFmkrX748Fy5c+HOLFhERuUc4LBBkZ2djNpuLtBVuWyyWP+VYt3scERERuc5hgcDNza3YC3bhtoeHx59yLHd39/+tSBERkXuUwwJBpUqVSE9PL/JCnpKSgtlsxtvb+7aPlZKSUqTt0qVLVKhQ4U+pVURE5F7jsEDg7++Pq6trkYV/e/fuJSAgABcXl9s6VlBQEPv27bNuZ2dn88svvxAcHPyn1SsiInIvcVgg8PDwoGfPnkRFRZGYmMjmzZtZsGAB/fr1A67PFuTk5Nh0rF69erF//35mz57N0aNHGTt2LFWrVuWRRx6x5xBERERKLYeemGj06NE0bNiQ/v3789ZbbzFs2DAiIiIACAsLIy4uzqbjVK9enQ8++IDVq1fTq1cvLl26xMcff4yTk87ELCIi8t+4vbn6/5GHhwdTpkxhypQpxfYlJyff9DaRkZFERkYWa2/dujWtW7f+02sUERG5F+kttYiIiCgQiIiIiAKBiIiIoEAgIiIiKBCIiIgICgQiIiKCAoGIiIigQCAiIiIoEIiIiAgKBCIiIoICgYiIiKBAICIiIigQiIiICAoEIiIiggKBiIiIoEAgIiIiKBCIiIgICgQiIiKCAoGIiIigQCAiIiIoEIiIiAgKBCJyBygoMEq6BLsq7eOT0sGlpAsQEXFyMrE7Ka2ky7CbZn7eJV2CyH+kGQIRERFRIBAREREFAhEREUGBQERERFAgEHEoo6CgpEuwq9I+PpHSTN8yEHEgk5MTqYnbS7oMu3mg0aMlXYKI/Jc0QyAiIiIKBCIiIqJAICIiIigQiIiICAoEIiIiggKBiIiIoEAgIiIiODgQWCwWxo0bR2hoKC1btmTevHm37JuUlMTTTz9NUFAQkZGRJCYmWvcVFBTQqFEjfH19i/ykp6c7YhgiIiKljkNPTBQdHU1CQgILFy7k/PnzjBgxgqpVq9K1a9ci/bKyshgwYAARERFMmjSJZcuWMWjQIDZu3IinpyenTp3CYrGwZcsWzGaz9XZeXl6OHI6IiEip4bAZgqysLGJjYxkzZgyBgYF06NCBAQMGEBMTU6xvXFwcrq6ujBo1ijp16jBmzBi8vLxYu3YtAEePHqVq1apUq1aNChUqWH9MJpOjhiMiIlKqOCwQJCUlYbFYCAkJsbaFhIRw4MAB8vLyivTdv38/TZo0wcnpenkmk4kmTZqQkJAAwLFjx6hVq5ajShcRESn1HBYIUlJS8Pb2xs3Nzdrm4+NDbm4uqampxfpWrFixSFv58uW5cOECcH2GIDMzk2effZawsDAGDhzI8ePH7T8IERGRUsphgSA7O7vI5/2AddtisdjUt7DfsWPHSEtL4+WXX+bjjz/Gzc2Nfv36ce3aNTuOQEREpPRy2KJCNze3Yi/8hdseHh429XV3dwdgyZIl5OfnU6ZMGQCmTZtz8mq9AAAgAElEQVRG69at2bx5Mz179rTXEEREREothwWCSpUqkZ6ejsVisb77T0lJwWw24+3tXaxvSkpKkbZLly5RoUIFgCIfOxRuV69e3fqRgoiIiNweh31k4O/vj6urq3VhIMDevXsJCAjAxaVoLgkKCiIhIQHDMAAwDIOEhASCg4PJy8vj0UcfZc2aNdb+mZmZnDhxgtq1aztmMCIiIqWMwwKBh4cHPXv2JCoqisTERDZv3syCBQvo168fcH22ICcnB4DOnTuTlZXFxIkTOXr0KJMnTyYjI4OIiAhcXFwICwtjxowZ/Pjjjxw+fJg333yTChUq0LZtW0cNR0REpFRx6JkKR48eTcOGDenfvz9vvfUWw4YNIyIiAoCwsDDi4uIA8PT0ZM6cOSQkJPD444+zb98+5s6di6enJwDjxo2jdevWvPbaazz11FMAzJ8/v9hMg4iIiNjGoa+gHh4eTJkyhSlTphTbl5ycXGS7UaNGrFq16qbHKVOmDOPGjWPcuHF2qVNEROReo4sbiYiIiAKBiIiIKBCIiIgICgQiIiKCAoGIiIigQCAiIiIoEIiIiAgKBCIiIoICgYiIiKBAICIiIigQiIiICAoEIiIiggKBiIiIoEAgIiIiKBCIiIgICgQiIiKCAoGIiIigQCAiIiIoEIiIiAgKBCIiIoICgYiIiKBAICIiIigQiIiICAoEIiIiggKBiIiIoEAgIiIiKBCIiIgICgQiIiKCAoGIiIigQCAiIiIoEIiIiAgKBCIiIoICgYiIiKBAICIiIigQiIiICA4OBBaLhXHjxhEaGkrLli2ZN2/eLfsmJSXx9NNPExQURGRkJImJiUX2x8XFER4eTlBQEEOGDOHy5cv2Ll9ERKTUcmggiI6OJiEhgYULFxIVFcXs2bNZs2ZNsX5ZWVkMGDCAoKAgVq5cSUhICIMGDSIjIwOAxMRERo0axZAhQ1i+fDkZGRmMGDHCkUMREREpVRwWCLKysoiNjWXMmDEEBgbSoUMHBgwYQExMTLG+cXFxuLq6MmrUKOrUqcOYMWPw8vJi7dq1AMTExNCxY0ciIyPx8/MjOjqa+Ph4Tpw44ajhiIiIlCoOCwRJSUlYLBZCQkKsbSEhIRw4cIC8vLwifffv30+TJk1wcrpenslkokmTJiQkJFj3h4aGWvtXqVKFatWqWfeLiIjI7XFYIEhJScHb2xs3Nzdrm4+PD7m5uaSmphbrW7FixSJt5cuX58KFCwBcvHjxD/eLiIjI7XFx1B1lZ2djNpuLtBVuWywWm/oW9svJyfnD/bdy5swZIiMj/6v671n6fd3ccv1ebm5GSRcgd5G1H5d0BXemP/tp98yZMzb1c1ggcHNzK/aCXbjt4eFhU193d3eb9t/KDz/88F/VLiIiUto57CODSpUqkZ6eXuSFPCUlBbPZjLe3d7G+KSkpRdouXbpEhQoVrPsvXbp0y/0iIiJyexwWCPz9/XF1dS2y8G/v3r0EBATg4lJ0oiIoKIiEhAQMwwDAMAwSEhIIDg627t+7d6+1/7lz5zh79qx1v4iIiNwehwUCDw8PevbsSVRUFImJiWzevJkFCxbQr18/4PpsQU5ODgCdO3cmKyuLiRMncvToUSZPnkxGRgYREREAPPPMM3z77bfExsaSnJzMyJEjadWqFTVr1nTUcEREREoVk1H4NtwBsrOzmTBhAhs2bKBs2bK8+OKLvPjiiwD4+voyefJk66K/xMRE3nrrLY4ePYqvry8TJkwgMDDQeqxVq1Yxa9Ysrl69SosWLZg4cSIPPPCAo4YiIiJSqjg0EIiIiMidSRc3EhEREQUCERERUSCQUiw/P7+kS5C7mD5NlUKFC95LOwUCKVVOnTrF/PnzAXB2dgb0xP5nKigoKOkS7K7w8ZKZmVnCldydStvfW2ZmJnv27LFebbe0je9GzhMmTJhQ0kXI7TEMA5PJVKy9oKDgpu33ktTUVObOnUtSUhJbt27F2dmZhx56qKTLKhUMw7BecGzTpk3k5eVRvnz5Eq7qz1P4d2UymVi/fj3vvvsuLVq0wNPTs6RLu2sUFBRYHyPffPMNaWlpVKtWrYSr+u/9+OOP5Obm8tJLL+Ht7c3ly5fZt28f/v7+pfK5VjMEd5nCJ609e/bw6aefMnXqVOLi4sjLy8PJyalUp1dbVKtWjT59+rB+/XqWLl3K/fffD9wb72zt6caweejQIcaPH8+MGTP47bffSrawP1Hh+E6fPs3q1avp2bNnqQo89nZjGNizZw9r1qxh2rRp/PzzzyVc2X9nxowZDB48mHr16vHOO+8wZcoUhg4diqenZ6kMA6BAcNcxmUxs3LiRgQMHcuzYMU6ePMn8+fMZNGiQNSzcq6HAMAzMZrP1fBT169dn5cqVWCwWnJyctKbgf1D4RD9p0iTeeecdypcvz86dOxkzZgy//vprCVf35zAMg5MnT9KhQwcOHjzIQw89VOwsqnJrhY+RqVOnEhUVxeXLlzl16hSTJ08ucmbZu0FOTg7Jycn07NkTgFq1allnis6dO1dqP07SRwZ3mbS0NMaNG0ffvn158803CQwMZP78+XTs2JGaNWtSpkwZ6x/mvcJiseDs7GxN7T4+Pjz22GNUrFiR7777jgMHDtCqVStcXFzIz8+/534/f5b4+Hg++eQTpkyZQt++fXnxxRdZvXo1O3bsIDg42Dobc7cymUx4e3vj5OTEli1bqFq1Kg0bNsTV1bWkS7trbN++nZkzZzJ16lQGDhxI48aNuXLlCnFxcdSqVYsqVaqUdIk2MQyDrVu3curUKS5cuMDixYuZMmUKDz/8MOPHj8fDw4MGDRoUu+ru3U6B4C6Tnp7OsmXLGDt2LNnZ2fTu3Zv27dvz8ssvM3/+fFJTU/H19S3pMh1i27Zt1KxZ07p48Msvv2TVqlWkpqbSuHFjfH19cXJy4vvvv+fAgQO0bdtWYeB/cPDgQfbt28eAAQO4//77cXd357HHHmPRokXEx8fTpEkTypUrV9Jl3pbCWbWMjAxycnJwc3MjNDSUsmXLMmPGDKpVq0a9evWsjzH5Yz///DO//vorw4cPx2w2U716dSpXrszRo0dZv349DRo0uCsuQufs7Eznzp2ZM2cO69evp3v37vTs2ZP69etTpUoVJk2aRJkyZQgICMDV1bXUrN9SILiDFa4LADh8+DDly5enbNmybN68mdOnTzNp0iTatGnD+PHjcXZ2ZubMmbi4uPDoo4+WcOX2t3r1aiZNmoTZbKZRo0Z89NFHTJ8+nTJlyrBo0SJSUlIIDAwkJCQEk8lEfHw8W7ZsYcWKFXh6elK7du2SHsId7WZPcKdOneKHH36gbdu2lCtXjtzcXMxmM3Xr1uWjjz7izJkzhIaG4unpecuFr3eSwhq3bNlCVFQUy5YtY+XKlTg5OfHss89iNpt59913qVKlikLBTdzsMXL69GliYmLo1KmT9aM7Hx8f0tLSWLx4Mb/++iu1a9emcuXKJVGyzXJyckhPT2f69OlUrVqV3NxcKlasSNWqVQkICLCGAoBdu3bx66+/0qhRoxKu+n+nQHAHWr16NQ8++CBubm4YhsGxY8d44YUXCA8Px8vLi7Nnz7JixQpq1qzJ+++/j5OTE87OzsTHx1OzZk2aNm16Vzwh/y88PDzIz89n/fr1nD9/nt9++43x48czePBgHn74YRYuXMi5c+cICAigadOmuLu7c/ToUdLS0nj99df15P4HblwclpyczNWrV3nggQeoWrUqy5Yt4+DBg7Rp0wYPDw/g+ovAlStXOHz4MPv376d79+53xWPPZDKxa9cuXnnlFZ544gleeeUVDh8+zOeff05oaCjdu3fHxcWFqVOnUq5cOfz9/fW4+f9ufIwcO3aMzMxMzGYz9erVY//+/ezZs4d69epZQ0Fubi7Hjx/nwQcf5KeffqJZs2a4u7uX5BBuqvB508XFBQ8PD1566SX69etHbGwsCQkJVK1a1RoKqlatyvLlyzl06BCDBw8uFQtQdS2DO8yZM2f4y1/+wn333cfChQtxd3cnMTGRv/zlL8TFxfHAAw+QmppKdHQ0Z86cwc/Pj2bNmrFr1y6+/vprvvrqK2rVqlXSw7CrwiejU6dOERsbS3x8PKmpqcTGxlKpUiUAdu7cydixY2nRogVDhw6latWqwL/+4PPy8rRg7CZuDJLTpk1j/fr1nDt3jp49e/Lqq6+SmZnJs88+i6+vL0888QRVqlRh1qxZ1K1bl5deeokePXrwwQcf0KxZsxIeSXE5OTnWFyGLxYKrqytTp07l2rVr/P3vfyc1NZWnnnqKTp068fjjj5OWlkZISAjR0dGsXLmSjRs34uXlVcKjuLPMmDGDFStW4OHhQZ06dazfKpgzZw4Wi4UXXniBBx54gI8++ghvb28iIiJ4++23mTt3LnXr1i3p8q0KXwZNJhMJCQns3buX7OxsGjRoQPv27bl69SqDBw/Gzc2NwYMH06xZM5ydnTl//jzu7u53/fqZQpohuMOULVuWOnXqsGfPHr755hs6d+6Mh4cHGzZs4Nlnn7Um14YNG5KVlUV8fDzx8fFkZWUxc+ZM6tWrV9JDsKsbvwvv7OxMSEgI58+fZ/fu3fj4+NC4cWMAatSoga+vL1988QXJyck0bNiQ++67z/otDL3TK+7GMLB161Y+++wz3nzzTVq0aMFXX33Fb7/9xqOPPsozzzzDpk2b+O6771i3bh3u7u68++671sV4Xbt2xcfHp4RHU9wnn3xCcnIyQUFB/P7775jNZnbs2IGbmxs1a9bkySefJCwsjL/97W988803zJ07l169etGqVSsiIyNLzZP+/+LGjwm2bdvG7NmzGT9+PDVr1uTw4cP84x//YMCAAfj7+3PixAlmzpzJ/v37yc/PZ86cOdSuXZuvv/6asLAwa3i/ExSef2LDhg288cYbFBQUYBgGU6dOJTs7m/bt29O+fXtWr17NwYMHKV++PDVq1MDLy+uOnOn4rxlyxygoKDAMwzDy8/ONHTt2GE8//bTx/PPPGydPnjQ6duxojB8/3ti9e7dx/PjxIrfLyMgwsrKySqJkh8rPz7f+e9euXcagQYOMkydPGteuXTOmTJlidO/e3YiJiSlym61btxrDhw8vclspatq0acahQ4es2999950xaNAgY/r06da2ffv2GR06dDBef/114+LFi0Z+fr5x7do14+jRo0ZmZqZhGIbxwQcfGOHh4caFCxccPgZbLFy40GjWrJnxxhtvGD169DAyMzONefPmGV26dDHCwsKM8ePHW/uuW7fO6Natm3H16lXDMP71t3mvmjBhgpGRkWHd/vzzz42ZM2caH330kbXtxx9/NJ599lnjmWeeMdLT0w3DMIyTJ08a586ds26/9957RocOHYyUlBTHDuAm5syZY7z99tvW7fPnzxtdu3Y1lixZYhiGYVy7ds0ICAgwPv30U+P8+fOGYRjG5cuXjc6dOxtDhgwplc+5miG4gxQmb5PJRNWqValVqxbbtm3jq6++4vTp06Snp7Nx40YWLFjAvn372Lp1Kw888AA1a9Ys9V+NMm6YGSj8NkF8fDwXL14kKCiIVq1akZKSwrp168jPz7cu8KlZsyZdunTBZDKVmpXAf6affvqJgwcP8thjj+Hk5ITFYuHLL79k69atuLq60q1bNwCqVKlCw4YNrQvDatSoQbVq1fj111+ZNm0a8+fPZ/fu3Xz44Yd37EdWfn5+HDlyhDVr1tCkSRMee+wxmjRpwpYtWzh69Cjvvvsunp6eODs7s3z5cq5du8bjjz+Oq6vrPf24uXTpEt988w3du3e3zqwtWrSIJUuW4O3tTdu2bXFxcaFy5co89NBD/Pjjj3z99de0bduWypUrc/bsWYYOHUpcXBzx8fHMnj37jjh76Llz55g6dSr5+fk8/PDD5ObmEhcXx2uvvcalS5fo1asXnTt3ZtCgQUyYMAEPDw/8/f3p1q0bzZs3LxVrBv6dAsEdwvj/07WHDh3i0KFDZGZm0rhxY2rVqkVSUhIXL15k8eLFvPTSS9SsWROTyURiYuI9MZV54wKmmTNnsnDhQrp06UKNGjX47bffOHToEAEBAbRu3ZqUlBQ2bNhAeno6ISEhRY5zLz+p30rlypVp1aoVzs7OrF27loKCAjp37kxubi7fffcdeXl51t9jYSiYPn065cuXJzQ0lHLlypGdnc0jjzzCkCFD7qjPhQsV/m05Ozuzc+dOKleuTGJiIgUFBTRp0oTOnTuzbds2Vq1axbp169i4cSPfffcdM2fOvGu+N29PZcqUoVu3bjg7O7Ny5Upq1apF165dycjI4B//+Af169fnwQcftIaCBx98kE2bNvHbb7/Rrl07PDw88Pb2plGjRgwfPpw6deqU9JCA6ycu8/PzY9KkSeTk5ODv78/ixYvx8fFh/PjxhIWFMWHCBNzd3Vm0aBEFBQW0bNkSd3d37rvvvpIu3z5KeIbinrZjxw7j8uXL1u21a9caoaGhRlhYmNGjRw/j008/NQzDMPbs2WN069bN6Nu3r5GTk2PtX9qnwadNm2akpaVZt69du2Y899xzxurVq61t33//vTFy5EjjtddeM06ePGlcvXrVGD9+vDFy5Mh7fpr3Pyl8/BQUFBi//vqr0bt3b2PQoEFGUlKSkZ2dbcyYMcN47LHHrI/DQocPHzby8vJKouTbVvgYKJz6//33343ff//d+Pjjj42WLVsaCxYssPaNiYkxZs6caXz44YfFPpaT639/oaGhxpNPPmlkZ2cbhmEYf/vb34zg4GBj06ZNhsViMQzj+uPq0KFDd81jZP369UaDBg2MpUuXGl9++aXh6+tr/OUvfynS57nnnjMWLlxYMgU6kGYISsiMGTN4//33ee6553B3d2fXrl289tprjBo1ildffZXU1FSWLVuGs7MzERER+Pn5sX37dmJiYoiMjMTFxaVUv+PdsWMHBw4cIDw83Do7kJaWxgcffEDTpk0JDAwEri8edHV1JTY2lqNHj9K0aVM6depEx44drdd2KM2/p//WjbMuJpOJ+++/n0qVKnHgwAF2796Nr68v7dq1IzU1lQ0bNpCVlWVdsFm+fHnrqaDv9BM9mUwmtm7dyttvv01sbCxOTk48+OCDNG3aFIDPP/8cwzBo0qQJlSpVon379jRr1uyuO8GSPfz7R2xms5mIiAhiY2PZsGED3bt3Jzw8nNOnTzN79mz8/PyoXr06Li4u+Pj43DWPkTp16lCnTh3GjBlD+fLladSoEZs3b8bHx4cTJ06wevVqtm/fzl//+tdSPxurQFACcnNzWbRoEYGBgXTp0oXff/+dH3/8kfvuu4/hw4fj4uLChx9+SIUKFfj555/Jzs6mS5cuPPjgg/zyyy888sgjpXfK6v+rUqUKHTp0wMnJibVr11KuXDkqVKjAqVOnSEpKolGjRnh7ewPX1wmsW7eOs2fPcvr0afz9/bn//vuLvOjJvxg3rMfYsGED27Ztw2w207RpUypXrszu3bvZs2cPfn5+tGvXjqtXr7JkyRIqVKhA/fr1rce5k3+3hUHwl19+YeDAgbRv356cnBzi4+PJyMggICDA+lHInDlz+Prrr61npCvtYdsWN/7tbNq0if3793Pp0iUCAwNp164dixcvtoaCjh07cubMGaKjowkLC7N+xRfu7MfIjerWrUv9+vV5//33adCgAU888QQfffQRiYmJXLhwgffff7/Uf4MLFAhKREFBAXv27OHs2bMkJSUxadIkrly5gsViISIigpkzZ1KxYkVef/11jh49yldffcWlS5fo3bs3Xbp0KfXvXvLz863nCFizZg0LFixgz549tGrVivvuu4/Nmzdz+fJlatasyX333UdGRgbbtm2jcePGHD16lMqVK1O3bt17/kn9Zm6cMYmOjuaTTz7hp59+YuPGjeTm5tK5c2eqV6/O7t272bt3L/7+/rRu3ZoHHniAHj163DVP8CaTiZ9//pl169YREhLCG2+8Qbdu3cjMzGTz5s2kpaUREBBAs2bNqFWrFjk5ObzxxhtUqVLlnn/c3BgYp02bxrRp0/jll19YtmwZrq6utGnThvbt27NkyRI2bdpEt27dCA8Px2w206VLl7vmMfLv6tSpQ7169Zg6dSphYWGMHDmS3r1706NHj7v6Es63Q4GgBDg7OxMWFkZMTAybN2/mqaee4q233sLT05Pc3Fy++eYbOnToQNOmTTl+/DgnTpzg7NmztGvXrtRPWcG/3lXEx8czceJEOnXqRFJSEnv27KFfv354eXmxefNmVqxYQUJCAgsXLiQzM5MPPviA7777jl9//ZVOnTqV8CjuTIUvdgcOHGDDhg1ERUXxyiuvcO3aNb777jsyMjKsoWDv3r1s3LiRFi1a0KJFi7tmChjg2rVrfPbZZyxevJiHHnqItm3bAhASEkJaWhqbNm0iIyOD2rVrExwcTPv27e+Kc+w7QuFjJDk5mUWLFjFjxgyefPJJ6taty+TJkzGbzbRt25YOHTqwdOlSli5dytNPP02zZs1wcnIqcsr1u01hKBg1ahRly5YlLCwMNze3ki7LYRQISkhOTg7vvfcetWvXxmKx4OHhQYcOHdi+fTtr1qzhnXfeAa6/Q65fv7719Kn3irlz57J48WIiIiJ49dVX8fLy4ocffmDnzp0MHDiQZs2aUaZMGVJTU2nQoAHTpk3DycmJf/7zn9SoUYPmzZuX9BDuWNu3b2fcuHEAPP/885QtW5ZHHnmEM2fOsGXLFjIzM+ncuTMVKlQgJyfH+rVNuHumgN3c3KhcuTKXL19mw4YNtGrVyvqCHxISwrVr11ixYgVOTk4EBwfj5OR0z88M3Gjp0qV8/vnnmM1m+vfvj4+PD/Xq1aNSpUq8++67mM1m2rRpQ5s2bUhOTi4yM3C3PEZupU6dOtZTnheeevleoUBQQsxmM3379qV79+5s3LiRX375hfvuu4/q1auzfft2Dh06RHx8PGvXruVvf/sbFStWLOmSHerixYssX76czMxMevXqRb169TCbzezevZudO3fSqVMnHnnkEVq2bEmlSpVISkpixYoVrF69mtGjR99zf8h/5N8Xh6WkpHDy5El+/PFHevXqZV2P8sgjj3D27Fm2bdvG+fPnefzxx3n00UfvinM4FH4Ucvz4cY4cOcK5c+fw9/enefPm/Pbbb3z++ec0bdrU+nfUpEkTDMOgY8eOlCtX7o4emyPcbPHtqlWrOHPmDI0bN6ZatWo4OztTv359KleuzHvvvcfvv/9Op06drGHgbpk9skWtWrXuyecQBYISZDab8fDwIDg4mH/+858cPHgQT09P6tevT2JiIpcuXWL69OlFFnLdK+rVq4efnx+LFi0iIyODRx99lHr16uHm5sa+fftYs2YN4eHh5OTk8M033/DRRx+RnZ3N9OnT75nLP9vixsVhZ8+excnJiZo1axIQEMAvv/zC0qVLad26tXWB5iOPPEJSUhJpaWm0b9++yMmy7mQmk4n169czduxY/vnPf7J7927mzp1LeHg4oaGhnDlzhs8++4ymTZtaZwqCg4Ot476X3fgYOXfuHBaLhZo1a9KiRQu2bNnCyZMnefDBB6lYsaI1FHh6erJ9+3YiIyOB67//0hIG7mkl9oVHKeLkyZPGgAEDjP/7v/8zvvnmG8MwDOspYe9lGzZsMAICAoz33nvPMIzr3yv/8ssvjYkTJ1q/Y56VlWWkpaUVObWqFD1PxaxZs4zw8HDjySeftJ6S+NixY8bAgQONrl27GidPnixy28Lf7d1yLodDhw4ZzZs3N1asWGEYhmEcPHjQ8PX1NVavXm3k5+cbJ0+eNF5//XWjadOmRU7TfK+78THy/vvvG4899pjRsmVLY/ny5YZhGMaRI0eMzp07G6+88opx8OBBa1+LxXLXPUbkP9MMwR3C29ubxo0bs2rVKs6fP0+bNm0oU6ZMSZdV4urUqUPdunWZMmUKOTk5tGjRwrryvXAq22w24+bmhtlsLuly7yiF7+pnzJjBkiVLGDRoED4+PmzYsIGLFy8SERFBYGAgiYmJLF++nIcffti6TqXwIlB3+sxAoSNHjnDs2DHGjh3LyZMn+ctf/kKPHj2IiIjgk08+oX379vj6+pKRkUHz5s3vicW5tij8/50+fTqLFy/mhRdeoG7dukyfPh1vb2/atGlD8+bNWbx4MadOnaJy5cpUrlwZZ2fnu+4xIjYo6UQiRZ0+fdo4e/ZsSZdxx9mwYYPh6+trvfCI2ObkyZNGjx49jB07dhiGcf3CRQ0bNjS6du1qvbDLsWPHjCeffNJ44403SrLU21L4rjQtLc3IysoyYmNjjW7duhkXLlww2rZta/ztb38zDOP6+Nu2bWts27bNMIzrZyqUog4fPmx0797dOHDggGEY12dbfH19jYCAAGP+/PmGYVyfKWjWrJkxY8aMkixV7EwXhL/D3Cvfd71d4eHhLFq0iCZNmpR0KXeVtLQ0Ll++jK+vL7/++ivLli1j+PDhlClThjlz5uDp6cnQoUOZNWvWXbNw1fj/70o3bdrE559/zuXLl3nppZcoW7YsrVq1olevXkycOBEAl//X3r0HVVW1Dxz/nnO4eIRROAc8ROKbXASUEjEgDI0RQ3TAVPKeE6aVpshFT05jjjkpKTReIYhIcbyQN7zfQFKsVFSSJFMnSAFTMZUwLsrF8/vD4ST5vm8/e4uLPJ//2HuzZ585i8Wzn7XWs0xMMDU1NWbbJIv0KKVSSVVVFSYmJhQVFRmrqFZUVLBgwQLMzc0ZPnw4u3btapXbWou/j8wCEW2Gj48PJiYm1NfXt/SjtEr3799/5JiHhweBgYHcuHGDrKwstFotYWFhBAQEUFVVRVJSEh9++CF2dnYolcp/e4/WRqFQcPLkSfR6PUOGDGH06NHY2dkRHh6Ot7c3d+/epbKyktLSUrZt20ZDQwMODg4t/ditwr/7fs3MzAgJCaFjx2o724AAAA2pSURBVI7k5ORgb2+Pu7s7gYGBmJmZsWLFCtavX49Op0OlUtHQ0NACTy6ag2QIRJvTWMVQ/O7hmeLHjh2jtraWyspKQkJC0Ov1WFpa8v777zNmzBg0Gg2lpaV4enry1ltvGev6Q9tZQ37q1Cn69+/P+PHjmxwvLCwkISGB3NxcrKysqKqqIiEhAZ1O10JP2no83Eby8vL45ZdfsLW1xdnZmejoaO7evcuePXsIDg6mW7du/Pzzz/j7+xMQEMDw4cON92ncAlk8eaRnFaKNMzxUajY+Pp79+/cb96HfsGEDUVFRPP/88yiVSgoKCvD39zcWvvL29jauIW9LHf2VK1eoqqoy/lxbW4uZmRkVFRWMHj2awMBAOnXqhL29fbsPBhqHWBrbSFxcHIcOHcLc3JwuXbpw5coVli5dasyimJmZUVBQQGJiInV1dYwcORKFQtHm2oh4fG3jdUAI8Yh79+4Bv88Uz8zMZPfu3SQmJrJx40b0ej1nzpxBrVZTU1PDqFGj+Prrr5kwYQK3bt0iOTnZOEzQ1jr6QYMGkZ+fT3Z2NvD73AALCwuKi4vx9/enT58+7T4YaCwo1ZjmP3jwIDt37iQuLo7du3fj5+dHcXExd+7coVOnTowZM4YVK1Ywe/Zsbt68SXJysnE1QVtrI+LxSYZAiDYoIiICOzs7oqKisLCwAB5UIHRxccHd3Z19+/Yxf/585s+fz/3791m9ejUzZ85kwIABXL16ld69exvrzrfFIRg/Pz+GDRtGYmIiBoOBQYMGAVBfX4+1tbW8zfJguemlS5dYunSp8Tu+efMm/fr1w9PTk0OHDpGYmEhsbCxarZaPP/6Y2bNn4+npicFgoEePHm26jYjHJxkCIdqggQMHsn79ej7//HMqKyuBB3MrLCwsyMnJYe7cucyaNYtx48ZhZmbGZ599xg8//IBOp6NPnz7GYYK22tF37NiR6dOn4+3tjV6vZ+LEiUyaNIkvvviCqVOntvvVBHV1dVhbW3Pz5k0WLFhgnIirUCgoLS1l586dzJkzB71ez8iRIykuLmb79u0UFxfj6uqKm5tbm28j4vFJYSIh2hiDwYC7uztOTk4sXLgQlUqFj48PNjY2LF68mO3btxMbG8urr74KQGlpKfn5+YwdOxZLS0vjfdrKBML/pGPHjvj4+ODl5UVtbS1ubm7o9fp2sW/9n1GpVLi5uaFQKDh27Bj5+fkMGDAAnU5HVlYW6enpTJ8+nfDwcOBBZuWrr75iyJAhTYo2tfU2Ih6PBARCtFEuLi44OjqyaNEi6urqCAoKolevXhw+fBiNRoNWq+XXX38lMTERlUrFhAkTnriqciYmJjg4ONCvXz/69OnTrnYE/W8MBgOmpqY4OTmhUCg4fvw4586dIzQ0FJVKRUlJCVZWVmi1Wu7cuUN8fDwmJiaEh4c/cW1E/P8pDAaDoaUfQgjxeBr/bBUKBfv27SMmJoZp06YxefJkCgoKeO+991AqlZiZmaHVaklLS8PU1LTJ0jPxZDH8oYxw43d99+5ddu7cSUZGBh4eHsybN4+tW7eyb98+jh07Rs+ePTEzM2PdunXSRto5CQiEaOXOnj2LSqWiV69exs764a1mHw4Kpk6dSlRUFL/99hvXr1+nvr4eV1dXmRz2hLt27RpVVVU4Ozs3Od4YJNy7d48dO3awbds2nn32WebOnYtSqeTMmTN06tSJ7t27SxsRMmQgRGt35swZli9fTt++fTl16hTOzs4olcomb4M9evTA0dGR2NhY6uvr8fHxQafTYWtra9wEqr3Pun9SlZWVsX//fpKSkggODiYnJwdTU1M6d+5sXDJoYmKCi4sLSqWSo0ePcvbsWfz9/enatSsajUbaiABk2aEQrV6XLl1wcnJi0qRJlJWV8e2336JWq1EoFMYO32AwMHToUBQKBdHR0Tz99NOMGjXKeA9JAT+ZRo4cib+/P9OmTWPdunUMHjwYU1NTtmzZYrymsY007kmgVCpJTU1lzZo1vP3228brpI0ICQiEaOW8vLw4cuQIZWVluLq6cvnyZXr27Gk8/3BQ0DhL3NvbuwWfWDSHvLw8ysrKeOmll1Cr1YSGhpKWlka3bt0eufbhoCA0NBSNRkNAQEDzP7Ro1SQkFKKVMRgMj2xC4+/vz/Lly3F3d2fBggWcPn3aeC3QZPjAz89PNoFqB0xNTTEzM6OoqIhVq1Zx4cIFNmzYQG1tLTNnzuTSpUtNrm+sWNihQwcCAwNloyLxCJlUKEQrUlNTg1qtNv584sQJ7t27h6enJ507d+bs2bOsXbuWK1euoNfrm2xMJNqftLQ0kpKSqK6uJi0tjb59+1JZWcmIESPQaDQsWbKEZ555pqUfU7QRkiEQopVYuXIl06ZNM1YeXLRoEZGRkURFRTFu3DiOHj3Kc889x6RJk3BwcCAuLo7s7GzS0tKM+xqI9qHxzd5gMFBRUYGVlRWFhYWUlZVhaWnJjh07KC8vZ86cOZw+fZrU1FQuX77csg8tWj3JEAjRChgMBrZs2cLevXvRarW88sorfPrpp+j1ep566ik++OADysrKiIyMJCAggO+//541a9aQk5ODpaUl2dnZMkO8HfhjjYCKigoA0tPT2bRpE5MmTWLIkCHY2tpSXV3NqFGjqKiowM7Ojk2bNkkbEf+VBARCtBINDQ3s3buXnTt3Ul5eTteuXVm5cqXx/DvvvMPVq1eJiooiICCAmpoaiouLcXFxQaVSSUGZJ9zD3+/x48epqqqitraWoUOHAvDJJ5+wdetWwsPDjUFBbW0tBQUFeHp6ShsRf0oCAiFa2B8rzB04cICkpCTKy8vJzs7G1NTUeG769Olcv36dKVOmMGjQIOM52d2v/YiLiyMrK4vOnTtTU1ODwWBg+fLl9OjRg4SEBDIyMnjjjTcICgqiS5cuxt+TNiL+jISKQrSgxv3qAQ4dOsSGDRsIDg4mIiICjUbDrFmzjGlhgMTERMzNzTl69GiTQEE6+vbhwIED7Nq1i4SEBLZu3UpERAQ//fQT1dXVGAwGZsyYwfDhw4mLiyMvL6/J70obEX9GKhUK0UIeTt9++eWXZGRkcPjwYWxsbBg8eDDW1tbk5uaSm5vLiy++iLm5OQBhYWEMHDhQNqF5wkVGRnL69Gleeukl47G8vDxqamp4/fXX2bdvH/PmzWPu3LnodDqSk5MJCAjA19cXW1tbQkJCZHhAPBYJCIRoAQaDwdhZL1myhPT0dKysrLhx4waFhYUoFApCQ0OxsLAgNzeXU6dO4evrS4cOHQCMpWYlKHhy1dXVsWrVKu7du4efnx8AJ0+epKSkBHt7e6Kjo4mJiWHcuHGUlZWxdOlSXnjhBXQ6HT179nxkzwsh/oxUKhSiGeXm5uLr62v8R37+/Hn2799PfHw83t7e3L59m7Vr15KZmYlKpWL48OHAgyWJa9euJTIy0ngv6eifbMOGDUOtVhMdHY3BYCAmJobQ0FBSU1N57bXXiI+PJzQ0FHgQPGi1WmxsbJrcQ4YJxOOQDIEQzSQ1NZWcnByCgoKAB2/5hYWFHDx4kBkzZqBWq1Gr1Tg6OnLx4kX279+PtbU1wcHBuLi4MGzYMOng2xknJyecnZ1ZvHgx1dXVvPzyyzg4OJCfn49SqcTFxYVr166RlJSEubk5EyZMkKyR+MskIBCimVhbWxMWFoZKpaKoqAiNRoNarSYjIwOtVkuvXr0AsLS0xNbWlpSUFK5du4apqWmTUrOSGWhfHg4KGhoaCAsLw9nZmQ0bNrB582ays7NRKpWsXr0aExMTGUoSf5kEBEI0g/v376PRaFAqlRw/fpx3330XrVZL7969KSoq4rvvvsPCwgJHR0fgQQnj8+fP061bNy5evIiXlxeWlpYSDLRTTk5OuLi4sGjRIgwGA2PGjGHs2LH4+voSEhLCm2++ady/QrJI4q+SgECIf9jDEwjT09MpKipCq9WSmZmJg4MDQ4cO5ciRI5w6dYoff/yR2tpali1bhkajISIigvj4eDw8PHB2dm7hTyJaUmNQ8NFHH1FTU0P//v3R6XTY2NgYJ5lKMCD+FxIQCPEPa0zffvPNNyxZsgS9Xo+rqytlZWVs374dDw8Pxo4dS3V1NXv27OHcuXOo1WpWrFiBVqvlxIkT+Pj48K9//auFP4loaY1BwcKFC7G1tcXDw8N4ToYJxP9KKhUK0QxSUlLIysrCx8cHvV4PwIULF9i4cSMFBQVER0czYMAAAG7fvk1dXR06nY5ly5axbds2Nm/ejL29fUt+BNGKnDx5Ei8vL0xMZKGY+PtIaxKiGTg4OHDx4kVUKhWVlZVYWlri5ubG+PHjSU9PZ9WqVdy6dYsRI0ZQXl7O4sWLKSkpoa6ujpSUFAkGRBM+Pj4A1NfXS1Ag/jaSIRCimRw+fJiIiAgmTpzInDlzjMcvXLhASkoKHTp0IDY2lurqai5dukRlZSXdu3dvUo9eCCH+KRIQCNGMsrKyiI6OZvLkyURHRxuPl5SU0LVrV1lFIIRoMRIQCNHMMjMziYmJYcqUKURFRTU5J9vTCiFaivQ8QjSzoKAgli1bRnJyMps2bWpyToIBIURLkQyBEC1EZooLIVoTCQiEaGEyU1wI0RpIQCCEEEIImUMghBBCCAkIhBBCCIEEBEIIIYRAAgIhhBBCIAGBEEIIIZCAQAghhBDA/wGsC9RaLDz84QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"white\")\n",
    "sns.set_palette(\"coolwarm_r\", n_colors=6)\n",
    "\n",
    "tweets = all_tweets[all_tweets.c_mbfc!=\"-1\"]\n",
    "media_type_counts = tweets[\"c_mbfc\"].value_counts(normalize=True)\n",
    "display(media_type_counts)\n",
    "plt.figure(figsize=(8, 6))\n",
    "x = range(len(media_type_counts))\n",
    "\n",
    "for i in x:\n",
    "    plt.bar(i, media_type_counts[mbfc_labels[i]])\n",
    "    \n",
    "plt.xticks(x, [mbfc_labels[i] for i in x])\n",
    "# plt.xlabel(\"IRAs\", fontsize=18)\n",
    "plt.ylabel('Prop of tweets', fontsize=15)\n",
    "plt.xticks(rotation=45)\n",
    "# plt.yticks(fontsize=14)\n",
    "\n",
    "# plt.savefig(\"fig/size-of-intersection500.pdf\", dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "left leaning     12874141\n",
       "left              7925543\n",
       "fake              7440897\n",
       "right             5992412\n",
       "center            2769693\n",
       "right leaning     2512789\n",
       "Name: c_mbfc, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(array([       0.,  2000000.,  4000000.,  6000000.,  8000000., 10000000.,\n",
       "        12000000., 14000000.]), <a list of 8 Text yticklabel objects>)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"white\")\n",
    "sns.set_palette(\"coolwarm_r\", n_colors=6)\n",
    "\n",
    "tweets = all_tweets[all_tweets.c_mbfc!=\"-1\"]\n",
    "media_type_counts = tweets[\"c_mbfc\"].value_counts()\n",
    "display(media_type_counts)\n",
    "plt.figure(figsize=(8, 6))\n",
    "x = range(len(media_type_counts))\n",
    "\n",
    "for i in x:\n",
    "    plt.bar(i, media_type_counts[mbfc_labels[i]])\n",
    "    \n",
    "plt.xticks(x, [mbfc_labels[i] for i in x])\n",
    "plt.ylabel('Prop of tweets', fontsize=15)\n",
    "plt.xticks(fontsize=15, rotation=45)\n",
    "plt.yticks(fontsize=14)\n",
    "\n",
    "# plt.savefig(\"fig/size-of-intersection500.pdf\", dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "left leaning     21363\n",
       "fake              9893\n",
       "right             4388\n",
       "left              3830\n",
       "right leaning     3085\n",
       "center            2493\n",
       "Name: c_mbfc, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(array([    0.,  2500.,  5000.,  7500., 10000., 12500., 15000., 17500.,\n",
       "        20000., 22500.]), <a list of 10 Text yticklabel objects>)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"white\")\n",
    "sns.set_palette(\"coolwarm_r\", n_colors=6)\n",
    "\n",
    "ira_tweets = all_tweets[all_tweets[\"is_IRA\"]==1]\n",
    "tweets = ira_tweets[ira_tweets.c_mbfc!=\"-1\"]\n",
    "media_type_counts = tweets[\"c_mbfc\"].value_counts()\n",
    "display(media_type_counts)\n",
    "plt.figure(figsize=(8, 6))\n",
    "x = range(len(media_type_counts))\n",
    "\n",
    "for i in x:\n",
    "    plt.bar(i, media_type_counts[mbfc_labels[i]])\n",
    "    \n",
    "plt.xticks(x, [mbfc_labels[i] for i in x])\n",
    "plt.ylabel('Prop of tweets', fontsize=15)\n",
    "plt.xticks(fontsize=15, rotation=45)\n",
    "plt.yticks(fontsize=14)\n",
    "\n",
    "# plt.savefig(\"fig/size-of-intersection500.pdf\", dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "left leaning     12862\n",
       "fake              3485\n",
       "right leaning     1955\n",
       "center            1123\n",
       "left               872\n",
       "right              473\n",
       "Name: c_mbfc, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(array([    0.,  2000.,  4000.,  6000.,  8000., 10000., 12000., 14000.]),\n",
       " <a list of 8 Text yticklabel objects>)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6+sLAwADTpk1DeHg4Dh48iL59+2Lbtm3o0qULvLy8AAALFixAu3btEB8fDzs7u2LpnYiIqCLRagVi4cKFyM/PR3h4OCpVqgQA8PPzQ7Vq1TB//vxia8bCwgK//PILsrOzkZycjNOnT6Np06aIjo5GzZo1Nd7sXV1dceXKFQBAdHQ0XFxcpLtiKhQKuLi4ICoqSqq7ublJ+9rY2MDW1laqExERkTxaBYjw8HBMnjwZ1tbW0lidOnUwffp0REREFFszM2fOxMWLF+Hi4gJPT0/UqFED48aNQ0pKCqysrDS2tbS0RFJSEgC8tZ6cnAzg5Qmgf1UnIiIiebT+Ou9X7wFRxNTUtFhvJJWQkIAPPvgA27ZtQ3BwMB4+fIj58+cjOzsbJiYmGtuamJggLy8PQoi31tVqNQAgJyfnL+tEREQkj1bnQLi6umLnzp2YNGmSNFZQUIDg4GCoVKpiaSQhIQE//PADTpw4IZ1TYWpqiqFDh+Lf//73a2/2arUalSpVgkKhgKmp6VvrRc/zV3UiIiKSR+vvwvD29saFCxeQl5eHOXPm4M6dO8jIyJCukvinrl69iqpVq2qckOno6IiCggKo1WqkpqZqbJ+amip9Q6i1tTVSUlL+sv5X+xMREZE8Wh3CaNy4MX755Rd4eHigdevWMDY2Ro8ePXDgwAE0bdq0WBqxsrJCRkYGEhMTpbE7d+4AADw9PZGcnIwHDx5ItcjISCiVSgCAUqlEVFSUdNMrIQSioqKk1RGlUonIyEhp38TERDx69KjYVk+IiIgqGq1WIICXn+InTJiA/Px8GBoaQqFQFGsjKpUKTZo0wdSpU+Hr64ucnBzMmDEDn3zyCZycnNCmTRtMmTIF06dPx7Vr17Bv3z5s2bIFANC1a1csXrwY/v7+GDRoEEJDQ5GZmYnu3bsDAAYOHAhvb2+4uLhAqVTi+++/h6enJ+rWrVuscyAiIqootFqBAIC9e/eia9euUKlUePDgAWbOnImVK1cWWyNGRkYICgqCubk5vvjiC3z99ddwd3fHd999B+DlvRuqVauGfv36YeXKlZgzZw6cnZ0BAGZmZggKCkJUVBR69+6Ny5cvIzg4WLpzprOzM/z9/bF69WoMGDCg2C8/JSIiqmi0WoHYu3cvfvjhBwwdOhSrVq0CADg4OGD+/PkwMTHBiBEjiqUZa2trLFu27I01S0tLrFmz5q37Ojk5Yc+ePW+t9+7dW+PW10RERPT3abUCsWHDBkyfPh2jRo2CoaEhgJeHBfz9/REaGlqiDRIREZH+0SpAxMfHv/GEQ5VKxZsxERERVUBaBQgbGxvExcW9Nh4REQEbG5tib4qIiIj0m1bnQAwdOhSzZs1CSkoKhBC4ePEiwsLCsGnTJnzzzTcl3SMRERHpGa0CRL9+/ZCfn4+goCDk5OTAz88P1tbWmDJlCgYMGFDSPRIREZGe0SpAPHv2DIMGDcKgQYOQlpYGExMT6RJJIiIiqni0OgfCw8MD3t7e2LRpEzIzMxkeiIiIKjitAsSPP/6Ili1b4tChQ+jatSu6d++ORYsW4fLlyyXdHxEREekhrQ5hODk5wcnJCWPHjkV6ejrOnDmDo0ePwtvbGxYWFjh79mxJ90lERER6ROvvwigoKEBsbCwuXryICxcu4PLlyzA1NYWjo2NJ9kdERER6SKsAMWzYMOlwRbNmzdCiRQuMHTsWSqVSujMlERERVRxaBYg///wTOTk58PDwwIcffoiWLVvCwcGhpHsjIiIiPaVVgPjtt98QHx+Pc+fOISIiAqtXr4ZCoYC7uztatmyJQYMGlXSfREREpEe0PgfCzs4OdnZ2GDhwIBITE7FhwwaEhITg6NGjDBBEREQVjFYB4sWLF7h48SLOnj2LiIgI/Pnnn2jUqBG+/PJLtG/fvqR7JCIiIj2jVYBo0aIFjIyM4ObmhkGDBqFDhw78Ei0iIqIKTKsAsWzZMrRo0eK1O1Cq1WqEh4ejc+fOJdIcERER6Set7kT59ddfQ61Wvzb+8OFDTJw4sdibIiIiIv321hWIHTt2YP369QAAIQT69OkDAwPNvJGRkYF69eqVbIdERESkd94aILy8vJCRkYHCwkIsX74cPXr0QJUqVaS6QqFA1apV0aVLl1JplIiIiPTHWwNEpUqVMGrUKACAjY0NPv74Y5iYmJRaY0RERKS/tDqJsnfv3iXdBxEREZUhWp1ESURERPQqBggiIiKS7a0BYsGCBXj27BkA4NGjRxBClFpTREREpN/eGiC2bduG58+fAwA6duyIp0+fllpTREREpN/eehJl7dq18fXXX6NJkyYQQmDOnDkwNTV947Zz584tsQaJiIhI/7w1QCxatAhBQUFITk6GQqHA48ePYWxsXJq9ERERkZ56a4D44IMPsGzZMgBAhw4dEBgYiHfffbfUGiMiIiL9pdV9IE6cOAEhBMLDw3Hr1i0YGRmhUaNGaNmyJQwNDUu6RyIiItIzWgWI9PR0DB06FNevX8e7776LwsJCPHv2DB988AE2bNgACwuLku6TiIiI9IhW94GYO3cuCgoK8OuvvyIiIgIXLlzA/v37IYTAokWLSrpHIiIi0jNaBYiTJ09ixowZaNCggTTWsGFD+Pn54fjx4yXWHBEREeknrQKEEALm5uavjVtYWCA7O7vYmyIiIiL9plWAUKlUWLt2LQoKCqSxgoICBAcHw8nJqcSaIyIiIv2k1UmUkyZNwqBBg9C5c2cpMMTExCAzMxMbNmwo0QaJiIhI/2i1AtG4cWP8/PPP6Nq1K7KyslBQUIBPPvkEBw8ehKOjY7E1k5eXh7lz56JFixZo0aIFZs6cCbVaDQB4+PAhhg4dCpVKhW7duiE8PFxj3/Pnz6Nnz55QKpXw9vZGfHy8Rn3r1q3w9PSEs7Mzpk6diqysrGLrm4iIqKLR+ts4bW1tMXnyZAQHByMwMBDjx4+HpaVlsTazYMECHD16FKtWrcLq1atx+vRprFy5EkIIjBkzBhYWFti1axd69+4NHx8f3L9/HwCQmJiI0aNHo1evXti9ezdq1KiBMWPGoLCwEABw5MgRLF26FDNnzsSWLVsQGxuLefPmFWvvREREFYnefJ13RkYGfvzxR/j7+8PV1RUuLi74+uuvce3aNZw/fx53797Fd999h4YNG+Krr76Cs7Mzdu3aBQAIDQ2Fg4MDRowYgYYNG+KHH35AYmIizp8/DwDYvHkzBg8ejI4dO6JZs2aYNWsW9uzZgxcvXuhyykRERGWW3gSIyMhIVKpUCR4eHtKYl5cX1q1bh+joaHzwwQcwMzOTaq6urrhy5QoAIDo6Gm5ublKtcuXKaNq0KaKiolBQUIDY2FiNukqlQkFBAW7cuFEKMyMiIip/9CZAJCQkwNbWFvv378fHH3+M9u3bY/78+VCr1UhJSYGVlZXG9paWlkhKSgKAt9aTk5ORkZGB3NxcjbqRkREsLCyk/YmIiEgera7CKA0vXrzAgwcPsG3bNsyePRsvXrzA7NmzkZ+fj+zs7Ne+CdTExAR5eXkAgOzsbJiYmLxWV6vVyMnJkR6/qU5ERETyab0CceDAAQwYMAAuLi5o3rw5vL29X7sS4p8wMjJCZmYmFi5ciObNm6Nt27aYPHkyQkJCYGxsLIWFImq1GpUqVQIAmJqavhYGiuqmpqbS47ftT0RERPJoFSB++uknfPvtt7Czs8OkSZPw73//G1ZWVhg7diwOHjxYLI1YWVnByMgI77//vjRWr1495ObmombNmkhJSdHYPjU1FTVr1gQAWFtbv7VuYWEBU1NTpKamSrX8/Hykp6e/dtiDiIiItKPVIYx169Zh2rRp+Ne//iWNDR48GEqlEitWrEC3bt3+cSMqlQr5+fm4efMm7O3tAQB37txB1apVoVKpsH79emRlZaFKlSoAXp50qVKpAABKpRKXLl2Snis7OxvXr1/H6NGjYWBggGbNmiEyMlI6QfPKlSswNDREkyZN/nHfREREFZFWKxApKSkaV0cU8fT0xIMHD4qlkbp166Jjx46YOnUqrl69ikuXLmHRokXo168fWrVqhVq1asHX1xe3bt1CcHAwoqOj0bdvXwBAnz59EB0djdWrV+P27dvw8/NDrVq10KpVKwDAoEGDsGHDBhw5cgSxsbGYPXs2+vTpg6pVqxZL70RERBWNVgGibdu2CAkJeW38yJEj+PDDD4utmQULFsDe3h5ffPEFxo4di86dO2PixIkwNDTEqlWrkJaWBi8vL/z8889YsWIFateuDQCoXbs2AgMD8fPPP6NPnz5ITU3FqlWrYGDwcnoff/wxRo8ejVmzZmHIkCFwdHSEr69vsfVNRERU0SiEEOJ/bbRw4UJs3boVjRo1gru7O4yMjHDt2jVERESgc+fOGt/U6e/vX6INlyYvLy+EhYXpug0iIq2sPabrDkrOiE667qBikPO+p9U5EDExMVAqlQCAq1evSuPNmzfH06dP8fTpUwCAQqGQ2ysRERGVQVoFiK1bt5Z0H0RERFSGaH0jqUePHmH79u24desWjIyM0KhRI/Tr1w+2trYl2R8RERHpIa1Oorxx4wZ69uyJAwcOoHLlyjA0NMTPP/+MXr16IS4urqR7JCIiIj2j1QrE/Pnz4enpiQULFki3lM7Ly4Ovry8WLlyI9evXl2iTREREpF+0WoG4cuUKxowZo/F9FMbGxhg5ciSioqJKrDkiIiLST1oFiHfeeQcvXrx4bTwzMxNGRnrzfVxERERUSrQKEO3atcN3332HhIQEaezevXv4/vvv0bZt2xJrjoiIiPSTVssH33zzDYYMGYKPPvoI7777LgDg6dOnUCqVmDp1aok2SERERPpHqwBRUFCA3bt34/Tp07h16xZMTU3RsGFD6bsmiIiIqGLRKkD06dMHgYGBaNu2LQ9ZEBERkXbnQAghYGJiUtK9EBERURmh9QrE8OHD4eXlhdq1a6NSpUoa9Z49e5ZIc0RERKSftAoQq1atAgAEBQW9VlMoFAwQREREFYxWAYK3qyYiIqJX/WWAyMrKwvnz52FqagqVSoWqVauWVl9ERESkx94aIOLi4jB8+HCkpqYCAKysrLBixQo4OTmVWnNERESkn956FcbixYtRp04d/PTTTwgNDUW9evUwe/bs0uyNiIiI9NRbVyCuXLmCLVu2oEmTJgCAOXPm4KOPPkJWVhaqVKlSag0SERGR/nnrCsSLFy9Qo0YN6XGdOnVgaGiI9PT0UmmMiIiI9NdbA0RhYSEMDDTLRkZGKCgoKPGmiIiISL9pdSdKIiIiolf95WWcW7ZsQeXKlaXHBQUF2LFjB8zNzTW2GzVqVMl0R0RERHrprQGiVq1a2Ldvn8ZYjRo1cPjwYY0xhULBAEFERFTBvDVAnDhxojT7ICIiojKE50AQERGRbAwQREREJBsDBBEREcnGAEFERESyMUAQERGRbAwQREREJBsDBBEREcnGAEFERESyMUAQERGRbAwQREREJBsDBBEREcnGAEFERESy6WWA8PPzg7e3t/Q4Li4O/fv3h1KphJeXF2JiYjS2P3DgADp37gylUonRo0fjyZMnUk0IgSVLlqBVq1Zwc3PD/PnzUVBQUGpzISIiKo/0LkBERERg165d0uOsrCwMHz4cSqUSYWFhcHV1xciRI5GZmQkAiImJga+vL0aPHo2QkBBkZmZi8uTJ0v6bNm1CWFgYli1bhhUrVmD//v1Yv359qc+LiIioPNGrAJGVlYXp06fDxcVFGjtw4ACMjY3h6+uLBg0aYNq0aahWrRoOHjwIANi2bRu6dOkCLy8vODg4YMGCBThz5gzi4+MBAJs3b4aPjw/c3d3RokULTJo0Cdu3b9fJ/IiIiMoLvQoQS5Ysgbu7O9zd3aWx6OhouLi4wMDgZasKhQIuLi6IioqS6m5ubtL2NjY2sLW1RVRUFJKTk5GYmIjmzZtLdVdXVyQlJSExMbGUZkVERFT+6E2AiIqKwqFDhzBlyhSN8ZSUFFhZWWmMWVpaIjk5GQDw+PHjt9ZTUlIAQKNeo0YNAEBSUlKxz4GIiKii0IsAoVar4efnh2nTpsHc3Fyjlp2dDRMTE40xExMTqNVqAEBOTs5b6zk5OdLjV2tFfycRERH9PXoRIFauXAk7Ozt069bttZqpqekvnsMkAAAgAElEQVRrb/ZqtRqVKlX6n/U3hYWiP1euXLlY50BERFSRGOm6AQDYt28fUlJS4OzsDADIy8tDQUEBnJ2d0aNHD+lQRJHU1FTUrFkTAGBtbY3U1NQ31q2traXHVatWBQDpuYr2JyIiIvn0YgVi69at2L9/P/bu3Yu9e/eib9++cHR0xN69e6FUKhEVFQUhBICX93WIioqCSqUCACiVSkRGRkrPlZiYiEePHkGlUsHa2hq1atXSqEdGRsLKygo2NjalO0kiIqJyRC9WIGxtbTUev/POO6hUqRLs7OxgaWmJxYsXw9/fH4MGDUJoaCgyMzPRvXt3AMDAgQPh7e0NFxcXKJVKfP/99/D09ETdunWlekBAAGxsbGBoaIiAgAB8/vnnpT1FIiKickUvAsRfMTMzQ1BQEGbOnImdO3fC3t4ewcHBMDMzAwA4OzvD398fy5cvR3p6Ojw8PODv7y/tP2zYMKSlpcHHxwcGBgbw8vLCsGHDdDUdIiKickEhio4N0Gu8vLwQFham6zaIiLSy9piuOyg5IzrpuoOKQc77nl6cA0FERERlCwMEERERycYAQURERLIxQBAREZFsDBBEREQkGwNEKRJ55fv7N8r7/IiI6P/o/X0gyhOFsQnuj+mj6zZKTJ1Vu3XdAhERlRKuQBAREZFsDBBEREQkGwMEERERycYAQURERLIxQBAREZFsDBBEREQkGwMEERERycYAQURERLIxQBAREZFsDBBEREQkGwMEERERycYAQURERLIxQBAREZFsDBBEREQkGwMEERERycYAQURERLIxQBAREZFsDBBEREQkGwMEERERycYAQURERLIxQBAREZFsDBBEREQkGwMEERERycYAQURERLIxQBAREZFsDBBEREQkGwMEERERycYAQURERLIxQBAREZFsehMgEhISMGrUKLi5ucHT0xPz5s1Dbm4uAODhw4cYOnQoVCoVunXrhvDwcI19z58/j549e0KpVMLb2xvx8fEa9a1bt8LT0xPOzs6YOnUqsrKySm1eRERE5ZFeBAi1Wo1Ro0bBxMQEP/30ExYtWoRjx45hyZIlEEJgzJgxsLCwwK5du9C7d2/4+Pjg/v37AIDExESMHj0avXr1wu7du1GjRg2MGTMGhYWFAIAjR45g6dKlmDlzJrZs2YLY2FjMmzdPl9MlIiIq8/QiQMTExCAhIQFz585FgwYN4O7ujvHjx2Pfvn04f/487t69i++++w4NGzbEV199BWdnZ+zatQsAEBoaCgcHB4wYMQINGzbEDz/8gMTERJw/fx4AsHnzZgwePBgdO3ZEs2bNMGvWLOzZswcvXrzQ5ZSJiIjKNL0IEPXr10dwcDCqVq0qjSkUCqjVakRHR+ODDz6AmZmZVHN1dcWVK1cAANHR0XBzc5NqlStXRtOmTREVFYWCggLExsZq1FUqFQoKCnDjxo1SmBkREVH5pBcBonr16vDw8JAeFxYWYtu2bXB1dUVKSgqsrKw0tre0tERSUhIAvLWenJyMjIwM5ObmatSNjIxgYWEh7U9ERETy6UWA+G9z587FjRs3MHHiRGRnZ8PY2FijbmJigry8PABAdnY2TExMXqur1Wrk5ORIj99UJyIior9HrwKEEAJz5szBjh07sHjxYjRq1AimpqZSWCiiVqtRqVIlAICpqelrYaCobmpqKj1+2/5E+kr8/xOBy6vyPj+i8s5I1w0UKSwshJ+fH/bt24clS5agU6dOAABra2vExcVpbJuamoqaNWtK9ZSUlNfqjRo1goWFBUxNTZGamorGjRsDAPLz85Genv7aYQ8ifaMwMEBazGldt1Fiqjt9qOsWiOgf0JsViHnz5mHfvn0IDAxEly5dpHGlUom4uDiNezdERkZCpVJJ9cuXL0u17OxsXL9+HSqVCgYGBmjWrBkiIyOl+pUrV2BoaIgmTZqUwqyIiIjKJ70IEFeuXMHmzZvh4+MDR0dHpKSkSP+5u7ujVq1a8PX1xa1btxAcHIzo6Gj07dsXANCnTx9ER0dj9erVuH37Nvz8/FCrVi20atUKADBo0CBs2LABR44cQWxsLGbPno0+ffpoXPFBRERE8ujFIYzDhw8DABYvXozFixdr1K5du4ZVq1bBz88PXl5eeP/997FixQrUrl0bAFC7dm0EBgZi7ty5WLNmDZRKJVatWgUDg5fZ6OOPP8bDhw8xa9YsqNVqdO7cGb6+vqU7QSIionJGIYQQum5CX3l5eSEsLKxYn/P+mD7F+nz6pM6q3bpuodzhORAkx9pjuu6g5IzopOsOKgY573t6cQiDiIiIyhYGCCIiIpKNAYKIiIhkY4AgIiIi2RggiIiISDYGCCIiIpKNAYKIiIhkY4AgIiIi2RggiIiISDYGCCIiIpKNAYKIiIhkY4AgIiIi2RggiIiISDYGCCIiIpKNAYKIypTCQqHrFkpUeZ8flR9Gum6AiEgOAwMFLsY903UbJcbdwVzXLRBphSsQREREJBsDBBEREcnGAEFERESyMUAQERGRbAwQRERUbuXll++rWnQ5P16FQURE5ZaxkQKj5j/VdRslZs2Ud3X2d3MFgoiIiGRjgCAiIiLZGCCIiIhINgYIIiIiko0BgoiIiGRjgCAiIiLZGCCIiIhINgYIIiIiko0BgnROFOTruoUSVd7nR0QVE+9ESTqnMDRCTsgCXbdRYir1n6zrFoiIih1XIIiIiEg2BggiIiKSjQGCiIiIZGOAICIiItkqTIBQq9WYPn063Nzc0Lp1a6xdu1bXLREREZVZFeYqjAULFiAqKgobN25EUlISJk+ejFq1auHjjz/WdWtERERlToVYgcjKykJoaCimTZsGR0dHdOrUCcOHD8e2bdt03RoREVGZVCECRFxcHNRqNVxdXaUxV1dXxMbGIj+fN/khIiKSq0IEiJSUFJibm8PU1FQaq1GjBvLy8pCWlqbDzoiIiMqmCnEORHZ2NkxMTDTGih6r1eq37vfw4UN4eXmVaG/lCn9WbxbCn8ubLdF1A1SGHFyl6w70U3H/2n348KHW21aIAGFqavpaUCh6XLly5bfud+HChRLti4iIqKyqEIcwrK2tkZGRoREiUlJSYGJiAnNzcx12RkREVDZViADRpEkTGBsbIyoqShqLjIxE06ZNYWRUIRZhiIiIilWFCBCVK1fGp59+itmzZyMmJgbHjx/Hhg0b8Pnnn+u6NSIiojJJIYQQum6iNGRnZ2PWrFk4cuQIqlatiqFDh2Lo0KG6bouIiKhMqjABgoiIiIpPhTiEQURERMWLAYKIiIhkY4CgCu/u3bvSn/Py8sCjekSlq7CwUNctFKui+ZT33yUMEFTh7du3D3v37gUAPH/+HAqFQscdlS/l/Zco8H9zTE5O1nEnZY8QAgYGL9+K7t+/r+Nu/rnnz5/jt99+Q05OTrn/XcIAQRXas2fPEB0djZCQEAwaNAh9+vSBEAIFBQW6bq1cEEJIv0RDQ0Nx79493TZUQhQKBY4cOQJvb2/ExcXpup0yo7CwUHp9REREYNSoUbh06ZKOu/r7/vzzT8TFxSE4OBhnzpzBwYMHcfjwYV23VWIYICqAVz8B/vdSYUX4dPhXzM3NMXfuXDx58gQxMTHw8vKCQqGAoaFhuVtWLW2vvjncuHED27dvxzfffIPExEQdd1b87t27h7Nnz+Krr76Cg4ODrtspEwoLC6WVhz179iAkJAR37tzBggULcPHiRR13J9+cOXMwf/58vP/++6hatSp++OEHTJgwAdnZ2QDK5+9aBohyrugT4Llz5zB79mz861//wrJlyxAeHg4A5X6J7W2K/jEXFBQgOzsbtWrVgpOTE86fP4/du3cDAAwMDBgi/oGiN4elS5di9uzZyMrKwvXr1zFmzJhysVRd5P79++jRoweOHj0KKysrXbdTZhS9PhYtWoTFixejfv36GDhwIPLy8hAQEIBz587puEPt3bhxA/v27UO3bt1gbW2NL7/8Es+fP0fNmjVhYmKC3NxcKBSKchciDGfNmjVL101QyVEoFDh27BgmTpyIxo0bw83NDbt378aZM2fg5uaGGjVq6LrFUvfqJx8AePfdd/Hpp5/Czc0NERERiIqKgoGBAZo0aVJhA1ZxCQkJQVBQEGbPng1vb2+oVCrcu3cPO3fuRJs2bcr0d9EUhXOFQgFjY2OcO3cONjY2aNmypa5bKzPu3r2LNWvW4LvvvsNnn32Gdu3aoX79+khISMDRo0dRr1491KpVS9dt/k9JSUnYs2cP3NzccOfOHQQFBWHatGlISUnBiRMnUKVKFTRs2BBGRkYah/XKOgaIci41NRUzZszAl19+ifHjx6NZs2ZYs2YNBgwYgKZNmyIlJQXVq1fXdZul4v79+zA3N5f+8W7YsAEbN27E7t270bhxYzRs2BBNmzbFpUuXcOXKFRgaGsLBwQF37typMD+j4iSEwO7du2Fvb4/BgwfD3NwcjRs3hq2tLX7//Xf8+uuv8PT0RLVq1XTdqiyvBgfg5bf9uru7Iy8vD2vWrIGFhQWcnJx03GXZkJaWhk2bNqFbt26oU6cOAMDW1hbvvPMOdu3ahStXrqB+/fqwtbXVcad/zdraGjk5OQgMDMThw4cxduxY9OrVCx07dsTp06dx5swZVK1aFQ0aNChX37/EQxjlnKGhIfLy8tC1a1fcv38f7dq1Q7t27TBixAisXr0aYWFhum6xVKxZswadO3fGtWvXAAABAQEICgqCqakpUlJSMGjQIERERKBBgwaYMmUKzMzMsHbtWri4uMDPz0/H3ZcN/708q1AokJmZiVu3bmmMN2/eHG3atMH169fh4+ODBw8elGab/0hReLhw4QKWLVuGiRMnYtWqVXj8+DHGjx+PMWPGYM6cOdixY4euW9U7bzocaGRkhJo1ayI+Ph75+fnSeKtWrVCnTh08f/4cK1euxPXr10uzVVmK5uXh4YHc3FwYGRnB0NAQaWlpqFy5MgIDA2FjY4NNmzYhJCQECxYswK+//qrjrosHVyDKgaJfakUvZIVCgeTkZJiamiI9PR3r169H7dq1MXPmTLRp0wazZ8+GkZERTp48iT/++AOffPKJxpJ+eWRjY4OEhASsW7cODg4OuHTpEqZPnw5vb2906tQJDx48wMqVK6FUKuHk5ARHR0eYmZmhUaNGmDdvHgwNDXU9Bb326mEhtVot/byePHmCs2fPok6dOqhVq5Y0npqaipSUFJiZmeHChQvw9PSEiYmJzvrXlkKhwNGjRzF+/HjUrVsXJiYmOH36NNauXYuBAweiRYsWEEJg2bJlqFy5MpydnXXdsl549fURGxuLP//8EzY2NqhevTru3r2LjRs3okmTJrC1tYWhoSGePXuG8PBwtG7dGnfv3kWVKlWgUql0PIs3K1qJevLkCbp16wYLCwsEBgbC2toatWvXRrVq1dC5c2ecOXMGZ8+eRUREBIYOHVo+zpcRVObdu3dP4/GlS5dEu3btRHJyshBCCH9/f2Fvby9GjBihsd24cePEzJkzRWFhYan1qkuJiYlixIgRwtHRUbRt21bExcVJtZSUFDFhwgShUqnEuXPnhBBCFBQUSPW8vLxS77esePX1ExoaKr755hsREBAg7t69K/Lz88XAgQNF165dxc8//yxSU1PF06dPxYgRI4S/v78IDQ0VHTt2FHfv3tXdBGRISkoSPXv2FFu3bpUee3h4iAULFoj4+Hjx/PlzIYQQ8+bNE+7u7uLZs2e6bFfvLF68WHh6eoomTZqIXr16icjISFFYWCgmTJggnJ2dxfTp00VgYKAYPHiw8PLyEkII4ePjI0aPHq3jzl9X9LrPyMgQT548Eenp6VJt5syZwtHRUWzdulWkpaUJIYTIzc0VsbGx4vHjxzrptyRwBaKMO3bsGIYPH46GDRuifv36AIDff/8d169fx/DhwwG8PEnw+fPn+P3332FhYYHbt29j7969OHz4ML777jtYWlrqcgolSrxywpKZmRnatGmDR48e4fLly+jatStq164NAKhSpQpcXV3x6NEjLFy4EO3bt4e1tbX0POV9hebvevXnu2TJEqxevRrvvvsu9u/fj9jYWDRt2hTDhw/HuXPncOzYMaxZswbHjx9Heno6goKCUL16densdX07z+TZs2d48OCBRl/JyckICwvDzJkzkZ6ejn79+qFdu3aYOHEigoKCcPXqVbRo0QLOzs4YMGAALCwsdDgD3cvPz5f+7fzyyy/YuHEjpk2bhv79+yMiIgL79++Ho6MjhgwZAgMDA/z555/4448/8P7772PFihUwNDTEiRMnYGdnh5YtW+rNyYdFr/sTJ05g4cKFCA4OxsWLF3Hx4kV07NgR7dq1Q1paGoKCgvDee+/B1tYWZmZmsLKyQtWqVXXdfvHRcYChf+jy5cvCx8dHdOrUSRw9elQIIcSvv/4qpfcicXFxYu7cuaJly5aie/fu4l//+pe4ceOGLlouNa+uIPzyyy9i3rx5Qgghnjx5IoYPHy7c3d1FbGysxj7Jycli+fLlXHHQwqsrD3FxccLb21tcvnxZCCFEQkKC6N69uxg8eLCIjY0VhYWF4tq1a2LPnj3i2LFjQq1WCyFeflLv3bu39ClNn5w9e1Z4e3uL+/fvixs3boinT5+KO3fuiD59+ogDBw6Idu3aienTp0uvsyFDhohx48bpuGv9ULSKV+TQoUNi9erVYuPGjdJYfn6+6NWrl+jQoYOIjIyUxjMyMkRycrK4e/euWLp0qXBzcxO3b98urda1dvLkSeHk5CTWrVsnLl26JJYuXSrs7e3FiRMnpG2KVn9DQ0PL5UovA0Q5EBsbKyZMmCDat28vzp07J6Kjo4VKpRK///67+PPPPzW2TU9PF8+fP5eWWsurV8PD6dOnxbhx44Srq6tYv369EEKIp0+fimHDhr0xRBRhiHizL7/8Uly7dk16vG3bNvHFF1+I/v37ayzZ//HHH1KIuHDhgjQeHR0t/P39xbfffitcXV3F9evXS7V/bYWHhwtvb2/RvXt3YW9vL65evSpyc3PFp59+Kuzt7cW3336rsf2ECRPE/PnzRWFhYbl8s9DW+vXrxSeffCIKCwtFXl6eyM3NFa6ursLe3l5Mnz5dY9v8/HzRu3dv0blzZ3Hy5EmRn58vbt++LUaOHCk6d+4sevbsqfFa05XY2FiRkZEhhHgZnHNycsTkyZNFYGCgEOLlh5J27dqJhQsXijt37ohdu3ZJ+y5cuFAvA1Bx4CGMMqzoTn9WVlZ47733kJiYiJCQEGRmZuLKlSs4efIkdu/ejUOHDiEyMhIPHjyAqakp7OzsysQJa3+XeOXe+vPnz8ePP/4IExMTvHjxArGxscjIyEDbtm3h6emJmJgYbNy4ES4uLrCxsdF4Hh62eF16ejri4+Px8ccfSydE5ubmIiwsDAkJCVCpVKhbty4AwNLSEi1atMDPP/+MM2fOoGnTprC2tsbZs2dx48YNVK1aFf7+/rC3t9fhjN7Ozs4Ojx8/xoEDB2BnZ4du3brhvffeQ+vWrXHixAmo1WqYmJjgyZMn2LFjBw4dOoQZM2agevXqerPUrgu1a9fGl19+CQMDA9y6dQvW1tYYNGgQTp06hVu3bsHe3h61atWCQqGAgYEB+vbti3Xr1uHZs2fo3r07qlevjtq1a6Nv377o168f7OzsdDqf+/fvo2fPnqhZsyYaNGgAU1NTGBgYYP369XBzc4OVlRW8vLzw4Ycfws/PD+Hh4dixYwdatWoFc3NzeHh46N3huWKj6wRDf0/RJ5xXVxJu3LghvvnmG+Hq6iq8vb1FXFycOHnypJg6daoYNGiQaNu27WsnXJYn/72qcvLkSdG6dWtx5coVUVBQIJ4+fSoCAgJE7969xYoVK4QQQqSlpYk+ffqI4cOH66LlMi04OFicOXNGCCHE1atXRefOncWQIUPExYsXNba7ceOGmDhx4msrOq+uEumbot5CQ0PF0qVLxdChQ8XAgQPF77//LoR4eUJu//79RZcuXUT79u1F37599XYlRVfCw8OFvb292L17txDi5aGJTp06iR49ekgnTxYpLCwU+fn5umr1fwoPDxfNmjUTW7duFc+ePROFhYXCx8dHTJw4UbRv31785z//kbbdvXu3aN++vcZJleUVA0QZVPTL7dSpU2LkyJHiq6++EgcPHhT5+fni5s2bwsfHR6hUKnHy5EmN/crzYYv+/fuLnTt3aoxt27ZN9OzZU+Tm5kpjycnJYubMmaJVq1bS4YzMzEyNbejNXv2Fn5mZKYYMGSKaNWsmBYaoqCjRqVMn8dVXX0lvtP+t6NyH/34+fVHUU0pKikhKSpLGjx8/Lj7//HMxYMAAaW75+fni0aNH4v79+7zaQrz+/zMhIUFMnz5dNG3aVOzZs0cI8TJEdOzYUboC479DpD6HiFOnTokPPvhAbNmyRQjx8gOKvb296Nmzp3jy5Im03dy5c8UXX3xRrn/fFuEhjDJi586dyMrKgq2tLRQKBQ4fPgwfHx80a9YMSUlJOHHiBIyMjPDhhx/CwcFBurWqpaUlGjVqBAAwNjYut0urBQUF+PTTT2FoaIjs7GwYGxvj7t27uHjxItq2bQsLCwsIIWBmZoYaNWpg+/btiI+PR1ZWFlq1agVDQ0MUFBTwsMVb/Pftv01MTPDhhx/i3r17WLVqFZydneHq6gonJyfs2rULd+/eRfXq1aW7CxZ59X4a+vhaVCgUOH78OPz8/LBx40ZERESgefPmcHJygoWFBa5evYozZ86gTp06iIuLg7m5Od5//32YmprqunWdevX1kZycjCpVqsDCwgLOzs5IT0/H8uXLUbt2bSiVSvTu3Rs7duzAL7/8gjZt2mjcTl+f//3Z2dnByckJkydPxjvvvINevXrh/fffR0hICO7du4dTp07h2LFj2L9/P5YsWVImbsH9TzFAlAErV67E6tWrMXz4cLzzzjuIi4vD1KlTMXXqVIwbNw4dO3bEsmXLcO/ePRgZGaFNmzZo3LgxYmJicOrUKfTq1avchgfx/y+ncnR0hKGhIVauXImLFy/CyckJ5ubm2LRpE9RqNZycnFC5cmUAL2/4cvv2bTRp0gTR0dFo1qwZLC0t9fqXly69+uZw8uRJxMbG4vnz52jQoAE+/PBD3Lx5E6tXr5ZCRLNmzbBy5Uq88847aNOmjY67lycqKkq6DXHLli2ly089PDygVCrx7rvv4sqVK9iyZQv27t2LwYMHV/hLNcUr5xytXbsWS5Yswa5du5CbmwtHR0d4eHi8FiJ69eqFmzdvYuDAgWXq352dnR2USiV8fX1hbm6Ofv36wcXFBQ8ePEBSUhKqV6+u1+f1FDsdr4DQ/5CbmytGjhwpXR72/PlzER4eLnr37i1yc3PFkydPxNixY8WcOXPExIkThZubm/jpp59EZmamePDggcYybHn06rLp48ePxfLly4W9vb0ICgoSQghx+PBhYW9vL6ZOnSqOHDkirl+/LoYMGSImTZok4uPjhUqlkpZX6XWv/nznz58vPDw8hEqlEh999JFYvny5EOLla3Ls2LHCxcVFOpzxxx9/6PVy9JvcvHlTHDp0SJqXEEI8fPhQdOnSRfTu3VskJCQIIYS4ffu2OHnypPS4Inv19bF+/Xrh7u4uVqxYIb744gvRtWtXERAQIDIzM0VGRoaYMWOGaNq0qdixY4fGc5S114kQLw9nODg4iM2bN2uMv3qIriLgCoSeKygoQHR0NG7cuIGYmBhMmTIF7733Hp48eYIOHTpg7969ePHiBcaPH49u3bph9erViImJwZ07d9C7d+8y90VFcrz6yfiHH35AYGAgli1bBmNjYyxZsgRGRkbo378/VCoVwsLCcPDgQelQT2BgICwtLXH27Fm0bNkS9erV0/Fs9I945SZRZ8+exY4dO7Bo0SL0798fL168wKlTp/D48WN4enqibdu2+OOPP7Bo0SJ06NABDg4OMDAwKDOHhbKysvDZZ58hLCwMderUQceOHQEA1apVQ/v27bFr1y6Eh4ejefPmqF+/PurWrVumv0m0uBS9Pq5fv46DBw/i66+/Rr9+/fDpp5/iwYMHOHXqFJ48eQI3Nze0atUKCQkJuHz5Mvr06SNdRVYWXh//rWgl4j//+Q+MjY3RsGFDmJqaVrxb3us6wdD/lpeXJz777DPh6Ogopk6dKoQQ4uLFiyIlJUV4e3uLrVu3StdP9+/fXwQGBoqHDx/quOvSExMT89qJe4GBgcLe3l6sXr1aCCHEs2fPRGJiorh+/brIysoSQggREBAgPD09K9TP6u/47bffxPjx48WsWbOksSdPnojvv/9e9OjRQyxdulQI8XIlYvHixWXq/hmvfoK+efOm6NGjh+jVq9dr90958OCBaNGihfj8888r3KfM/+XEiROiV69eolWrVq+dPDt37lzRo0cPERAQIJ4/fy4yMzOlEyf18SRauY4fPy5atGhRIa64eBOuQJQB2dnZmDdvHho1aoSnT5/C0NAQnTp1wt27d7Fu3TpMnz4d1apVw/bt25GWloZvv/0WNWvW1HXbpSIhIQG9evVCSkoKPvvsM+n20+7u7lAoFFi+fDlMTEzg4OCAmjVr4vbt21iyZAk2bdqECxcuICgoSLoFOGkSQuD58+cIDg7G2bNnYWBg8P/au/OoqO7zj+PvGbZxG5BANQ4CigIiCgoiIhpxbQzEJRq3ak/VJEatRo0aUdEQLeS44VawDcZqFUXA1lolRm2EgBpQiFbrBokHMYggCqiEGZjfH3amoPklmiZlBp7XXx6Yuede53K/n/ne5/tcRo8eDUCzZs3o2rUrxcXFfP7551y/fp3BgwfTp08flEplvRbGpkj/79mVhw8fAlBZWYlGo8Hf35/9+/dz7tw5evfujVqtBkCtVjN8+HAGDBjQeNf0PyPDzIGBRqMhPz+fCxcuoFAoCAgIMPaZCQ4OpqCggOTkZOzt7enZs6fxwX+mfCHqsAsAABODSURBVH48qw4dOjBhwoRGPdP7fSRAmAFra2umTJlCWFgYp0+fJisrC6VSib+/PwcPHiQpKYnPPvuMTz75hLVr1z5V+d6Y2draGi/6Dg4OeHp6olKpgMchwsLCgpiYGNzd3fH09ESn01FZWUmPHj2YN28enTp1auAjMC11BweFQoGNjQ2+vr48ePCA06dPU1VVRUBAAPCfEHH16lWqqqoYPHiw8b2mPDgYwsPJkyfZvHkzH330EcePH0er1fLSSy8RHBzMzp07OXv2LL179zYODq1atWryty3qDvwFBQWUlpbi4OBA3759uXv3Ll988QWVlZV4e3tjZWUFPA4RKpWKcePGGd/bmAq6G3NTvh8iAcJMWFtbo1Kp8PHx4dy5c5w6dQqVSsX06dOpqKjAwcGBlStXNp3q3zqcnJzw9fVlxYoVtGnTBjc3N+Mfda9evWjXrh1hYWEolUpat26Nn58f3t7eTX4weFLdweHw4cMcPnyYhw8f0q1bN7y9vSktLSU9PZ3y8nL8/f2BxyEiICCA4cOHo1Qq69VNmCrDQ5Dmzp1LaGgoQ4YMoaKigq1bt9K/f3+8vLwIDg5mz549nDhxgv79+zfZb5h11T0/Nm3axIYNG9i+fTslJSUEBQXRr18/8vLySE9Pp6Kiol6I6N69u1nVxIhnIwHCzKjVanx8fMjJyeHs2bPY2toyc+ZMgoODG/VTNX+Is7OzcXmVo6MjHTt2NIaILl26oFQq0Wq1Ta/I6Rnpn2j/vWXLFoqKivjzn/+MVqslMDCQnj17UlBQQFpaGhUVFfj5+QFgY2NjVtPS3377LRs2bCAsLIy33nqLli1bsmHDBsaMGUP37t25du0aPj4+9O7dmyNHjhAaGmq8ldGUGYLhunXr2Lt3L1OnTiUkJIStW7fy8OFDgoKC6N+/P3l5eWRmZlJYWEivXr2wtLQ0bsMczg/x7CRAmCG1Wo2vry+fffYZ169fZ9CgQcZp+6bM0OglPDyc5s2b06VLF+M3IEDCw/cwDA4ff/wxe/fu5fe//z0LFy6kuLiY/fv3o9PpCA4OxtfXl8LCQpKSknjhhRfw9PR8ahumrrq6mq1btzJy5Ejs7OwYPXo0wcHBLF++nNTUVHbu3ElISAjOzs6MGTOG1q1bN/Qum4ycnBzi4uLYsmULQ4cOpbq6muTkZHJzcykqKiIkJIT+/fuTm5vLo0eP+OUvf2k254V4fpY//BJhijQaDR9++CEKhUK+HdXRr18/YmJi2L59O2+88UZD745Z0el0nDlzhunTp+Pn50dOTg5ffPEFAwcOZNu2bQC89dZb/Pa3v8XJyYmwsLAG3uNnY7itkpeXx/3797l79y7+/v5kZGTw/vvvExISQkREBAqFgoqKCiorK43NoZry/e3vUlpaikqlwtPTk8uXLxMbG8uqVatwdHTkzTffxNbWlpEjR/Lhhx8a/9/N4baW+HEkQJixJ58eKR4bNGgQAwcOlIvXc6itraWyspL8/HyGDRtGcXExiYmJhIaGMnv2bGxsbNixYwdXrlzh3XffZerUqcDjPiWmPrNjaP2+atUq7Ozs0Ol0tGrVivPnzxMQEMCCBQuMx1BaWoqTkxPV1dXGzqVN1XcFgBYtWmBnZ8e9e/dITU3F2dnZWKysUqn4+OOP0Wq1LFu2zKxua4kfRwKEaJQkPHy/Jy/sSqUSOzs73n77bTp06EB2djZlZWWMGTMGAJVKhbOzM05OTvWWvZp6eAC4ePEiK1euZPHixfj7+6NWq1Gr1cTGxhIfH8/q1atp06YNJSUlpKamsmfPniYfHuqeH/fv38fCwgKFQkGfPn1wcXGhefPm/OMf/2DWrFk4OTlx8+ZNQkJCGDVqFEFBQcbtSHho3CRAiEZLwsN3qzs4fP7559y7d4+SkhJGjhxJWFgYlpaW7Nixg1/84hf4+fmh1+spLCxk2rRpjBgxwuy+WV69ehUnJyeGDRtWLxiEhoayceNGFAoFaWlpdOrUiYSEhCa5kqku/RPPtsjIyKC0tBQLCwvmzZtHQEAAN2/eJC8vjxYtWlBVVcUHH3zAt99+S3BwMAqFwixmpsR/TwKEEE2MYXBYs2YNn376Kfb29lRVVREbG0tERASvvPIKDg4O/O1vf8PT05PDhw9TXl5OaGiocWbHXMIDQHFxMRUVFcaCWkP4qa2tBWDixIm4u7ujVCqbfM1D3WAYExPDvn37CA8Px8XFhZUrV7J06VL27duHRqNhyJAhTJs2DVdXV5o1a0ZiYqLx/JDw0DSYz1VACPFf0+v1ACQlJZGcnExMTAx79+5l3rx53L9/nzZt2lBeXs6kSZPo0aMHycnJ2NnZkZycjKWlJTU1NWY3sxMUFMSNGzfYvXs3gLFfhU6nw8PDg1atWqFSqZp0eMjKygIw9mooKSnh1KlTREVFERYWRklJCQUFBSxatIjCwkKKiopYuXIlW7ZsYcaMGSQlJWFlZYVOpzO780P8eDIDIUQjt3btWry8vBg+fLjx4n7r1i1GjBiBl5cXhw8fZvny5axYsYKWLVsSGRlJdHQ0cXFx3L17l9atW6NQKNDpdPXW9JuLbt26MWvWLKKjo6mtrWXs2LEAHDlyBK1W2+RXMW3ZsoVDhw4xc+ZMXn31VSwsLLh37x75+fn4+vqSkZHBggULWLRoEa+++irLly/nzp07bN26lcGDBxu3U1NTY5bnh/jx5NMWohG7fv062dnZZGVloVKpGDhwIABXrlyhZcuWZGdns2zZMt59910mTJjAtWvXSE1N5bXXXqNPnz7G5z7o9XqzHhymTp2KtbU169atIyEhgWbNmlFaWsof/vCHJt2ADWDIkCHk5OSwb98+9Ho9I0aMwMXFBQ8PD1atWsXx48cJDw9n7Nix6PV6qqqqsLa2fuo2hdy2aHqkkZQQjZi9vT0uLi5cvnyZtLQ07O3t6dixIxYWFiQmJrJ7926WLVvG+PHjAbh58yZnzpxh3Lhx9Roomfu0tJWVFX5+fgwaNAhnZ2cCAwOZPXs2bm5uDb1rDaq2thZHR0e8vb3JzMzkyy+/xNLSEi8vLy5dukRSUhKhoaHMnTsXeDzLkJCQgJubG8HBwQ2896Khme9XCiHE9zIsY+3VqxcA8fHxbNu2DRsbG/r168fBgwdRqVRotVqqq6spKioiLi6OF198EVdX14bd+Z+Jh4dHk19lUZehHsTNzY2lS5eyevVqEhISUKvVvPfee5SWlnLhwgVmzJiBu7s7WVlZVFRUsGDBgobedWECFHpDVZUQotGp2wsjKyuLjz76iJKSEpYsWULHjh1Zs2YN6enpPHr0iLZt29K8eXP27NmDlZWVWS3VFP+dut06V69eTWVlJbNmzaJv377s27eP9PR0amtrad++PUuWLDEW1Mpti6ZNAoQQjcijR49o1qyZcfA3/Hn/fyHC39+fmzdvkp2dTbt27fDz88PCwsJsCybF97t27RpKpbLerRtDEHgyRDx48IA33njDWChZXV1tXKki54cAqYEQotG4c+cOSUlJuLq60rx5c+BxcKjblVOj0dC2bVsuX77MsWPHcHBwwMfHB09PTzQajXEZnwwOjU9ZWRlHjx4lIyMDPz8/jh8/TufOnY2zTIbzxN7eHm9vb86cOUN2djZarRZvb2/jbIP0eRAGcpUQopH45ptvSElJ4e7duxQVFeHh4cGbb74J1G/tbaiJ2L59O6tXr0atVuPv72/cjgwOjc+0adMYNWoUAKmpqZw7d46vvvqKwMDAp4plDTUR4eHhLFy4kH/961/1tmXuBbXipyMBQohGonv37oSGhrJr1y7Ky8sZOXIk8J/720+GiOrqajIyMujRo0cD77n4OcXGxpKTk0NsbCzW1tZ8+eWXpKamMnz4cHQ63VOvrxsiNm/ebHxonzxbRjxJKqSEaARqamoA6NChA0qlEo1GQ2ZmJrdv3zYOCEC9f/ft25dFixZhYWFhfL9oXGpqaigvL0ej0VBWVkZsbCxVVVXMnDmTnJwc4uLi+Prrr596n+E8MdzWqq2tlfAgniI1EEKYsZqaGpRKpfE+tr29PePHj0er1XLixAlu376Np6cnLVu2/M6ZCANZbdE4KZVKHB0d2b59O8eOHePUqVNs2rSJYcOGoVAoSE5OprKyEhcXF+zs7IzvM5xXBhIexHeRACGEmaqtrTXWKxw5coTTp09TUlKCr68vAQEB3Llzh/T0dIqLi/H29jYWVoIMCE2FoVFUbm4uubm5+Pj4MHjwYGxtbfH19cXCwoKUlBTKy8uxt7fn0KFD9OzZUwKleCYSIIQwM3PmzCE7O5sBAwYAEB0dzaZNm8jLy2P//v2UlZXx0ksvERgYSElJCWlpaZw/f564uDgePHhAz549G/YAxM/OMMOk1+upqanhn//8J6+//jpJSUl8/fXXdOrUybgCR6lUkpKSwsGDB7lw4QJTpkyRACGeiRRRCmFGdDodvr6+rF+/HrVazfDhw8nIyGDv3r20atWKrKwsli9fjlarJTIykjlz5mBtbc358+dRq9X8+te/buhDED+zug3AdDod1tbWzJ8/H5VKhYuLC9OnT0ehUDBnzhw8PDyYMmUKXl5elJWVMXDgQCwsLKSJmHgm0khKCDNTXV3NgQMHiIyMpFevXtja2rJu3TosLS3RarUcOXKEZcuWMWrUKN5//30AKisradGihVk/VVP8sLoD/5/+9CcuXLhAYWEhbm5uTJ48GQ8PD3Jzc/nNb35D3759mTNnDu7u7vW2IR0mxbOSACGEmXjyW2FCQgLr1q1Do9GQkpJivOhrtVpSU1OJiIhg4MCBrFu3zvgeWYrXeNX9bNeuXUtycjIzZ85Er9ezY8cOtFothw4dwtbWlpycHKZPn46npyfR0dG0b9++gfdemCOZoxLCDOj1emN4+Pvf/05+fj4TJkxg4cKFXL9+nZiYGONrraysePnll1myZAnFxcXU1tYafyfhofG5ffs28J/P9sqVK5w8eZLNmzczefJkXFxcKC8vZ/HixeTl5XH79m169OjBtm3bsLKyQqPRNOTuCzMmAUIIE1d3DX5ubi47duxg9erV3Lp1i3HjxhEeHk58fDwbNmwwvsfS0pLXXnuNXbt2Gdfxi8ZnwYIFzJ07l7y8POPPKioqKC8vN7arfuedd5g/fz5Dhgxh165dJCUlodVq8ff3Z8eOHXJ+iB9NVmEIYcLqzjxs3ryZpKQkbt26xZUrV8jLy8PX15d+/fphZ2fHxo0b0Wq1BAYGAsg6/iagbdu2xMfHU1BQgKenJ/b29pSVlXHmzBnu379PVFQU7733HhMmTEChUBAfH4+dnR0hISH1tiPnh/gxpJJKCBNmuLDv3r2bnTt3sn79epycnMjMzOTTTz8lMjKSFStWMGnSJBQKBZGRkbz44ouMGzeugfdc/C/4+Piwe/duJk6cSG1tLUuWLKFr165YWVmxfv16Zs+ebTwXtFot1tbWODk5NfBei8ZCiiiFMDEffPABkydPxtXVFb1ebxwYXnjhBRYvXmx83dGjR4mLi8PR0ZEVK1bQrl07jh49SkhICFZWVg14BOJ/7eLFi0yaNIlevXrxu9/9Dr1ez7Rp0wB4+eWXcXBw4MiRI5SUlHDgwAFZhSN+ElIDIYQJuX37NpcvXzYWtikUCiwsLKiqqqKwsLDea4cOHYq/vz8nT54kMjKSgoIChg4dipWV1Xc+JEk0Xl27dmX37t1kZWWxdOlSamtr2bt3L507d+bYsWMkJiZib29PSkoKlpaW8uwT8ZOQGQghTMSTyzT/8pe/4ObmRrdu3diyZQsHDhwgJiYGLy8v45LNxMRE9u3bh1qtxtXVlcWLF6NSqRrqEEQDqzsTERUVhYODA9XV1Tx69Ai1Wi19QMRPSgKEECaiboC4e/cuQUFB+Pr6smbNGjQaDWPHjuXhw4dERETg4eGBjY0N8+bNo2vXrjRr1oyEhAQSExNxdHRs4CMRDenixYv86le/onfv3syfP79eoyjpAyJ+ShIghDABdS/sUVFRuLq6EhwczPjx43F1dSUqKorWrVszY8YMvvnmG3Q6Ha1bt6aqqopPPvmEGzdu8Pbbb7Nt2zZpCiS4dOkSo0ePZtq0aSxcuLChd0c0UhIghDAh2dnZLFq0iIiICAYMGMCNGzd4/fXX6dy5M9HR0Tg5OXHy5EmKi4uxtrbmlVdewdLSkqioKM6dO0d8fDxqtbqhD0OYgK+++or27dvL7Qrxs5EAIYSJ+OMf/0hKSgru7u5s3LjR+PO6ISIyMpKOHTsCcPbsWTIzMyksLOT48ePs3LmTLl26NNTuCxMlNQ/i5yKrMIQwEb1796aoqIi0tDRycnKAx7c2XFxc2L9/P/n5+cydO5cbN24AcP78eS5evIherychIUHCg/hOEh7Ez0VmIIQwIVevXmXcuHH07NmT8PBw3NzcjL/Lz88nKiqK2NjYeoOCPD1RCNEQJEAIYWIuXbrExIkTCQwMZOHChfVChIFWqzU2i5LKeiFEQ5AAIYQJMizF69OnD++88069pXhCCGEKpAZCCBNk6Cx44sQJ/vrXvzb07gghxFNkBkIIEyZL8YQQpkoChBBmQJbiCSFMjQQIIYQQQjw3qYEQQgghxHOTACGEEEKI5yYBQgghhBDPTQKEEEIIIZ6bBAghhBBCPDcJEEIIIYR4bv8HJghRzn54v30AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"white\")\n",
    "sns.set_palette(\"coolwarm_r\", n_colors=6)\n",
    "\n",
    "tweets = ira_tweets[(ira_tweets.c_mbfc!=\"-1\") & (ira_tweets.is_source==1)]\n",
    "media_type_counts = tweets[\"c_mbfc\"].value_counts()\n",
    "display(media_type_counts)\n",
    "plt.figure(figsize=(8, 6))\n",
    "x = range(len(media_type_counts))\n",
    "\n",
    "for i in x:\n",
    "    plt.bar(i, media_type_counts[mbfc_labels[i]])\n",
    "    \n",
    "plt.xticks(x, [mbfc_labels[i] for i in x])\n",
    "plt.ylabel('Prop of tweets', fontsize=15)\n",
    "plt.xticks(fontsize=15, rotation=45)\n",
    "plt.yticks(fontsize=14)\n",
    "\n",
    "# plt.savefig(\"fig/size-of-intersection500.pdf\", dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一旦考虑到science上面的fake news类别，会不会增加数据量呢？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4952276"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_tweets[(all_tweets.c_sci_f!=\"-1\") & (all_tweets.c_alex!=\"-1\")])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2193"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_tweets[(all_tweets.is_IRA==1) & (all_tweets.c_sci_f!=\"-1\") & (all_tweets.c_alex!=\"fake\")])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "考虑到c_sci_f后，总体增加3456053，IRA增加2193"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IRA作为源，或者参与到其中的消息是否更加爆炸？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "ira_as_source = ira_tweets[ira_tweets.is_source == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4798b0c550>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(ira_as_source.c_mbfc.value_counts(), index=[mbfc_labels[i] for i in range(len(mbfc_labels))]).plot(\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IRA作为源的新闻，被转发多少次"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets_count = all_tweets.groupby(\"retweeted_id\").count().tweet_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1e8554bd968c4051be9c36d9fb37ad72",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import sys, traceback\n",
    "\n",
    "media_len_ret = Counter()\n",
    "\n",
    "for _, row in tqdm(IRA_as_source.iterrows()):\n",
    "    try:\n",
    "        tweet_id = row[\"tweet_id\"]\n",
    "        c = int(tweets_count[tweets_count.index==tweet_id].values)\n",
    "    except:\n",
    "        c = 0\n",
    "        # traceback.print_exc()\n",
    "        \n",
    "    media_len_ret[row.media_type] += c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8da8f5a668>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(dict_media_len_ret, index=[str(i) for i in range(8)]).plot(\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "URL_count = all_tweets.URL.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IRA所讲的故事是不是爆炸性的故事？ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "7345it [00:01, 7173.16it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    351.173469\n",
       "1    944.808525\n",
       "2    588.846596\n",
       "3    572.043689\n",
       "4    237.193947\n",
       "5    544.730479\n",
       "6    235.702247\n",
       "7    107.436170\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0     412980\n",
       "1    1440833\n",
       "2     717804\n",
       "3     117841\n",
       "4     384017\n",
       "5     432516\n",
       "6     167820\n",
       "7      10099\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dict_media_len_ret = defaultdict(list)\n",
    "\n",
    "for _, row in tqdm(IRA_tweets.iterrows()):\n",
    "    c = int(URL_count[row.URL])\n",
    "    dict_media_len_ret[row.media_type].append(c)\n",
    "\n",
    "_ave = {k: sum(v) / len(v) for k, v in dict_media_len_ret.items()}\n",
    "_sum = {k: sum(v) for k, v in dict_media_len_ret.items()}\n",
    "\n",
    "d = pd.Series(_ave, index=[str(i) for i in range(8)])\n",
    "display(d)\n",
    "d.plot(\"bar\")\n",
    "plt.show()\n",
    "plt.close()\n",
    "\n",
    "d = pd.Series(_sum, index=[str(i) for i in range(8)])\n",
    "display(d)\n",
    "d.plot(\"bar\")\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "7345it [00:00, 13289.07it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    216.328990\n",
       "1    924.779412\n",
       "2    374.208145\n",
       "3    250.130435\n",
       "4    483.034188\n",
       "5     97.679104\n",
       "6    253.585366\n",
       "7    109.384615\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0     66413\n",
       "1    125770\n",
       "2     82700\n",
       "3      5753\n",
       "4     56515\n",
       "5     26178\n",
       "6     20794\n",
       "7      1422\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dict_media_len_ret = defaultdict(list)\n",
    "\n",
    "for _, row in tqdm(IRA_tweets.iterrows()):\n",
    "    if row.is_source == 1:\n",
    "        c = int(URL_count[row.URL])\n",
    "        dict_media_len_ret[row.media_type].append(c)\n",
    "\n",
    "_ave = {k: sum(v) / len(v) for k, v in dict_media_len_ret.items()}\n",
    "_sum = {k: sum(v) for k, v in dict_media_len_ret.items()}\n",
    "\n",
    "d = pd.Series(_ave, index=[str(i) for i in range(8)])\n",
    "display(d)\n",
    "d.plot(\"bar\")\n",
    "plt.show()\n",
    "plt.close()\n",
    "\n",
    "d = pd.Series(_sum, index=[str(i) for i in range(8)])\n",
    "display(d)\n",
    "d.plot(\"bar\")\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了作为对比，所有的URL又是什么情况呢？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading all-users.csv ... ...\n",
    "\n",
    "!! Already coded in the all_tweets.py for making all-users.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fake\n",
      "extreme bias (right)\n",
      "right\n",
      "right leaning\n",
      "center\n",
      "left leaning\n",
      "left\n",
      "extreme bias (left)\n",
      "Finished!\n"
     ]
    }
   ],
   "source": [
    "# all_users = pd.read_csv(\"data/all-users-mbfc.csv\", index_col=\"user_id\", dtype={\"user_id\": str})\n",
    "all_users = pd.read_csv(\"data/all-users-nc.csv\", index_col=\"user_id\", dtype={\"user_id\": str})\n",
    "\n",
    "# change the type\n",
    "# f_labels = [map_labels[k] for k in map_labels]\n",
    "all_users = all_users.astype({\"is_IRA\": int})\n",
    "\n",
    "mbfc_labels = [\n",
    "    \"fake\",\n",
    "    \"right\",\n",
    "    \"right leaning\",\n",
    "    \"center\",\n",
    "    \"left leaning\",\n",
    "    \"left\",     \n",
    "]\n",
    "\n",
    "labels = [\n",
    "    \"fake\",\n",
    "    \"extreme bias (right)\",\n",
    "    \"right\",\n",
    "    \"right leaning\",\n",
    "    \"center\",\n",
    "    \"left leaning\",\n",
    "    \"left\",\n",
    "    \"extreme bias (left)\"\n",
    "]\n",
    "\n",
    "for label in labels:\n",
    "    print(label)\n",
    "    all_users = all_users.astype({label: int,\n",
    "                                  label + \"_source\": int,\n",
    "                                  label + \"_source_rate\": float})\n",
    "\n",
    "print(\"Finished!\")"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# user_id > screen_name\n",
    "\n",
    "user_info = pd.DataFrame([json.loads(line.strip()) for line in open(\"data/user-top-info.json\")])\n",
    "user_info = user_info.rename(columns={'id_str': 'user_id'})\n",
    "user_info = user_info.set_index(\"user_id\")\n",
    "map_user_name = []\n",
    "for line in open(\"data/user-top-info.json\"):\n",
    "    d = json.loads(line.strip())\n",
    "    try:\n",
    "        dat = {\"user_id\": d[\"id_str\"], \"name\": d[\"name\"]}\n",
    "    except:\n",
    "        dat = {\"user_id\": d[\"id_str\"], \"name\": d[\"message\"]}\n",
    "    map_user_name.append(dat)\n",
    "    \n",
    "map_user_name = pd.DataFrame(map_user_name).set_index(\"user_id\")\n",
    "\n",
    "# Show the top sources !!!\n",
    "\n",
    "for label in labels:\n",
    "    print(label)\n",
    "    tmp = users.sort_values(label + \"_source\", ascending=False).head(25)\n",
    "    tmp = tmp.join(map_user_name)\n",
    "#     display(tmp)\n",
    "#     display(tmp[[label + \"_source\", \"name\"]].sort_values(label + \"_source\", ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Are IRAs the sources of fake news or influencers? \n",
    "\n",
    "分析IRA到底在选举期间的作用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "348"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IRA_users = all_users[all_users.is_IRA > 0]\n",
    "len(IRA_users)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_big_table(_users):\n",
    "    big_table = []\n",
    "\n",
    "    for lab in labels:\n",
    "        volume = _users[lab].sum() # 某种媒体上总和\n",
    "        source_volume = _users[lab + \"_source\"].sum()\n",
    "#         first_volume = _users[lab + \"_first\"].sum()\n",
    "        source_rate = _users[_users[lab + \"_source_rate\"] > 0][lab + \"_source_rate\"].mean()\n",
    "#         first_rate = _users[_users[lab + \"_first_rate\"] > 0][lab + \"_first_rate\"].mean()\n",
    "        user_volume = len(_users[_users[lab]>0])\n",
    "        source_user_volume = len(_users[_users[lab + \"_source\"]>0])\n",
    "#         d = dict(Nt=volume, Ns=source_volume, Nu=user_volume,\n",
    "#                  Rs=source_rate, Rf=first_rate, Nsu=source_user_volume, Nf=first_volume)\n",
    "\n",
    "        d = dict(Nt=volume, Ns=source_volume, Nu=user_volume, Rs=source_rate, Nsu=source_user_volume)\n",
    "\n",
    "        big_table.append(d)\n",
    "    \n",
    "    big_table = pd.DataFrame(big_table, index=labels)\n",
    "    big_table[\"Pt\"] = big_table[\"Nt\"] / big_table[\"Nt\"].sum()\n",
    "    big_table[\"Ps\"] = big_table[\"Ns\"] / big_table[\"Ns\"].sum()\n",
    "    big_table[\"Ns/Nt\"] = big_table[\"Ns\"] / big_table[\"Nt\"]\n",
    "#     big_table[\"Nf/Nt\"] = big_table[\"Nf\"] / big_table[\"Nt\"]\n",
    "    big_table[\"Nt/Nu\"] = big_table[\"Nt\"] / big_table[\"Nu\"]\n",
    "    big_table[\"Ns/Nsu\"] = big_table[\"Ns\"] / big_table[\"Nsu\"]\n",
    "\n",
    "    big_table = big_table[[\"Nt\", \"Pt\", \"Ns\", \"Ns/Nt\", \"Ps\", \"Nu\", \"Nt/Nu\", \"Nsu\", \"Ns/Nsu\"]]\n",
    "#     big_table = big_table[[\"Nt\", \"Pt\", \"Ns\", \"Ns/Nt\", \"Ps\", \"Nu\", \"Nt/Nu\", \"Nsu\", \"Ns/Nsu\"]]\n",
    "#     big_table = big_table[[\"Nt\", \"Pt\", \"Ns\", \"Ps\", \"Ns/Nt\", \"Nu\", \"Rs\", \"Rf\", \"Nt/Nu\"]]\n",
    "    return big_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nt</th>\n",
       "      <th>Pt</th>\n",
       "      <th>Ns</th>\n",
       "      <th>Ns/Nt</th>\n",
       "      <th>Ps</th>\n",
       "      <th>Nu</th>\n",
       "      <th>Nt/Nu</th>\n",
       "      <th>Nsu</th>\n",
       "      <th>Ns/Nsu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fake</th>\n",
       "      <td>1660</td>\n",
       "      <td>0.068189</td>\n",
       "      <td>363</td>\n",
       "      <td>0.218675</td>\n",
       "      <td>0.050291</td>\n",
       "      <td>104</td>\n",
       "      <td>15.961538</td>\n",
       "      <td>34</td>\n",
       "      <td>10.676471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>extreme bias (right)</th>\n",
       "      <td>2882</td>\n",
       "      <td>0.118386</td>\n",
       "      <td>246</td>\n",
       "      <td>0.085357</td>\n",
       "      <td>0.034081</td>\n",
       "      <td>123</td>\n",
       "      <td>23.430894</td>\n",
       "      <td>47</td>\n",
       "      <td>5.234043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <td>5273</td>\n",
       "      <td>0.216604</td>\n",
       "      <td>2797</td>\n",
       "      <td>0.530438</td>\n",
       "      <td>0.387503</td>\n",
       "      <td>172</td>\n",
       "      <td>30.656977</td>\n",
       "      <td>70</td>\n",
       "      <td>39.957143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right leaning</th>\n",
       "      <td>2164</td>\n",
       "      <td>0.088893</td>\n",
       "      <td>183</td>\n",
       "      <td>0.084566</td>\n",
       "      <td>0.025353</td>\n",
       "      <td>182</td>\n",
       "      <td>11.890110</td>\n",
       "      <td>37</td>\n",
       "      <td>4.945946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>center</th>\n",
       "      <td>4274</td>\n",
       "      <td>0.175567</td>\n",
       "      <td>821</td>\n",
       "      <td>0.192092</td>\n",
       "      <td>0.113743</td>\n",
       "      <td>254</td>\n",
       "      <td>16.826772</td>\n",
       "      <td>92</td>\n",
       "      <td>8.923913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>left leaning</th>\n",
       "      <td>5578</td>\n",
       "      <td>0.229132</td>\n",
       "      <td>2400</td>\n",
       "      <td>0.430262</td>\n",
       "      <td>0.332502</td>\n",
       "      <td>244</td>\n",
       "      <td>22.860656</td>\n",
       "      <td>72</td>\n",
       "      <td>33.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>left</th>\n",
       "      <td>2403</td>\n",
       "      <td>0.098710</td>\n",
       "      <td>394</td>\n",
       "      <td>0.163962</td>\n",
       "      <td>0.054586</td>\n",
       "      <td>206</td>\n",
       "      <td>11.665049</td>\n",
       "      <td>60</td>\n",
       "      <td>6.566667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>extreme bias (left)</th>\n",
       "      <td>110</td>\n",
       "      <td>0.004519</td>\n",
       "      <td>14</td>\n",
       "      <td>0.127273</td>\n",
       "      <td>0.001940</td>\n",
       "      <td>44</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>11</td>\n",
       "      <td>1.272727</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Nt        Pt    Ns     Ns/Nt        Ps   Nu  \\\n",
       "fake                  1660  0.068189   363  0.218675  0.050291  104   \n",
       "extreme bias (right)  2882  0.118386   246  0.085357  0.034081  123   \n",
       "right                 5273  0.216604  2797  0.530438  0.387503  172   \n",
       "right leaning         2164  0.088893   183  0.084566  0.025353  182   \n",
       "center                4274  0.175567   821  0.192092  0.113743  254   \n",
       "left leaning          5578  0.229132  2400  0.430262  0.332502  244   \n",
       "left                  2403  0.098710   394  0.163962  0.054586  206   \n",
       "extreme bias (left)    110  0.004519    14  0.127273  0.001940   44   \n",
       "\n",
       "                          Nt/Nu  Nsu     Ns/Nsu  \n",
       "fake                  15.961538   34  10.676471  \n",
       "extreme bias (right)  23.430894   47   5.234043  \n",
       "right                 30.656977   70  39.957143  \n",
       "right leaning         11.890110   37   4.945946  \n",
       "center                16.826772   92   8.923913  \n",
       "left leaning          22.860656   72  33.333333  \n",
       "left                  11.665049   60   6.566667  \n",
       "extreme bias (left)    2.500000   11   1.272727  "
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BT = get_big_table(IRA_users)\n",
    "BT.to_csv(\"data/IRA_BT.csv\", float_format=\"%.2f\")\n",
    "BT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24344"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BT.Nt.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "说了这么多left的东西？究竟在说什么？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8d6236307ee74334b687ea66347d827a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "left_tweetids = set()\n",
    "\n",
    "for line in tqdm(open(\"disk/all_IRA_tweets.json\")):\n",
    "    d = json.loads(line.strip())\n",
    "    if d[\"c_mbfc\"] == \"left leaning\":\n",
    "        left_tweetids.add(d[\"tweetid\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "ira_data = pd.read_csv(\"data/ira-tweets-ele.csv\", usecols=[\"tweetid\", \"tweet_text\"], dtype=str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "099f49a819de4f4d894c899b5b46b27e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "cnt = 0\n",
    "w_cnt = Counter()\n",
    "with open(\"data/ira-left-text.txt\", \"w\") as f:\n",
    "    for i, row in tqdm(ira_data.iterrows()):\n",
    "        if row[\"tweetid\"] in left_tweetids and not row[\"tweet_text\"].startswith(\"RT\"):\n",
    "            f.write(row[\"tweet_text\"] + \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nt</th>\n",
       "      <th>Pt</th>\n",
       "      <th>Ns</th>\n",
       "      <th>Ns/Nt</th>\n",
       "      <th>Ps</th>\n",
       "      <th>Nu</th>\n",
       "      <th>Nt/Nu</th>\n",
       "      <th>Nsu</th>\n",
       "      <th>Ns/Nsu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fake</th>\n",
       "      <td>3315543</td>\n",
       "      <td>0.096556</td>\n",
       "      <td>1135577</td>\n",
       "      <td>0.342501</td>\n",
       "      <td>0.108179</td>\n",
       "      <td>214307</td>\n",
       "      <td>15.470997</td>\n",
       "      <td>74713</td>\n",
       "      <td>15.199189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>extreme bias (right)</th>\n",
       "      <td>4459235</td>\n",
       "      <td>0.129863</td>\n",
       "      <td>1270035</td>\n",
       "      <td>0.284810</td>\n",
       "      <td>0.120987</td>\n",
       "      <td>309226</td>\n",
       "      <td>14.420634</td>\n",
       "      <td>106350</td>\n",
       "      <td>11.942031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <td>4553689</td>\n",
       "      <td>0.132614</td>\n",
       "      <td>1230771</td>\n",
       "      <td>0.270280</td>\n",
       "      <td>0.117247</td>\n",
       "      <td>445995</td>\n",
       "      <td>10.210179</td>\n",
       "      <td>149353</td>\n",
       "      <td>8.240685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right leaning</th>\n",
       "      <td>1169747</td>\n",
       "      <td>0.034066</td>\n",
       "      <td>391250</td>\n",
       "      <td>0.334474</td>\n",
       "      <td>0.037272</td>\n",
       "      <td>303690</td>\n",
       "      <td>3.851780</td>\n",
       "      <td>94319</td>\n",
       "      <td>4.148157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>center</th>\n",
       "      <td>6938350</td>\n",
       "      <td>0.202060</td>\n",
       "      <td>2151380</td>\n",
       "      <td>0.310071</td>\n",
       "      <td>0.204947</td>\n",
       "      <td>1116700</td>\n",
       "      <td>6.213262</td>\n",
       "      <td>290324</td>\n",
       "      <td>7.410273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>left leaning</th>\n",
       "      <td>8518070</td>\n",
       "      <td>0.248065</td>\n",
       "      <td>2496675</td>\n",
       "      <td>0.293103</td>\n",
       "      <td>0.237841</td>\n",
       "      <td>1383299</td>\n",
       "      <td>6.157794</td>\n",
       "      <td>425077</td>\n",
       "      <td>5.873465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>left</th>\n",
       "      <td>4738758</td>\n",
       "      <td>0.138003</td>\n",
       "      <td>1638315</td>\n",
       "      <td>0.345727</td>\n",
       "      <td>0.156071</td>\n",
       "      <td>711835</td>\n",
       "      <td>6.657102</td>\n",
       "      <td>244109</td>\n",
       "      <td>6.711408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>extreme bias (left)</th>\n",
       "      <td>644608</td>\n",
       "      <td>0.018772</td>\n",
       "      <td>183248</td>\n",
       "      <td>0.284278</td>\n",
       "      <td>0.017457</td>\n",
       "      <td>103718</td>\n",
       "      <td>6.215006</td>\n",
       "      <td>37662</td>\n",
       "      <td>4.865594</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Nt        Pt       Ns     Ns/Nt        Ps       Nu  \\\n",
       "fake                  3315543  0.096556  1135577  0.342501  0.108179   214307   \n",
       "extreme bias (right)  4459235  0.129863  1270035  0.284810  0.120987   309226   \n",
       "right                 4553689  0.132614  1230771  0.270280  0.117247   445995   \n",
       "right leaning         1169747  0.034066   391250  0.334474  0.037272   303690   \n",
       "center                6938350  0.202060  2151380  0.310071  0.204947  1116700   \n",
       "left leaning          8518070  0.248065  2496675  0.293103  0.237841  1383299   \n",
       "left                  4738758  0.138003  1638315  0.345727  0.156071   711835   \n",
       "extreme bias (left)    644608  0.018772   183248  0.284278  0.017457   103718   \n",
       "\n",
       "                          Nt/Nu     Nsu     Ns/Nsu  \n",
       "fake                  15.470997   74713  15.199189  \n",
       "extreme bias (right)  14.420634  106350  11.942031  \n",
       "right                 10.210179  149353   8.240685  \n",
       "right leaning          3.851780   94319   4.148157  \n",
       "center                 6.213262  290324   7.410273  \n",
       "left leaning           6.157794  425077   5.873465  \n",
       "left                   6.657102  244109   6.711408  \n",
       "extreme bias (left)    6.215006   37662   4.865594  "
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BT2 = get_big_table(all_users)\n",
    "BT2.to_csv(\"data/all_BT.csv\", float_format=\"%.2f\")\n",
    "BT2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_compare(col_name):\n",
    "    ax = plt.figure(figsize=(10, 6))\n",
    "    plt.ylabel(col_name, fontsize=15)\n",
    "    print(BT[col_name])\n",
    "    print(BT2[col_name])\n",
    "    BT[col_name].plot.bar(width=0.3, position=1, color=sns.color_palette(\"coolwarm\", 8)[6], label=\"IRAs\")\n",
    "    BT2[col_name].plot.bar(width=0.3, position=0, color=sns.color_palette(\"coolwarm\", 8)[1], label=\"All users\")\n",
    "    \n",
    "    plt.xlim(-1, len(labels))\n",
    "    \n",
    "#     if col_name == \"Ns/Nt\":\n",
    "#         plt.ylim((0, 0.5))\n",
    "#     if col_name == \"Rs\":\n",
    "#         plt.ylim((0, 0.15))\n",
    "#     if col_name == \"Rf\":\n",
    "#         plt.ylim((0, 0.08))\n",
    "    \n",
    "    plt.xticks(fontsize=14, rotation=45)\n",
    "    plt.legend(fontsize=14)\n",
    "\n",
    "    col_name = col_name.replace(\"/\", \"-\")\n",
    "    plt.savefig(\"fig/{}.pdf\".format(col_name), dpi=300, bbox_inches='tight')\n",
    "    plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fake                    0.068189\n",
      "extreme bias (right)    0.118386\n",
      "right                   0.216604\n",
      "right leaning           0.088893\n",
      "center                  0.175567\n",
      "left leaning            0.229132\n",
      "left                    0.098710\n",
      "extreme bias (left)     0.004519\n",
      "Name: Pt, dtype: float64\n",
      "fake                    0.096556\n",
      "extreme bias (right)    0.129863\n",
      "right                   0.132614\n",
      "right leaning           0.034066\n",
      "center                  0.202060\n",
      "left leaning            0.248065\n",
      "left                    0.138003\n",
      "extreme bias (left)     0.018772\n",
      "Name: Pt, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_compare(\"Pt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fake                    0.050291\n",
      "extreme bias (right)    0.034081\n",
      "right                   0.387503\n",
      "right leaning           0.025353\n",
      "center                  0.113743\n",
      "left leaning            0.332502\n",
      "left                    0.054586\n",
      "extreme bias (left)     0.001940\n",
      "Name: Ps, dtype: float64\n",
      "fake                    0.108179\n",
      "extreme bias (right)    0.120987\n",
      "right                   0.117247\n",
      "right leaning           0.037272\n",
      "center                  0.204947\n",
      "left leaning            0.237841\n",
      "left                    0.156071\n",
      "extreme bias (left)     0.017457\n",
      "Name: Ps, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_compare(\"Ps\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fake                    0.218675\n",
      "extreme bias (right)    0.085357\n",
      "right                   0.530438\n",
      "right leaning           0.084566\n",
      "center                  0.192092\n",
      "left leaning            0.430262\n",
      "left                    0.163962\n",
      "extreme bias (left)     0.127273\n",
      "Name: Ns/Nt, dtype: float64\n",
      "fake                    0.342501\n",
      "extreme bias (right)    0.284810\n",
      "right                   0.270280\n",
      "right leaning           0.334474\n",
      "center                  0.310071\n",
      "left leaning            0.293103\n",
      "left                    0.345727\n",
      "extreme bias (left)     0.284278\n",
      "Name: Ns/Nt, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_compare(\"Ns/Nt\")\n",
    "# (BT[\"Rs\"] / BT2[\"Rs\"]).plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fake                    15.961538\n",
      "extreme bias (right)    23.430894\n",
      "right                   30.656977\n",
      "right leaning           11.890110\n",
      "center                  16.826772\n",
      "left leaning            22.860656\n",
      "left                    11.665049\n",
      "extreme bias (left)      2.500000\n",
      "Name: Nt/Nu, dtype: float64\n",
      "fake                    15.470997\n",
      "extreme bias (right)    14.420634\n",
      "right                   10.210179\n",
      "right leaning            3.851780\n",
      "center                   6.213262\n",
      "left leaning             6.157794\n",
      "left                     6.657102\n",
      "extreme bias (left)      6.215006\n",
      "Name: Nt/Nu, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_compare(\"Nt/Nu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fake                    10.676471\n",
      "extreme bias (right)     5.234043\n",
      "right                   39.957143\n",
      "right leaning            4.945946\n",
      "center                   8.923913\n",
      "left leaning            33.333333\n",
      "left                     6.566667\n",
      "extreme bias (left)      1.272727\n",
      "Name: Ns/Nsu, dtype: float64\n",
      "fake                    15.199189\n",
      "extreme bias (right)    11.942031\n",
      "right                    8.240685\n",
      "right leaning            4.148157\n",
      "center                   7.410273\n",
      "left leaning             5.873465\n",
      "left                     6.711408\n",
      "extreme bias (left)      4.865594\n",
      "Name: Ns/Nsu, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_compare(\"Ns/Nsu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IRAs as sources\n",
    "\n",
    "1. 看看每个用户发布的量\n",
    "2. 找到top sources看看交集\n",
    "\n",
    "结论：IRA从量上来看伪装非常普通的用户！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用户发帖量的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fake\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "right\n",
      "right leaning\n",
      "center\n",
      "left leaning\n",
      "left\n"
     ]
    }
   ],
   "source": [
    "sns.set_style(\"white\")\n",
    "\n",
    "for lab in mbfc_labels:\n",
    "    print(lab)\n",
    "    \n",
    "    plt.figure(figsize=(10, 4))\n",
    "    ax = plt.subplot(121)\n",
    "    _users = all_users[all_users[lab] > 0]\n",
    "    _users[lab].hist(ax=ax, bins=20)\n",
    "    # display(all_users[lab + \"_source\"].describe())\n",
    "    # USER_TABLE[lab] = _users[lab].describe()\n",
    "    plt.yscale(\"log\")\n",
    "#     plt.xscale(\"log\")\n",
    "    \n",
    "    ax = plt.subplot(122)\n",
    "    IRA_users[lab].hist(ax=ax, bins=20)\n",
    "    # IRA_USER_TABLE[lab] = IRA_users[lab].describe()\n",
    "    plt.yscale(\"log\")\n",
    "    \n",
    "    if lab == \"fake\":\n",
    "        plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用户发source tweets的分布"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 从量的角度看IRA的确不高，甚至低于一般的用户"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_IRAs_users = set()\n",
    "\n",
    "for user_id, row in IRA_users.iterrows():\n",
    "    set_IRAs_users.add(user_id)\n",
    "    \n",
    "# print(len(set_IRAs_users))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fake 0\n",
      "right 2\n",
      "right leaning 3\n",
      "center 0\n",
      "left leaning 2\n",
      "left 0\n"
     ]
    }
   ],
   "source": [
    "top_num = 1000\n",
    "\n",
    "for lab in mbfc_labels:\n",
    "    top_sources = set(all_users[lab].sort_values(ascending=False).head(top_num).index)\n",
    "    # print(top_sources)\n",
    "    len_intersection = len(top_sources & set_IRAs_users)\n",
    "    print(lab, len_intersection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fake 1\n",
      "right 1\n",
      "right leaning 1\n",
      "center 0\n",
      "left leaning 3\n",
      "left 0\n"
     ]
    }
   ],
   "source": [
    "top_num = 1000\n",
    "\n",
    "for lab in mbfc_labels:\n",
    "    top_sources = set(all_users[lab + \"_source\"].sort_values(ascending=False).head(top_num).index)\n",
    "    # print(top_sources)\n",
    "    len_intersection = len(top_sources & set_IRAs_users)\n",
    "    print(lab, len_intersection)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IRA as influencers\n",
    "\n",
    "分析IRA是不是影响力比较大的节点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "import graph_tool.all as gt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 可以选择类型，both or out\n",
    "\n",
    "def build_CI_rank(graph_file):\n",
    "    g = gt.load_graph(graph_file)\n",
    "    user_CI = {g.vp.id[v]: g.vp.CI_both[v] for v in g.vertices()}\n",
    "#     user_CI = {g.vp.id[v]: g.vp.CI_out[v] for v in g.vertices()}\n",
    "    st_user_CI = sorted(user_CI.items(), key=lambda d: d[1], reverse=True)\n",
    "    return user_CI, st_user_CI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fake ...\n",
      "参与人数： 250 400972\n",
      "right ...\n",
      "参与人数： 168 415172\n",
      "right leaning ...\n",
      "参与人数： 200 391570\n",
      "center ...\n",
      "参与人数： 180 491068\n",
      "left leaning ...\n",
      "参与人数： 296 1472115\n",
      "left ...\n",
      "参与人数： 210 966042\n"
     ]
    }
   ],
   "source": [
    "top_num = 1000\n",
    "\n",
    "len_intersection = {}\n",
    "dict_CI = {}\n",
    "\n",
    "for label in mbfc_labels:\n",
    "    print(label, \"...\")\n",
    "    user_CI, sort_user_CI = build_CI_rank(\"data/network/network_{}_mbfc.gt\".format(label))    \n",
    "    set_CI_users = set([d[0] for d in sort_user_CI[:top_num]])\n",
    "    set_source_users = set()\n",
    "    \n",
    "    IRA_CI = []\n",
    "    for user_id, row in IRA_users.iterrows():\n",
    "        set_source_users.add(user_id)\n",
    "        try:\n",
    "            IRA_CI.append(user_CI[user_id])\n",
    "        except:\n",
    "            pass\n",
    "    \n",
    "    len_intersection[label] = len(set_CI_users & set_source_users)\n",
    "    print(\"参与人数：\", len(IRA_CI), len(sort_user_CI))\n",
    "    IRA_CI = pd.Series(IRA_CI)\n",
    "    IRA_CI_mean = IRA_CI.mean()\n",
    "    all_CI = pd.Series(list(user_CI.values()))\n",
    "    all_CI_mean = all_CI.mean()\n",
    "    \n",
    "    dict_CI[label] = {\"IRA\": IRA_CI_mean, \"All users\": all_CI_mean,\n",
    "                      \"IRA dist\": IRA_CI, \"All dist\": all_CI}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "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>All users</th>\n",
       "      <th>IRA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fake</th>\n",
       "      <td>3921301</td>\n",
       "      <td>5363345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <td>2414449</td>\n",
       "      <td>2353156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right leaning</th>\n",
       "      <td>260852</td>\n",
       "      <td>166862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>center</th>\n",
       "      <td>250321</td>\n",
       "      <td>908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>left leaning</th>\n",
       "      <td>3016266</td>\n",
       "      <td>214180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>left</th>\n",
       "      <td>1679905</td>\n",
       "      <td>24367</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               All users      IRA\n",
       "fake             3921301  5363345\n",
       "right            2414449  2353156\n",
       "right leaning     260852   166862\n",
       "center            250321      908\n",
       "left leaning     3016266   214180\n",
       "left             1679905    24367"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def plot_corr(cor):\n",
    "    # display(cor)\n",
    "    plt.figure(figsize=(6, 6))\n",
    "    x = range(len(mbfc_labels))\n",
    "    for i in x:\n",
    "        plt.bar(i, cor[mbfc_labels[i]], color=sns.color_palette(\"coolwarm\", 7)[6-i])\n",
    "    plt.xticks(x, mbfc_labels)\n",
    "#     plt.xlabel('Only IRA', fontsize=15)\n",
    "    plt.ylabel('N of IRAs in {} top CIs'.format(top_num))\n",
    "    plt.xticks(rotation=40)\n",
    "#     plt.yticks(fontsize=14)\n",
    "    plt.savefig(\"fig/size-of-intersection{}.pdf\".format(top_num), dpi=300, bbox_inches='tight')\n",
    "    plt.show()\n",
    "    plt.tight_layout()\n",
    "    plt.close()\n",
    "    \n",
    "plot_corr(len_intersection)\n",
    "\n",
    "compare_CI_value = pd.DataFrame(dict_CI_value).transpose()\n",
    "compare_CI_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.figure(figsize=(10, 6))\n",
    "plt.ylabel(\"Average CI score\", fontsize=15)\n",
    "compare_CI_value[\"IRA\"].plot.bar(width=0.3, position=1, \n",
    "                                 color=sns.color_palette(\"coolwarm\", 8)[6], label=\"IRAs\")\n",
    "compare_CI_value[\"All users\"].plot.bar(width=0.3, position=0, \n",
    "                                 color=sns.color_palette(\"coolwarm\", 8)[1], label=\"All users\")\n",
    "plt.xticks(fontsize=13, rotation=45)\n",
    "\n",
    "plt.xlim(-1, 6)\n",
    "plt.legend(fontsize=15)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"fig/CI.pdf\", dpi=300, bbox_inches='tight')\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 针对差异进行显著性检验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fake KS -> Ks_2sampResult(statistic=0.08464367586764165, pvalue=0.05262013632818994)\n",
      "right KS -> Ks_2sampResult(statistic=0.14278104129418767, pvalue=0.0018540681149345718)\n",
      "right leaning KS -> Ks_2sampResult(statistic=0.13331332839594456, pvalue=0.0014427361485919738)\n",
      "center KS -> Ks_2sampResult(statistic=0.16186339434321384, pvalue=0.00013417156448881147)\n",
      "left leaning KS -> Ks_2sampResult(statistic=0.47410253458643026, pvalue=4.685866967921268e-59)\n",
      "left KS -> Ks_2sampResult(statistic=0.5003685139983561, pvalue=6.841542243961308e-47)\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import ks_2samp\n",
    "from scipy.stats import chi2_contingency\n",
    "\n",
    "def get_cdf_pdf(data, mini, maxi, step, norm=True):\n",
    "    x = [float(i) for i in np.arange(mini, maxi, step)]\n",
    "    cdf = [0] * len(x)\n",
    "    pdf = [0] * len(x)\n",
    "    count = 0\n",
    "    for d in data:\n",
    "        d = float(d)\n",
    "        if d < mini or d > maxi:\n",
    "            continue\n",
    "        count += 1\n",
    "        for i in range(len(x)):\n",
    "            if i == (len(x) - 1) and d >= x[i]:\n",
    "                pdf[i] += 1\n",
    "            elif d >= x[i] and d < x[i+1]:\n",
    "                pdf[i] += 1\n",
    "\n",
    "            if d >= x[i]:\n",
    "                cdf[i] += 1\n",
    "\n",
    "    if norm:\n",
    "        cdf = [c / count for c in cdf]\n",
    "        pdf = [p / count for p in pdf]\n",
    "\n",
    "    return x, cdf, pdf\n",
    "\n",
    "\n",
    "for label in mbfc_labels:\n",
    "    d = dict_CI[label]\n",
    "    print(label, 'KS ->', ks_2samp(d[\"IRA dist\"], d[\"All dist\"]))\n",
    "    # print(d[\"IRA dist\"].describe())    \n",
    "    # print(d[\"All dist\"].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Who is collective influencers?\n",
    "\n",
    "TOP 100 CIs!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "db_user_name = json.load(open(\"data/user_name.txt\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = gt.load_graph(\"data/network/network_fake.gt\")\n",
    "map_user_id = {i: g.vp.id[v] for i, v in enumerate(g.vertices())}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_user_list = {}\n",
    "\n",
    "# both\n",
    "user_CI = {g.vp.id[v]: g.vp.CI_both[v] for v in g.vertices()}\n",
    "st_user_CI = sorted(user_CI.items(), key=lambda d: d[1], reverse=True)\n",
    "\n",
    "cnt = 0\n",
    "top_users = []\n",
    "for user_id, v in st_user_CI:\n",
    "    cnt += 1\n",
    "    # print(map_user_id[v], g.vp.CI_both[v])\n",
    "    # print(user_id, db_user_name[user_id], v)\n",
    "    # print(db_user_name[user_id], v)\n",
    "    \n",
    "    top_users.append(db_user_name[user_id])\n",
    "    if cnt >= 100:\n",
    "        break\n",
    "\n",
    "top_user_list[\"Top CI(both)\"] = top_users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# out\n",
    "user_CI = {g.vp.id[v]: g.vp.CI_out[v] for v in g.vertices()}\n",
    "st_user_CI = sorted(user_CI.items(), key=lambda d: d[1], reverse=True)\n",
    "\n",
    "cnt = 0\n",
    "top_users = []\n",
    "for user_id, v in st_user_CI:\n",
    "    cnt += 1\n",
    "    # print(db_user_name[user_id], v)\n",
    "    # print(user_id, v)    \n",
    "    top_users.append(db_user_name[user_id])\n",
    "    if cnt >= 100:\n",
    "        break\n",
    "        \n",
    "top_user_list[\"Top CI(out)\"] = top_users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = pd.read_csv(\"data/CI_dyn.txt\", dtype=str)\n",
    "d = d.astype({\"source\": int, \"CI_dynamics\": int})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "d[\"ave_CI_dyn\"] = d[\"CI_dynamics\"] / d[\"source\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "825501626 VivaLaAmes\n",
      "18643437 PrisonPlanet\n",
      "109065990 RealAlexJones\n",
      "19211550 gatewaypundit\n",
      "17980523 mitchellvii\n",
      "4855687055 Lagartija_Nix\n",
      "707278892801765377 TruthFeedNews\n",
      "18856867 zerohedge\n",
      "2242085304 RickRWells\n",
      "14669951 DRUDGE_REPORT\n",
      "4831603043 TPoliticmanager\n",
      "3022045469 ThePatriot143\n",
      "3914047581 Italians4Trump\n",
      "14505245 infowars\n",
      "25073877 realDonaldTrump\n",
      "704034081898668032 Miami4Trump\n",
      "1575689569 BigStick2013\n",
      "39344374 DonaldJTrumpJr\n",
      "4510755552 _Makada_\n",
      "2728672957 KitDaniels1776\n",
      "1564676498 surfermom77\n",
      "19173608 JayS2629\n",
      "3270559782 MONAKatOILS\n",
      "567626521 jko417\n",
      "122455671 TeaPartyOrg\n",
      "2217181338 conserv_tribune\n",
      "4717892303 V_of_Europe\n",
      "1494835716 AIIAmericanGirI\n",
      "27404671 SavageNation\n",
      "358545917 Cernovich\n",
      "4786763677 MOVEFORWARDHUGE\n",
      "975217231 JrcheneyJohn\n",
      "53297881 redflagnews\n",
      "2845849081 ARnews1936\n",
      "999453985 BlissTabitha\n",
      "4165642155 ConstanceQueen8\n",
      "4584443975 TRUMP_PREZ\n",
      "72882689 SonofLiberty357\n",
      "2417844248 _Proud_American\n",
      "788571666 EjHirschberger\n",
      "2164876369 RepStevenSmith\n",
      "16589206 wikileaks\n",
      "779739206339928064 WDFx2EU7\n",
      "18208120 trscoop\n",
      "3419210573 FemalesForTrump\n",
      "1464560094 ed_hooley\n",
      "60893069 gsarnoldjr\n",
      "17561562 slone\n",
      "746875038901633024 TrumpNewss\n",
      "2363057095 TPInsidr\n",
      "761464464642375680 PolitiCompass\n",
      "4496549793 Tanya_USA\n",
      "3309169489 TheDonaldNews\n",
      "32497478 KLSouth\n",
      "272114651 SpecialKMB1969\n",
      "710990834972233728 Always_Trump\n",
      "542962969 hrkbenowen\n",
      "212708176 TheRightScoop\n",
      "431917957 magnifier661\n",
      "3677742792 immigrant4trump\n",
      "379767285 reg1776\n",
      "3274720530 farrightgregy\n",
      "2753220014 halsteadg048\n",
      "38284349 WayneRoot\n",
      "33611715 PatVPeters\n",
      "4691189473 dixiefortrump\n",
      "366819502 FredZeppelin12\n",
      "313038011 StefanMolyneux\n",
      "1075296265 NewsTodayUS\n",
      "16206579 GigiTracyXO\n",
      "4289460455 asamjulian\n",
      "393190233 LeahR77\n",
      "16710420 irritatedwoman\n",
      "21587082 10thAmendment\n",
      "592730371 JackPosobiec\n",
      "758652815996776448 Vnzla4Trump\n",
      "759791725011427328 VioletaCarieles\n",
      "3873587412 TrumpMyPres\n",
      "16190478 HouseCracka\n",
      "49452556 DarkStream\n",
      "32707036 MikaelThalen\n",
      "54769979 TIMENOUT\n",
      "759317433345769472 Girls4urtrump\n",
      "954124423 AmyMek\n",
      "133371829 kinni00\n",
      "347627434 LindaSuhler\n",
      "3995778614 KamVTV\n",
      "3481965513 eeynouf\n",
      "26487169 LouDobbs\n",
      "750517084166193156 saratrump1\n",
      "3293118434 Veteran4Trump\n",
      "3301187533 CodeAud\n",
      "129235890 Lrihendry\n",
      "764662496179421185 Theypolitician\n",
      "765648314159210496 Politician_News\n",
      "4826510725 US_Army_Vet\n",
      "763518863212511232 trumpamericann\n",
      "409430647 nathanielbumpo\n",
      "47253057 RogerJStoneJr\n",
      "2735511486 JoeFreedomLove\n"
     ]
    }
   ],
   "source": [
    "uids = d.sort_values(\"CI_dynamics\", ascending=False).user_id\n",
    "top_users = []\n",
    "\n",
    "for user_id in uids:\n",
    "    top_users.append(db_user_name[user_id])\n",
    "    print(user_id, db_user_name[user_id])\n",
    "    if len(top_users) >= 100:\n",
    "        break\n",
    "        \n",
    "top_user_list[\"Top CI dynamics\"] = top_users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "uids = d.sort_values(\"ave_CI_dyn\", ascending=False).user_id\n",
    "top_users = []\n",
    "\n",
    "for user_id in uids:\n",
    "    top_users.append(db_user_name[user_id])\n",
    "    if len(top_users) >= 100:\n",
    "        break\n",
    "        \n",
    "top_user_list[\"Top average CI dynamics\"] = top_users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = pd.DataFrame(top_user_list)\n",
    "d.index += 1\n",
    "d.to_csv(\"data/top_users.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23dde95f6135ab333fab997ea397603ddf208d1f91a43846f0a162581949fa0c\n",
      "974ffda4a098faf4999ede2034acb8a463ea8fa9dc2f25ed9e90079868c64c14\n",
      "cf5fc79c235a6c66c28eb92e0c581de346e0df0ba1dbde697de42040b207deb7\n"
     ]
    }
   ],
   "source": [
    "\n",
    "for user_id in uids:\n",
    "    if len(user_id) == 64:\n",
    "        print(user_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IRAs as supporters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "sns.set_style(\"white\")\n",
    "sns.color_palette(\"RdBu\", n_colors=7)\n",
    "\n",
    "count = Counter(first_opinion)\n",
    "print(count)\n",
    "\n",
    "label = [\"pro-hillary\", \"pro-trump\"]\n",
    "plt.figure(figsize=(20, 6))\n",
    "\n",
    "plt.subplot(131)\n",
    "plt.bar(1, count[0], color=sns.color_palette(\"RdBu\")[5], width=0.2)\n",
    "plt.bar(1.5, count[1], color=sns.color_palette(\"RdBu\")[0], width=0.2)\n",
    "label = [\"pro-hillary\\n{0:.0%}\".format(count[0] / (count[0]+count[1])), \"pro-trump\\n{0:.0%}\".format(count[1] / (count[0]+count[1]))]\n",
    "plt.xticks([1, 1.5], label)\n",
    "plt.xlabel('First source', fontsize=15)\n",
    "plt.ylabel('Number of tweets', fontsize=15)\n",
    "plt.xticks(fontsize=14); plt.yticks(fontsize=14)\n",
    "\n",
    "count = Counter(staters_opinion)\n",
    "print(count)\n",
    "\n",
    "plt.subplot(132)\n",
    "plt.bar(1, count[0], color=sns.color_palette(\"RdBu\")[5], width=0.2)\n",
    "plt.bar(1.5, count[1], color=sns.color_palette(\"RdBu\")[0], width=0.2)\n",
    "label = [\"pro-hillary\\n{0:.0%}\".format(count[0] / (count[0]+count[1])), \"pro-trump\\n{0:.0%}\".format(count[1] / (count[0]+count[1]))]\n",
    "plt.xticks([1, 1.5], label)\n",
    "plt.xlabel('source', fontsize=15)\n",
    "plt.xticks(fontsize=14); plt.yticks(fontsize=14)\n",
    "\n",
    "count = Counter(other_opinion)\n",
    "print(count)\n",
    "\n",
    "plt.subplot(133)\n",
    "plt.bar(1, count[0], color=sns.color_palette(\"RdBu\")[5], width=0.2)\n",
    "plt.bar(1.5, count[1], color=sns.color_palette(\"RdBu\")[0], width=0.2)\n",
    "label = [\"pro-hillary\\n{0:.0%}\".format(count[0] / (count[0]+count[1])), \"pro-trump\\n{0:.0%}\".format(count[1] / (count[0]+count[1]))]\n",
    "plt.xticks([1, 1.5], label)\n",
    "plt.xlabel('non-source', fontsize=15)\n",
    "# plt.ylabel('N of fake sources', fontsize=15)\n",
    "plt.xticks(fontsize=14); plt.yticks(fontsize=14)\n",
    "\n",
    "plt.savefig(\"fig/support.pdf\", dpi=300, bbox_inches='tight')\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Making URL "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "URL_counts = all_tweets.URL.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'URL_counts' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-32-ea66e0e41f5b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mset_URL\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mURL\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mURL_counts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m     \u001b[0;31m# print(URL)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mURL\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'URL_counts' is not defined"
     ]
    }
   ],
   "source": [
    "# cnt = 0\n",
    "\n",
    "set_URL = set()\n",
    "\n",
    "for URL in URL_counts.index:\n",
    "    # print(URL)\n",
    "    if URL.endswith(\"/\"):\n",
    "        URL = URL[:-1]\n",
    "    if URL in set_URL:\n",
    "        print(URL)\n",
    "    set_URL.add(URL)\n",
    "        \n",
    "print(len(set_URL))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2524061"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(URL_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 网络分析\n",
    "\n",
    "分析每种媒体网络中IRA的节点数量，相关边的数量，平均度，最大度等。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fake_identify import Are_you_IRA\n",
    "\n",
    "putin = Are_you_IRA()\n",
    "\n",
    "\n",
    "def get_G_char(g_file):\n",
    "    print(g_file, \"...\")\n",
    "    G = nx.read_gpickle(g_file)\n",
    "    \n",
    "    n_nodes = G.number_of_nodes()\n",
    "    n_edges = G.number_of_edges()\n",
    "    k = G.number_of_edges() / G.number_of_nodes()\n",
    "\n",
    "    out_d = pd.Series([k[1] for k in G.out_degree()])\n",
    "    ave_out_d = out_d.mean()\n",
    "    std_out_d = out_d.std() / k\n",
    "    max_out_d = max(out_d)\n",
    "\n",
    "    in_d = pd.Series([k[1] for k in G.in_degree()])\n",
    "    ave_in_d = in_d.mean()\n",
    "    std_in_d = in_d.std() / k\n",
    "    max_in_d = max(in_d)\n",
    "\n",
    "    rst = {\n",
    "        \"N nodes\": n_nodes,\n",
    "        \"N edges\": n_edges,\n",
    "        \"<k>\": k,\n",
    "        \"std(k_out) / <k>\": ave_out_d,\n",
    "        \"std(k_in) / <k>\": ave_in_d,\n",
    "        \"max(k_out)\": max_out_d,\n",
    "        \"max(k_in)\": max_in_d,\n",
    "    }        \n",
    "    \n",
    "    return rst\n",
    "\n",
    "\n",
    "def get_G_char_IRA(g_file):\n",
    "    print(g_file, \"...\")\n",
    "    G = nx.read_gpickle(g_file)\n",
    "    \n",
    "    nodes = [uid for uid in putin.IRA_user_set if G.has_node(uid)]\n",
    "    \n",
    "    n_nodes = len(nodes)\n",
    "    out_d = pd.Series([G.out_degree(n) for n in nodes])\n",
    "    ave_k_out = out_d.sum() / n_nodes\n",
    "    std_k_out = out_d.std() / ave_k_out\n",
    "    max_out_d = max(out_d)\n",
    "\n",
    "    in_d = pd.Series([G.in_degree(n) for n in nodes])\n",
    "    ave_k_in = in_d.sum() / n_nodes\n",
    "    std_k_in = in_d.std() / ave_k_in\n",
    "    max_in_d = max(in_d)\n",
    "    \n",
    "    rst = {\n",
    "        \"N nodes\": n_nodes,\n",
    "        \"<k_out>\": ave_k_out,\n",
    "        \"<k_in>\": ave_k_in,\n",
    "        \"std(k_out) / <k_out>\": std_k_out,\n",
    "        \"std(k_in) / <k_in>\": std_k_in,\n",
    "        \"max(k_out)\": max_out_d,\n",
    "        \"max(k_in)\": max_in_d,\n",
    "    } \n",
    "    return rst\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "disk/network_fake.gpickle ...\n",
      "disk/network_extreme bias (right).gpickle ...\n",
      "disk/network_right.gpickle ...\n",
      "disk/network_right leaning.gpickle ...\n",
      "disk/network_center.gpickle ...\n",
      "disk/network_left leaning.gpickle ...\n",
      "disk/network_left.gpickle ...\n",
      "disk/network_extreme bias (left).gpickle ...\n"
     ]
    }
   ],
   "source": [
    "networks_char = {}\n",
    "\n",
    "for i in range(len(map_labels)):\n",
    "    category = map_labels[str(i)]\n",
    "    rst = get_G_char(\"disk/network_{}.gpickle\".format(category))\n",
    "    networks_char[category] = rst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>N nodes</th>\n",
       "      <th>N edges</th>\n",
       "      <th>&lt;k&gt;</th>\n",
       "      <th>std(k_out) / &lt;k&gt;</th>\n",
       "      <th>std(k_in) / &lt;k&gt;</th>\n",
       "      <th>max(k_out)</th>\n",
       "      <th>max(k_in)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fake</th>\n",
       "      <td>187581.0</td>\n",
       "      <td>1328109.0</td>\n",
       "      <td>7.080189</td>\n",
       "      <td>32.504332</td>\n",
       "      <td>2.519126</td>\n",
       "      <td>45305.0</td>\n",
       "      <td>1234.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>extreme bias (right)</th>\n",
       "      <td>268065.0</td>\n",
       "      <td>1904334.0</td>\n",
       "      <td>7.104001</td>\n",
       "      <td>38.048120</td>\n",
       "      <td>2.780742</td>\n",
       "      <td>60622.0</td>\n",
       "      <td>642.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <td>381854.0</td>\n",
       "      <td>2130544.0</td>\n",
       "      <td>5.579473</td>\n",
       "      <td>48.089533</td>\n",
       "      <td>2.772111</td>\n",
       "      <td>87216.0</td>\n",
       "      <td>546.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right leaning</th>\n",
       "      <td>254212.0</td>\n",
       "      <td>631148.0</td>\n",
       "      <td>2.482762</td>\n",
       "      <td>50.028980</td>\n",
       "      <td>1.821710</td>\n",
       "      <td>38981.0</td>\n",
       "      <td>147.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>center</th>\n",
       "      <td>975312.0</td>\n",
       "      <td>2960757.0</td>\n",
       "      <td>3.035702</td>\n",
       "      <td>122.764473</td>\n",
       "      <td>2.786870</td>\n",
       "      <td>257559.0</td>\n",
       "      <td>551.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>left leaning</th>\n",
       "      <td>1183403.0</td>\n",
       "      <td>4284667.0</td>\n",
       "      <td>3.620632</td>\n",
       "      <td>74.030897</td>\n",
       "      <td>3.498212</td>\n",
       "      <td>172026.0</td>\n",
       "      <td>913.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>left</th>\n",
       "      <td>587553.0</td>\n",
       "      <td>2077901.0</td>\n",
       "      <td>3.536534</td>\n",
       "      <td>50.410517</td>\n",
       "      <td>3.593570</td>\n",
       "      <td>59749.0</td>\n",
       "      <td>797.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>extreme bias (left)</th>\n",
       "      <td>83669.0</td>\n",
       "      <td>299408.0</td>\n",
       "      <td>3.578482</td>\n",
       "      <td>33.826937</td>\n",
       "      <td>2.517140</td>\n",
       "      <td>23208.0</td>\n",
       "      <td>659.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        N nodes    N edges       <k>  std(k_out) / <k>  \\\n",
       "fake                   187581.0  1328109.0  7.080189         32.504332   \n",
       "extreme bias (right)   268065.0  1904334.0  7.104001         38.048120   \n",
       "right                  381854.0  2130544.0  5.579473         48.089533   \n",
       "right leaning          254212.0   631148.0  2.482762         50.028980   \n",
       "center                 975312.0  2960757.0  3.035702        122.764473   \n",
       "left leaning          1183403.0  4284667.0  3.620632         74.030897   \n",
       "left                   587553.0  2077901.0  3.536534         50.410517   \n",
       "extreme bias (left)     83669.0   299408.0  3.578482         33.826937   \n",
       "\n",
       "                      std(k_in) / <k>  max(k_out)  max(k_in)  \n",
       "fake                         2.519126     45305.0     1234.0  \n",
       "extreme bias (right)         2.780742     60622.0      642.0  \n",
       "right                        2.772111     87216.0      546.0  \n",
       "right leaning                1.821710     38981.0      147.0  \n",
       "center                       2.786870    257559.0      551.0  \n",
       "left leaning                 3.498212    172026.0      913.0  \n",
       "left                         3.593570     59749.0      797.0  \n",
       "extreme bias (left)          2.517140     23208.0      659.0  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "networks_char = pd.DataFrame(networks_char).transpose()\n",
    "networks_char = networks_char[[\"N nodes\", \"N edges\", \"<k>\", \"std(k_out) / <k>\", \"std(k_in) / <k>\", \"max(k_out)\", \"max(k_in)\"]]\n",
    "\n",
    "networks_char.to_csv(\"data/table_all_network.csv\", float_format=\"%.1f\")\n",
    "networks_char"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "disk/network_fake.gpickle ...\n",
      "disk/network_extreme bias (right).gpickle ...\n",
      "disk/network_right.gpickle ...\n",
      "disk/network_right leaning.gpickle ...\n",
      "disk/network_center.gpickle ...\n",
      "disk/network_left leaning.gpickle ...\n",
      "disk/network_left.gpickle ...\n",
      "disk/network_extreme bias (left).gpickle ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alex/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:44: RuntimeWarning: invalid value encountered in double_scalars\n"
     ]
    }
   ],
   "source": [
    "networks_char = {}\n",
    "\n",
    "for i in range(len(map_labels)):\n",
    "    category = map_labels[str(i)]\n",
    "    rst = get_G_char_IRA(\"disk/network_{}.gpickle\".format(category))\n",
    "    networks_char[category] = rst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N nodes</th>\n",
       "      <th>&lt;k_out&gt;</th>\n",
       "      <th>&lt;k_in&gt;</th>\n",
       "      <th>std(k_out) / &lt;k_out&gt;</th>\n",
       "      <th>std(k_in) / &lt;k_in&gt;</th>\n",
       "      <th>max(k_out)</th>\n",
       "      <th>max(k_in)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fake</th>\n",
       "      <td>69.0</td>\n",
       "      <td>27.811594</td>\n",
       "      <td>9.884058</td>\n",
       "      <td>2.862679</td>\n",
       "      <td>1.826853</td>\n",
       "      <td>379.0</td>\n",
       "      <td>114.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>extreme bias (right)</th>\n",
       "      <td>98.0</td>\n",
       "      <td>20.551020</td>\n",
       "      <td>10.255102</td>\n",
       "      <td>3.867525</td>\n",
       "      <td>2.184276</td>\n",
       "      <td>558.0</td>\n",
       "      <td>149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <td>97.0</td>\n",
       "      <td>28.030928</td>\n",
       "      <td>7.701031</td>\n",
       "      <td>4.151999</td>\n",
       "      <td>2.127529</td>\n",
       "      <td>752.0</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right leaning</th>\n",
       "      <td>83.0</td>\n",
       "      <td>5.072289</td>\n",
       "      <td>1.855422</td>\n",
       "      <td>3.661605</td>\n",
       "      <td>1.554630</td>\n",
       "      <td>107.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>center</th>\n",
       "      <td>170.0</td>\n",
       "      <td>4.482353</td>\n",
       "      <td>3.776471</td>\n",
       "      <td>6.512995</td>\n",
       "      <td>1.798196</td>\n",
       "      <td>310.0</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>left leaning</th>\n",
       "      <td>168.0</td>\n",
       "      <td>7.559524</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.500030</td>\n",
       "      <td>1.393359</td>\n",
       "      <td>270.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>left</th>\n",
       "      <td>141.0</td>\n",
       "      <td>3.198582</td>\n",
       "      <td>3.978723</td>\n",
       "      <td>5.901482</td>\n",
       "      <td>1.033877</td>\n",
       "      <td>153.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>extreme bias (left)</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.885714</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.850932</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      N nodes    <k_out>     <k_in>  std(k_out) / <k_out>  \\\n",
       "fake                     69.0  27.811594   9.884058              2.862679   \n",
       "extreme bias (right)     98.0  20.551020  10.255102              3.867525   \n",
       "right                    97.0  28.030928   7.701031              4.151999   \n",
       "right leaning            83.0   5.072289   1.855422              3.661605   \n",
       "center                  170.0   4.482353   3.776471              6.512995   \n",
       "left leaning            168.0   7.559524   3.000000              4.500030   \n",
       "left                    141.0   3.198582   3.978723              5.901482   \n",
       "extreme bias (left)      35.0   0.000000   1.885714                   NaN   \n",
       "\n",
       "                      std(k_in) / <k_in>  max(k_out)  max(k_in)  \n",
       "fake                            1.826853       379.0      114.0  \n",
       "extreme bias (right)            2.184276       558.0      149.0  \n",
       "right                           2.127529       752.0      118.0  \n",
       "right leaning                   1.554630       107.0       24.0  \n",
       "center                          1.798196       310.0       71.0  \n",
       "left leaning                    1.393359       270.0       44.0  \n",
       "left                            1.033877       153.0       33.0  \n",
       "extreme bias (left)             0.850932         0.0        8.0  "
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "networks_char = pd.DataFrame(networks_char).transpose()\n",
    "networks_char = networks_char[[\"N nodes\", \"<k_out>\", \"<k_in>\", \"std(k_out) / <k_out>\", \"std(k_in) / <k_in>\", \"max(k_out)\", \"max(k_in)\"]]\n",
    "networks_char\n",
    "\n",
    "networks_char.to_csv(\"data/table_IRA_network.csv\", float_format=\"%.1f\")\n",
    "networks_char"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分析IRA的outlets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>URL</th>\n",
       "      <th>dt</th>\n",
       "      <th>hostname</th>\n",
       "      <th>is_IRA</th>\n",
       "      <th>is_first</th>\n",
       "      <th>is_source</th>\n",
       "      <th>media_type</th>\n",
       "      <th>retweeted_id</th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>http://www.newsweek.com/vladimir-putin-sidney-...</td>\n",
       "      <td>2016-10-10 18:49:57</td>\n",
       "      <td>newsweek.com</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>785628468746727424</td>\n",
       "      <td>215207998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>http://www.newsweek.com/vladimir-putin-sidney-...</td>\n",
       "      <td>2016-10-10 18:50:09</td>\n",
       "      <td>newsweek.com</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>785628468746727424</td>\n",
       "      <td>785628515991302144</td>\n",
       "      <td>20634939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>http://www.newsweek.com/vladimir-putin-sidney-...</td>\n",
       "      <td>2016-10-10 18:50:10</td>\n",
       "      <td>newsweek.com</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>785628468746727424</td>\n",
       "      <td>785628519703326722</td>\n",
       "      <td>26378513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>http://www.newsweek.com/vladimir-putin-sidney-...</td>\n",
       "      <td>2016-10-10 18:50:16</td>\n",
       "      <td>newsweek.com</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>785628468746727424</td>\n",
       "      <td>785628546114859008</td>\n",
       "      <td>445966346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>http://www.newsweek.com/vladimir-putin-sidney-...</td>\n",
       "      <td>2016-10-10 18:50:23</td>\n",
       "      <td>newsweek.com</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>785628468746727424</td>\n",
       "      <td>785628574388621312</td>\n",
       "      <td>754104364164345860</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 URL                   dt  \\\n",
       "0  http://www.newsweek.com/vladimir-putin-sidney-...  2016-10-10 18:49:57   \n",
       "1  http://www.newsweek.com/vladimir-putin-sidney-...  2016-10-10 18:50:09   \n",
       "2  http://www.newsweek.com/vladimir-putin-sidney-...  2016-10-10 18:50:10   \n",
       "3  http://www.newsweek.com/vladimir-putin-sidney-...  2016-10-10 18:50:16   \n",
       "4  http://www.newsweek.com/vladimir-putin-sidney-...  2016-10-10 18:50:23   \n",
       "\n",
       "       hostname  is_IRA  is_first  is_source media_type        retweeted_id  \\\n",
       "0  newsweek.com       0         1          1          5                   0   \n",
       "1  newsweek.com       0         0          0          5  785628468746727424   \n",
       "2  newsweek.com       0         0          0          5  785628468746727424   \n",
       "3  newsweek.com       0         0          0          5  785628468746727424   \n",
       "4  newsweek.com       0         0          0          5  785628468746727424   \n",
       "\n",
       "             tweet_id             user_id  \n",
       "0  785628468746727424           215207998  \n",
       "1  785628515991302144            20634939  \n",
       "2  785628519703326722            26378513  \n",
       "3  785628546114859008           445966346  \n",
       "4  785628574388621312  754104364164345860  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_tweets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "======== fake ========\n",
      "truthfeed.com              528\n",
      "thegatewaypundit.com       129\n",
      "therightscoop.com          113\n",
      "conservativetribune.com    102\n",
      "infowars.com                55\n",
      "rickwells.us                42\n",
      "zerohedge.com               29\n",
      "therealstrategy.com         27\n",
      "thepoliticalinsider.com     27\n",
      "departed.co                 26\n",
      "thefederalistpapers.org     25\n",
      "thetruthdivision.com        18\n",
      "clashdaily.com              16\n",
      "usapoliticsnow.com          15\n",
      "redflagnews.com             14\n",
      "teaparty.org                 9\n",
      "thehill.com                  1\n",
      "Name: hostname, dtype: int64\n",
      "\n",
      "======== extreme bias (right) ========\n",
      "dailycaller.com          543\n",
      "breitbart.com            356\n",
      "freebeacon.com           141\n",
      "americanthinker.com       92\n",
      "newsninja2012.com         77\n",
      "pjmedia.com               69\n",
      "truepundit.com            62\n",
      "100percentfedup.com       37\n",
      "endingthefed.com          32\n",
      "wnd.com                   19\n",
      "westernjournalism.com     19\n",
      "hannity.com               18\n",
      "ilovemyfreedom.org        17\n",
      "newsbusters.org           13\n",
      "dailywire.com             11\n",
      "newsmax.com               10\n",
      "weaselzippers.us           7\n",
      "shareblue.com              2\n",
      "Name: hostname, dtype: int64\n",
      "\n",
      "======== right ========\n",
      "townhall.com                    254\n",
      "nypost.com                      238\n",
      "foxnews.com                     150\n",
      "dailymail.co.uk                 110\n",
      "theconservativetreehouse.com     60\n",
      "hotair.com                       58\n",
      "thefederalist.com                55\n",
      "ijr.com                          53\n",
      "washingtonexaminer.com           50\n",
      "investors.com                    31\n",
      "bizpacreview.com                 27\n",
      "lifezette.com                    27\n",
      "theamericanmirror.com            21\n",
      "redstate.com                     18\n",
      "nationalreview.com               18\n",
      "conservativereview.com           14\n",
      "allenbwest.com                   11\n",
      "theblaze.com                     10\n",
      "weeklystandard.com                8\n",
      "judicialwatch.org                 6\n",
      "Name: hostname, dtype: int64\n",
      "\n",
      "======== right leaning ========\n",
      "rt.com                   87\n",
      "washingtontimes.com      37\n",
      "wsj.com                  33\n",
      "fortune.com              18\n",
      "realclearpolitics.com    14\n",
      "telegraph.co.uk          11\n",
      "forbes.com                6\n",
      "Name: hostname, dtype: int64\n",
      "\n",
      "======== center ========\n",
      "thehill.com            1144\n",
      "politico.com            112\n",
      "cnn.com                 112\n",
      "observer.com             68\n",
      "usatoday.com             48\n",
      "bloomberg.com            39\n",
      "fivethirtyeight.com      30\n",
      "apnews.com               21\n",
      "reuters.com              18\n",
      "businessinsider.com      14\n",
      "bbc.co.uk                 7\n",
      "ibtimes.com               4\n",
      "bbc.com                   2\n",
      "Name: hostname, dtype: int64\n",
      "\n",
      "======== left leaning ========\n",
      "washingtonpost.com       268\n",
      "abcnews.go.com           186\n",
      "nytimes.com               63\n",
      "slate.com                 55\n",
      "theguardian.com           32\n",
      "nydailynews.com           28\n",
      "latimes.com               25\n",
      "buzzfeed.com              22\n",
      "nbcnews.com               21\n",
      "cbsnews.com               18\n",
      "theatlantic.com           17\n",
      "vox.com                   14\n",
      "mediaite.com              11\n",
      "newsweek.com               8\n",
      "politifact.com             7\n",
      "hollywoodreporter.com      6\n",
      "npr.org                    5\n",
      "independent.co.uk          4\n",
      "cnb.cx                     4\n",
      "Name: hostname, dtype: int64\n",
      "\n",
      "======== left ========\n",
      "theintercept.com         140\n",
      "thinkprogress.org         88\n",
      "time.com                  75\n",
      "huffingtonpost.com        74\n",
      "mashable.com              69\n",
      "rawstory.com              59\n",
      "thedailybeast.com         59\n",
      "talkingpointsmemo.com     22\n",
      "people.com                21\n",
      "newyorker.com             20\n",
      "salon.com                 19\n",
      "motherjones.com           18\n",
      "nymag.com                 17\n",
      "politicususa.com          10\n",
      "mediamatters.org          10\n",
      "dailykos.com               7\n",
      "thenation.com              2\n",
      "msnbc.com                  2\n",
      "Name: hostname, dtype: int64\n",
      "\n",
      "======== extreme bias (left) ========\n",
      "usuncut.com             29\n",
      "occupydemocrats.com     17\n",
      "bipartisanreport.com    16\n",
      "crooksandliars.com      13\n",
      "shareblue.com           12\n",
      "dailynewsbin.com         6\n",
      "bluenationreview.com     1\n",
      "Name: hostname, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "ira_tweets = all_tweets[all_tweets.is_IRA==1]\n",
    "\n",
    "for i in range(len(map_labels)):\n",
    "    lab = map_labels[str(i)]\n",
    "    print(\"\\n======== {} ========\".format(lab))\n",
    "    _tweets = ira_tweets[ira_tweets.media_type==str(i)]\n",
    "    print(_tweets[\"hostname\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "63ccb37a90e9419a9774ad10d9f8ef2f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "www.newsweek.com newsweek.com\n",
      "101\n"
     ]
    }
   ],
   "source": [
    "from urllib.parse import urlparse\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "\n",
    "cnt = 0\n",
    "for i, tweet in tqdm(all_tweets.iterrows()):\n",
    "    if urlparse(tweet[\"URL\"]) != tweet[\"hostname\"]:\n",
    "        cnt += 1\n",
    "        print(urlparse(tweet[\"URL\"]).hostname, tweet[\"hostname\"])\n",
    "        if cnt > 100:\n",
    "            break\n",
    "        \n",
    "print(cnt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>URL</th>\n",
       "      <th>dt</th>\n",
       "      <th>hostname</th>\n",
       "      <th>is_IRA</th>\n",
       "      <th>is_first</th>\n",
       "      <th>is_source</th>\n",
       "      <th>media_type</th>\n",
       "      <th>retweeted_id</th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10423703</th>\n",
       "      <td>http://www.thegatewaypundit.com/2016/09/breaki...</td>\n",
       "      <td>2016-09-11 15:05:10</td>\n",
       "      <td>thegatewaypundit.com</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>775062649822253056</td>\n",
       "      <td>4826510725</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                        URL  \\\n",
       "10423703  http://www.thegatewaypundit.com/2016/09/breaki...   \n",
       "\n",
       "                           dt              hostname  is_IRA  is_first  \\\n",
       "10423703  2016-09-11 15:05:10  thegatewaypundit.com       0         0   \n",
       "\n",
       "          is_source media_type retweeted_id            tweet_id     user_id  \n",
       "10423703          1          0            0  775062649822253056  4826510725  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_tweets[all_tweets.tweet_id==\"775062649822253056\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hostname = urlparse(url).hostname"
   ]
  }
 ],
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