ferguson-activists.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ferguson Activists\n",
    "\n",
    "These are some stats that Mosi Secret requested of our Ferguson related datasets. First we need to read in the results of our previous filtering of the tweet datasets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas\n",
    "df = pandas.read_csv('results.csv', parse_dates=['parsed_created_at'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Name Mentions\n",
    "\n",
    "Plotting name mentions and user mentions (combined) against time - over the entire timeframes of the datasets - looking at how the number of mentions changed over time? The idea here would be to characterize the volume of the conversation about the victims over time. When did it surge over the course of the movement, what happened when they died, when did mentions fall off, did it ever tick back up again?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11ce95dd0>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mentions = df.set_index('parsed_created_at')\n",
    "mentions_by_day = mentions['id'].resample('D').count()\n",
    "mentions_by_day.name = 'count'\n",
    "mentions_by_day.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Darren Seals\n",
    "\n",
    "Ranking the users who most mentioned Darren Seals in the week after Seals died on Sept. 6, 2016.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Tweets:  1830\n",
      "\n",
      "Top 25 Users:\n",
      "user_screen_name\n",
      "HotNostrilsrFun    24\n",
      "StreetTeamAC       19\n",
      "_Arcy__            19\n",
      "Taobender          10\n",
      "TRANSLATINAS_NY    10\n",
      "MissionaryBLMM      8\n",
      "courtneyjahenry     8\n",
      "Romonaga_           8\n",
      "GraciasDarling      7\n",
      "BestFrenz           7\n",
      "VibeHi              7\n",
      "l4wstud3nt28        6\n",
      "exposes_racism      6\n",
      "FergSays            6\n",
      "brea55055           6\n",
      "Deb_Saw_Boy         5\n",
      "BLMTerrorist        5\n",
      "Progress4Ohio       5\n",
      "330Kingish          5\n",
      "yung_figgy          5\n",
      "zappin_liberals     5\n",
      "FightForHotep       5\n",
      "TBTRrevolution      4\n",
      "ProAmericana        4\n",
      "computablu          4\n",
      "timelessera         4\n",
      "handymayhem         4\n",
      "GinaInTheBurg       4\n",
      "milodogbos          4\n",
      "6inthebluff         4\n",
      "mjack888            4\n",
      "ThatzMimi           4\n",
      "CapnSuckMaCock      4\n",
      "WhoIsHuma           4\n",
      "NoThiefs            4\n",
      "raider_truth        4\n",
      "DanteB4u            3\n",
      "MissDBunker         3\n",
      "TrumpGirll2016      3\n",
      "TonyaSheffield_     3\n",
      "OckyJ83             3\n",
      "TwettGentleman      3\n",
      "EvrythingsaNail     3\n",
      "Energy53Alan        3\n",
      "EJLandwehr          3\n",
      "prycegod            3\n",
      "lunarmuva           3\n",
      "TcatdaGod           3\n",
      "VenoArt             3\n",
      "phoenixfreeband     3\n",
      "Name: user_screen_name, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "q = 'parsed_created_at > \"2016-09-06 00:00:00+00:00\"' \\\n",
    "    'and parsed_created_at < \"2016-09-14 00:00:00+00:00\"' \\\n",
    "    'and (match_type == \"name mention\" or match_type == \"user mentioned\")'\n",
    "    \n",
    "mentions = df.query(q)\n",
    "counts = mentions['user_screen_name'].groupby(mentions['user_screen_name']).count().sort_values(ascending=False)\n",
    "\n",
    "print(\"Total Tweets: \", len(mentions))\n",
    "print()\n",
    "print(\"Top 25 Users:\")\n",
    "print(counts[0:50])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "death = death[death['user_match'] == 'Darren Seals']\n",
    "death = death.query('match_type == \"name mention\" or match_type == \"user mentioned\"')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Darren Seals Hashtags\n",
    "\n",
    "Ranking Darren Seals' most frequently used hashtags."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Darren Seals Mentions\n",
    "\n",
    "Ranking Darren Seals' most frequently mentions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}