Streaming Twitter NX User Graph.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convert Streamed Twitter API JSON file to NetworkX & Gephi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Developed by Chris Allison - @ToferC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook takes a custom JSON dict of tweets pulled from the Twitter API, extracts the key data desired and creates first a NetworkX Graph and then exports to a Gephi file.\n",
    "\n",
    "Tweets are collected by twitter_to_json.py and placed in the results directory with the title \"search_\"query_name\".json\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import tweepy\n",
    "import os\n",
    "import datetime, time\n",
    "import nltk\n",
    "from nltk import word_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "import re\n",
    "import json\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Set up source and data directories - configure these as required\n",
    "\n",
    "home_dir = \"/Users/christopherallison/.virtualenvs/py_twi/streaming_results\"\n",
    "save_dir = \"/Users/christopherallison/Documents/Coding/Gephi/twitter_stream\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Enter your search queries here separated by spaces - \n",
    "# You must have already run search_twitter_to_json.py for the query\n",
    "\n",
    "search_queries = \"trudeau\".split()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Processing functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following functions are used by the main script to prep data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def find_user(tweet_id, tweet_dict):\n",
    "    # Find tweet author based on tweet status ID\n",
    "    try:\n",
    "        x = tweet_dict[tweet_id]['user_screen_name']\n",
    "    except KeyError:\n",
    "        x = None # User is out of scope\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "first_cap_re = re.compile('(.)([A-Z][a-z]+)')\n",
    "all_cap_re = re.compile('([a-z0-9])([A-Z])')\n",
    "\n",
    "def convert(name):\n",
    "    # Convert text to camel_case\n",
    "    s1 = first_cap_re.sub(r'\\1_\\2', name)\n",
    "    return all_cap_re.sub(r'\\1_\\2', s1).lower()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Full function for network graphing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def twitter_data_to_graph(search_query):\n",
    "    # Convert JSON file of tweets to NetworkX Graph and Gephi graph file\n",
    "    \n",
    "    # Set up empty strings for NTLK analysis\n",
    "    full_tweets = \"\"\n",
    "    full_hashtags = \"\"\n",
    "    \n",
    "    # Create dict for storing user data\n",
    "    users = {}\n",
    "    \n",
    "    for sq in search_query:\n",
    "        \n",
    "        tweets = {}\n",
    "        \n",
    "        # Open source files for each query\n",
    "        \n",
    "        with open(os.path.join(home_dir, '{}_stream.json'.format(sq)), 'r') as f:\n",
    "        \n",
    "            for data in f:\n",
    "        \n",
    "                result = json.loads(data)\n",
    "\n",
    "                try:\n",
    "                    id_str = result['id_str']\n",
    "\n",
    "                    tweets[id_str] = {\n",
    "                        'id_str': id_str,\n",
    "                        'date': str(result['created_at']),\n",
    "                        'text': result['text'],\n",
    "                        'retweet_count': result['retweet_count'],\n",
    "                        'favorite_count': result['favorite_count'],\n",
    "                        'reply_to': result['in_reply_to_screen_name'],\n",
    "                        'coordinates': result['coordinates'],\n",
    "                        'reply_to_tweet': result['in_reply_to_status_id'],\n",
    "                        'user_screen_name': result['user']['screen_name'],\n",
    "                        'quoted_status': result['is_quote_status'],\n",
    "                        'lang': result['lang'],\n",
    "                        'entities': result['entities'],\n",
    "                        'urls': result['entities']['urls'],\n",
    "                        'hashtags': result['entities']['hashtags'],\n",
    "                        'user_mentions': result['entities']['user_mentions'],\n",
    "                        'user': result['user']\n",
    "                        }\n",
    "\n",
    "                    try:\n",
    "                        tweets[id_str]['quoted_status_id_str'] = result['quoted_status_id_str']\n",
    "                    except KeyError:\n",
    "                        tweets[id_str]['quoted_status_id_str'] = None\n",
    "                        \n",
    "                except KeyError:\n",
    "                    continue\n",
    "        \n",
    "        for t in tweets:\n",
    "    \n",
    "            # Convert hashtags to string\n",
    "            n = []\n",
    "            if tweets[t]['hashtags']:\n",
    "                for i in tweets[t]['hashtags']:\n",
    "                    n.append(i['text'].lower())\n",
    "                tweets[t]['hashtags'] = \" \".join(n)\n",
    "            else:\n",
    "                tweets[t]['hashtags'] = \"\"\n",
    "\n",
    "            # Convert user-mentions to string\n",
    "            n = []\n",
    "            if tweets[t]['user_mentions']:\n",
    "                for i in tweets[t]['user_mentions']:\n",
    "                    n.append(i['screen_name'].lower())\n",
    "                tweets[t]['user_mentions'] = \" \".join(n)\n",
    "            else:\n",
    "                tweets[t]['user_mentions'] = \"\"\n",
    "                \n",
    "            # Set user name to save a lot of typing\n",
    "            name = str(tweets[t]['user_screen_name']).lower()\n",
    "            \n",
    "            # User already exists\n",
    "            try:\n",
    "                # Add hashtags used by the user\n",
    "                users[name]['hashtags'] += \" {}\".format(\n",
    "                tweets[t]['hashtags'])\n",
    "\n",
    "                # Add user-mentions used by the user\n",
    "                users[name]['user_mentions'] += \" {}\".format(\n",
    "                tweets[t]['user_mentions'])\n",
    "\n",
    "                # Add tweet id_strings used by the user\n",
    "                users[name]['id_str'] += \" {}\".format(\n",
    "                tweets[t]['id_str'])\n",
    "\n",
    "                # We are not adding additional info to date as it will disrupt dynamic graphs\n",
    "                pass\n",
    "\n",
    "                # Add texts from additional tweets and date info as text\n",
    "                users[name]['text'] += \"\\n\\n{}\\n{}\".format(\n",
    "                tweets[t]['text'], tweets[t]['date'])\n",
    "\n",
    "                # Count user tweets\n",
    "                users[name]['user_tweets'] += 1\n",
    "\n",
    "                # Add cumulative retweets for all tweets\n",
    "                users[name]['retweet_count'] += int(tweets[t]['retweet_count'])\n",
    "\n",
    "                users[name]['replies'] += \" {}\".format(tweets[t]['reply_to_tweet'])\n",
    "                users[name]['quoted_id_str'] += \" {}\".format(tweets[t]['quoted_status_id_str'])\n",
    "\n",
    "                users[name]['user_replies'] += \" {}\".format(tweets[t]['reply_to'])\n",
    "\n",
    "            except KeyError:\n",
    "\n",
    "                # Key doesn't exist, so add user\n",
    "                users[name] = {}\n",
    "\n",
    "                users[name]['screen_name'] = name\n",
    "                users[name]['id_str'] = tweets[t]['id_str']\n",
    "                users[name]['date'] = tweets[t]['date']\n",
    "                users[name]['text'] = tweets[t]['text']\n",
    "                users[name]['hashtags'] = tweets[t]['hashtags']\n",
    "                users[name]['retweet_count'] = tweets[t]['retweet_count']\n",
    "                users[name]['replies'] = str(tweets[t]['reply_to_tweet'])\n",
    "                users[name]['quoted_id_str'] = str(tweets[t]['quoted_status_id_str'])\n",
    "                users[name]['user_replies'] = str(tweets[t]['reply_to'])\n",
    "                users[name]['user_mentions'] = str(tweets[t]['user_mentions'])\n",
    "                users[name]['user_tweets'] = 1\n",
    "                \n",
    "            full_tweets += \"{} \".format(tweets[t]['text'])\n",
    "            full_hashtags += \"{} \".format(tweets[t]['hashtags'])\n",
    "                \n",
    "        # Remove duplicate hashtags\n",
    "        \n",
    "        for u in users:\n",
    "            users[u]['hashtags'] = \" \".join(set(users[u]['hashtags'].split(' ')))\n",
    "        \n",
    "        # Create one node for each tweet with embedded data - Graph N\n",
    "        for u in users:\n",
    "            N.add_node(str(users[u]['screen_name']), attr_dict=users[u])\n",
    "            \n",
    "        # Create edge_list\n",
    "\n",
    "        edge_dict = {}\n",
    "\n",
    "        for u in users:\n",
    "            temp = []\n",
    "\n",
    "            # Prep user-mentions\n",
    "            try:\n",
    "                um = users[u]['user_mentions'].split(\" \")\n",
    "            except AttributeError:\n",
    "                um = []\n",
    "\n",
    "            # Prep replies to users\n",
    "            try:\n",
    "                ur = users[u]['user_replies'].split(\" \")\n",
    "            except AttributeError:\n",
    "                ur = []\n",
    "\n",
    "\n",
    "            # Prep replies to tweets\n",
    "            try:\n",
    "                rtt = users[u]['replies'].split(\" \")\n",
    "            except AttributeError:\n",
    "                rtt = []\n",
    "\n",
    "            if rtt:\n",
    "                rtt = [find_user(u, tweets) for u in rtt]\n",
    "                \n",
    "            # Prep quoted_status_id_str\n",
    "            try:\n",
    "                qis = users[u]['quoted_id_str'].split(\" \")\n",
    "            except AttributeError:\n",
    "                qis = []\n",
    "\n",
    "            if qis:\n",
    "                qis = [find_user(u, tweets) for u in qis]\n",
    "\n",
    "\n",
    "            # Create edge list keys and weight\n",
    "            temp = um + ur + rtt + qis\n",
    "\n",
    "            # Remove any empty elements in list\n",
    "            for e in temp:\n",
    "                \n",
    "                # Remove any empty elements in list\n",
    "                if str(e) == \"\" or str(e) == 'None':\n",
    "                    pass\n",
    "                # Add edges to dict\n",
    "                else:\n",
    "                    try:\n",
    "                        edge_dict[str([users[u]['screen_name'], str(e)]).lower()]['weight'] += 1\n",
    "\n",
    "                    except KeyError:\n",
    "                        edge_dict[str([users[u]['screen_name'], str(e)]).lower()] = {}\n",
    "                        edge_dict[str([users[u]['screen_name'], str(e)]).lower()]['node'] = users[u]['screen_name']\n",
    "                        edge_dict[str([users[u]['screen_name'], str(e)]).lower()]['target'] = str(e)\n",
    "                        edge_dict[str([users[u]['screen_name'], str(e)]).lower()]['weight'] = 1\n",
    "                        \n",
    "        # Add edges for @mentions and replies to users\n",
    "        for e in edge_dict:\n",
    "            N.add_edge(edge_dict[e]['node'],\n",
    "                       edge_dict[e]['target'],\n",
    "                       weight=edge_dict[e]['weight'])\n",
    "\n",
    "        # Insert any data analysis here\n",
    "        print(\"Converting and analyzing search query: {}\".format(sq))\n",
    "        print(\"Nodes: {}, Edges: {}\".format(len(N.nodes()), len(N.edges())))\n",
    "        \n",
    "        # NLTK analysis\n",
    "        \n",
    "        tweet_tokens = word_tokenize(full_tweets)\n",
    "        hashtag_tokens = word_tokenize(full_hashtags)\n",
    "        \n",
    "        text = nltk.Text(tweet_tokens)\n",
    "        clean_tweets = [w for w in set(text) if w.lower() not in stopwords.words('english')]\n",
    "        \n",
    "        hts = nltk.Text(hashtag_tokens)\n",
    "        \n",
    "        print('\\nFrequency Distribution for hashtags:')\n",
    "        fdist1 = nltk.FreqDist(hts)\n",
    "        V = fdist1.most_common(n=50)\n",
    "        print([y for y in V])\n",
    "        fdist1.plot(30, cumulative=False)\n",
    "        \n",
    "    # Write G graph in gexf for Gephi\n",
    "    file_name = \"{}_graph_{}.gexf\".format(\n",
    "        convert(\"_\".join(search_queries)),\n",
    "        datetime.datetime.now())\n",
    "    \n",
    "    nx.write_gexf(N, os.path.join(save_dir, file_name))\n",
    "    print(\"New file {} saved to {}.\".format(file_name, save_dir))\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "N = nx.MultiDiGraph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converting and analyzing search query: trudeau\n",
      "Nodes: 73035, Edges: 80154\n",
      "\n",
      "Frequency Distribution for hashtags:\n",
      "[('elxn42', 21204), ('trudeau', 4213), ('cdnpoli', 3865), ('canadavotes', 2871), ('canada', 1155), ('breaking', 1058), ('elexn42', 854), ('elxn2015', 805), ('cometogether', 771), ('liberals', 746), ('yyc', 743), ('trudeaumania', 742), ('lpc', 726), ('liberal', 708), ('bluejays', 654), ('thenorthawakens', 628), ('yesoui', 626), ('realchange', 603), ('ctvelection', 492), ('harper', 479), ('electioncanada', 452), ('justintrudeau', 390), ('polcan', 358), ('ndp', 321), ('news', 309), ('dontdodrugs', 278), ('cpc', 278), ('canadaelections', 276), ('canadianelection', 252), ('cl42', 245), ('exln42', 245), ('22votes', 231), ('electionstva', 223), ('liberalparty', 210), ('cbc', 210), ('election2015', 210), ('elx42', 199), ('c51', 193), ('fed2015', 186), ('l', 180), ('canpoli', 172), ('polqc', 157), ('yxe', 156), ('saskatoon', 150), ('teamtrudeau', 150), ('jays', 149), ('toronto', 148), ('mulcair', 148), ('justin', 145), ('winning', 142)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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+jqgrDoAbb4Tx46FvX7jjDth2267WyHGc3o77OCL0caT5+MfhhBPMVHXwwTB3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\nqKUyJfnkFAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15cb54dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New file trudeau_graph_2015-10-20 20:53:28.805286.gexf saved to /Users/christopherallison/Documents/Coding/Gephi/twitter_stream.\n"
     ]
    }
   ],
   "source": [
    "twitter_data_to_graph(search_queries)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## You're done!  Now go check out your graph file in Gephi!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}