discoursebias_under_development.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Author: Mattias Östmar, 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import re\n",
    "from elasticsearch.helpers import scan\n",
    "import elasticsearch\n",
    "import pickle\n",
    "from textblob import TextBlob\n",
    "from collections import Counter\n",
    "from nltk.stem.snowball import SnowballStemmer\n",
    "import itertools\n",
    "import community\n",
    "import os\n",
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "from collections import defaultdict\n",
    "from html.parser import HTMLParser\n",
    "import tldextract\n",
    "from datetime import datetime\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the IDs for each blog to later iterate through and get the blog posts from."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "blogs = pickle.load(open(\"../pronouns/df_posts_per_blog.pickle\",\"rb\"))\n",
    "bids = blogs.bid.values # The blog IDs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load Swedish stopwords. Let's use the first hit on Google, taken from [https://github.com/stopwords-iso/stopwords-sv](https://github.com/stopwords-iso/stopwords-sv/blob/master/stopwords-sv.txt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total words: 419\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['aderton',\n",
       " 'adertonde',\n",
       " 'adjö',\n",
       " 'aldrig',\n",
       " 'alla',\n",
       " 'allas',\n",
       " 'allt',\n",
       " 'alltid',\n",
       " 'alltså',\n",
       " 'andra']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stopwords = [w.strip() for w in open(\"stopwords-sv.txt\").readlines()]\n",
    "stopwords.extend([\"http\"])\n",
    "print(\"Total words: {}\".format(len(stopwords)))\n",
    "stopwords[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load a sample text in order to have a high quality text to work with during development."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<?xml version=\\'1.0\\' encoding=\\'utf-8\\'?>\\n<gexf version=\"1.2\" xmlns=\"http://www.gexf.net/1.2draft\" xmln'"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_text_file = open(\"example_text.txt\")\n",
    "sample_text = sample_text_file.read()\n",
    "sample_text[:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use for development\n",
    "G = nx.read_gexf(\"bid_1397160.gexf\")\n",
    "G.name = \"bid_1397160\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing\n",
    "\n",
    "The best description is in [Dmitry Paranyushkin, 2011](Paranyushkin) Nodus Labs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_sentences(text):\n",
    "    \"\"\"\n",
    "    Normalizes text (removes punctuation) and splits each sentence into a list of words.\n",
    "    Return:list of sentences.\n",
    "    \"\"\"\n",
    "    blob = TextBlob(text)\n",
    "    \n",
    "    return [list(s.words) for s in blob.sentences]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_lowercase_sentences(sentences):\n",
    "    \"\"\"\n",
    "    Return:list of sentences.\n",
    "    \"\"\"\n",
    "    all_new_sents = []\n",
    "    for sent in sentences:\n",
    "        new_sent = []\n",
    "        for token in sent:\n",
    "            new_sent.append(token.lower())\n",
    "        all_new_sents.append(new_sent)\n",
    "        \n",
    "    return all_new_sents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_stopwords(sentences):\n",
    "    \"\"\"Presupposes concatenated texts into one large if several.\n",
    "    text: string\n",
    "    Return: list of tokens\"\"\"\n",
    "    all_new_sents = []\n",
    "    for sent in sentences:\n",
    "        new_sent = []\n",
    "        for t in sent:\n",
    "            if t.lower() in stopwords:\n",
    "                continue\n",
    "            else:\n",
    "                new_sent.append(t)\n",
    "        all_new_sents.append(new_sent)\n",
    "    \n",
    "    return all_new_sents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stem_words(sentences):\n",
    "    \"\"\"Presupposes that stopwords are already removed.\n",
    "    text: string\n",
    "    Return: string\"\"\"\n",
    "    stemmer = SnowballStemmer(\"swedish\")\n",
    "    \n",
    "    all_new_sents = []\n",
    "    for sent in sentences:\n",
    "        new_sent = [stemmer.stem(token) for token in sent]\n",
    "        all_new_sents.append(new_sent)\n",
    "        \n",
    "    return all_new_sents      "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Graph metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_text_network(sentences, name):\n",
    "    \"\"\"\n",
    "    sentences: normalized, stemmed list sentences split into list of words.\n",
    "    Return: gexf-format graph\n",
    "    \"\"\"\n",
    "    G = nx.Graph()\n",
    "    G.name = name\n",
    "    \n",
    "    # First run, create all nodes\n",
    "    for sent in sentences:\n",
    "        for w in sent:\n",
    "            if w not in G.nodes:\n",
    "                G.add_node(w)\n",
    "    \n",
    "    # Second run, create edgelist from (undirected) combinations\n",
    "    edgelist = [] # One single list of tuples from several sentence word lists\n",
    "    for sent in sentences:\n",
    "        edgelist.extend(list(itertools.combinations(sent,2)))\n",
    "    # print(edgelist)\n",
    "    \n",
    "    # Create edges\n",
    "    for edge_tuple in edgelist:\n",
    "        s,t = edge_tuple\n",
    "        if G.has_edge(s, t):\n",
    "            new_weight = G[s][t]['weight'] + 1\n",
    "            G.add_edge(s, t, weight = new_weight)\n",
    "        else:\n",
    "            G.add_edge(s, t, weight = 1)\n",
    "    \n",
    "    return G"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_overall_graph_stats(G, stats):\n",
    "    \"\"\"\n",
    "    G: networkx.Graph\n",
    "    stats: a dictionary\n",
    "    \"\"\"\n",
    "    stats[\"edges\"] = G.number_of_edges()\n",
    "    stats[\"nodes\"] = G.number_of_nodes()\n",
    "    stats[\"density\"] = nx.density(G)\n",
    "    \n",
    "    # average degree = The average degree of an undirected graph \n",
    "    # is the sum of the degrees of all its nodes divided by the \n",
    "    # number of nodes in the graph.\n",
    "    avDegree = float(sum(dict(G.degree()).values())) / float(G.number_of_nodes())\n",
    "    stats[\"avDegree\"] = avDegree\n",
    "    \n",
    "    return stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_communities(G):\n",
    "    \"\"\"Uses https://github.com/taynaud/python-louvain/.\n",
    "    Return: nx.Graph with node community integer added as node attribute 'com'\"\"\"\n",
    "    partition = community.best_partition(G)\n",
    "    for key in partition: # a dictionary {\"word\":community_integer}\n",
    "        G.nodes[key][\"com\"] = partition[key]\n",
    "    \n",
    "    return G"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_betweeness_centrality(G):\n",
    "    \"\"\"Uses networkx.algorithms.bipartite.centrality.betweenness_centrality.\n",
    "    Return: nx.Graph with node betweeness centrality (bc) as attribute\"\"\"\n",
    "    \n",
    "    bc = nx.betweenness_centrality(G)\n",
    "    for k,v in bc.items():\n",
    "        G.nodes[k][\"bc\"] = bc[k]\n",
    "\n",
    "    return G"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_influence_distribution(G, stats, percent=0.1 ):\n",
    "    \"\"\"how the most influential nodes / words are spread among different topics / graph communities\n",
    "    \n",
    "    G: networkx.Graph\n",
    "    stats: a dictionary\n",
    "    percent: percent of top nodes ranked on betweenness centrality to use\n",
    "    \"\"\"\n",
    "\n",
    "    nodes = []\n",
    "    bcs = []\n",
    "    coms = []\n",
    "    for n, data in G.nodes(data=True):\n",
    "        nodes.append(n)\n",
    "        bcs.append(data[\"bc\"])\n",
    "        coms.append(data[\"com\"])\n",
    "    df = pd.DataFrame({\"node\":nodes, \"bc\":bcs, \"com\":coms})\n",
    "    \n",
    "    topPrcOfNodes = math.floor(percent * len(G.nodes()))\n",
    "    topPrcDf = df.nlargest(topPrcOfNodes, \"bc\")\n",
    "    \n",
    "    # compute entropy\n",
    "    dist = topPrcDf.com.value_counts().values # nparray\n",
    "    dist = dist / sum(dist) # normalize values\n",
    "    # entr = np.nansum(dist * np.log2(1/dist)) use normalized value instead\n",
    "    normalized_entr = np.nansum(dist * np.log2(1/dist) / np.log2(len(dist)))\n",
    "    stats[\"topnBCNodesCommDistrEntropy\"] = normalized_entr\n",
    "    \n",
    "    xticks = [enum for enum, orig in enumerate(list(topPrcDf.com.value_counts().index))]\n",
    "    ax = topPrcDf.com.value_counts().plot(title=\"Top {}% nodes community distr.\\n{} entropy:{:.3f}\".format(percent * 100, G.graph['name'], normalized_entr), use_index=False, xticks=xticks)\n",
    "    ax.set_xlabel(\"Community 0 through {} of tot {}.\".format(len(topPrcDf.com.value_counts()), len(set(coms))))\n",
    "    ax.set_ylabel(\"Number of nodes.\")\n",
    "    \n",
    "    plt.show()\n",
    "    \n",
    "    return stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_community_structure(G, stats, percent=0.1):\n",
    "    \"\"\"how distinct they are and the % of nodes belonging to the top communities.\n",
    "    \n",
    "    From https://noduslabs.com/publications/Pathways-Meaning-Text-Network-Analysis.pdf:\n",
    "    \n",
    "    'The modularity measure that is more than 0.4 (Blondel, 2008) indicates that the partition\n",
    "    produced by the modularity algorithm can be used in order to detect distinct communities\n",
    "    within the network. It indicates that there are nodes in the network that are more densely\n",
    "    connected between each other than with the rest of the network and that their density is\n",
    "    noticeably higher than the graph’s average. ''\n",
    "    \n",
    "    G: networkx.Graph\n",
    "    percent: percent of nodes to be used as \"top nodes\".\n",
    "    stats: a dictionary\n",
    "    \"\"\"\n",
    "    print(\"Graph: {}\".format(nx.info(G)))\n",
    "    \n",
    "    graph_density = nx.density(G)\n",
    "    stats[\"density\"] = graph_density\n",
    "    print(\"graph density: {}\".format(graph_density))\n",
    "    \n",
    "    partition = community.best_partition(G)\n",
    "    modularity = community.modularity(partition, G)\n",
    "    stats[\"modularity\"] = modularity\n",
    "    print(\"modularity: {}\".format(modularity))\n",
    "    \n",
    "    nr_conn_comp = nx.number_connected_components(G)\n",
    "    stats[\"connComp\"] = nr_conn_comp\n",
    "    print(\"connected components: {}\".format(nr_conn_comp))\n",
    "    conn_components = nx.connected_components(G)\n",
    "    largest_conn_comp = next(conn_components)\n",
    "    percent_of_words_in_larg_conn_comp = float(len(largest_conn_comp) / len(G) * 100)\n",
    "    stats[\"prcOfWordsInLargestConnComp\"] = percent_of_words_in_larg_conn_comp\n",
    "    print(\"{:.1f}% of words in largest connected component\".format(percent_of_words_in_larg_conn_comp))\n",
    "    \n",
    "    if modularity > 0.4:\n",
    "        pass # Todo: Calculate graph density for each subgraph?\n",
    "    \n",
    "    # percent of nodes belonging to the top communities\n",
    "    nodes = []\n",
    "    bcs = []\n",
    "    coms = []\n",
    "    for n, data in G.nodes(data=True):\n",
    "        nodes.append(n)\n",
    "        bcs.append(data[\"bc\"])\n",
    "        coms.append(data[\"com\"])\n",
    "    df = pd.DataFrame({\"node\":nodes, \"bc\":bcs, \"com\":coms})\n",
    "    \n",
    "    topPrcOfNodes = math.floor(percent * len(G.nodes()))\n",
    "    topBcDf = df.nlargest(topPrcOfNodes, \"bc\")\n",
    "    \n",
    "    coms = nx.get_node_attributes(G, 'com')\n",
    "    distinct_coms = set([y for x,y in coms.items()])\n",
    "    stats[\"louvainComms\"] = len(distinct_coms)\n",
    "    print(\"communities (louvain): {}\".format(len(distinct_coms)))\n",
    "    \n",
    "    sorted_df = df.com.value_counts()\n",
    "    print(\"words in top community: {}\".format(sorted_df.iloc[0]))\n",
    "    print(\"{:.1f}% of words in largest community (louvain)\".format((sorted_df.iloc[0] / len(df)) * 100))\n",
    "    \n",
    "    avEdgesPerComs = len(G.edges()) / len(distinct_coms)\n",
    "    stats[\"avEdgesPerCom\"] = avEdgesPerComs\n",
    "    print(\"Average Edges per communities: {}\".format(avEdgesPerComs))\n",
    "    \n",
    "    avNodesPerComs = len(G.nodes()) / len(distinct_coms)\n",
    "    stats[\"avNodesPerCom\"] = avNodesPerComs\n",
    "    print(\"Average Nodes per communities: {}\".format(avNodesPerComs))\n",
    "        \n",
    "    return stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def replace_fileextension(fname, new_ext):\n",
    "    \"\"\"Replaces the file extension, e.g. .txt to .gexf.\"\"\"\n",
    "    filename = Path(fname)\n",
    "    no_ext = filename.with_suffix('')\n",
    "    new_fname = str(no_ext) + \".\" + new_ext\n",
    "    return new_fname"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def write_graph_to_gexf_file(G, path=\"./\", fname=None):\n",
    "    \"\"\"Takes nx.Graph, writes it to gexf-file in ./.\n",
    "    Return: Info string\n",
    "    \"\"\"\n",
    "    if fname:\n",
    "        nx.write_gexf(G, path + fname)\n",
    "    else:\n",
    "        print(\"Error: no filename provided when trying to write gexf file.\")\n",
    "    return print(\"Stored graph in gexf-file {}.\".format(fname))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_graph_process_on_text(text, name):\n",
    "    \"\"\"text: string.\n",
    "    name: string, e.g. bid_134 or perolsson.txt.\n",
    "    stats: a dictionary\n",
    "    return: None\n",
    "    \"\"\"\n",
    "    start = datetime.now()\n",
    "    stats = {}\n",
    "    stats[\"name\"] = name\n",
    "    stats[\"total_words\"] = len(text.split())\n",
    "    stats[\"unique_words\"] = len(set(text.split()))\n",
    "    \n",
    "    # preprocessing\n",
    "    sentences = split_sentences(text)\n",
    "    stats[\"sentences\"] = len(sentences)\n",
    "\n",
    "    lowerc_sents = make_lowercase_sentences(sentences)\n",
    "\n",
    "    no_stops = remove_stopwords(lowerc_sents)\n",
    "\n",
    "    stemmed = stem_words(no_stops)\n",
    "    print(\"Finished preprocessing.\")\n",
    "\n",
    "    # Graph computations\n",
    "    G = create_text_network(stemmed ,name)\n",
    "    t26 = datetime.now()\n",
    "    diff = t26 - start\n",
    "    print(\"Finished creating graph after {}\".format(diff))\n",
    "\n",
    "    G = compute_communities(G) # # Adds value to nodes\n",
    "    t31 = datetime.now()\n",
    "    diff = t31 - t26\n",
    "    print(\"Finished compute_communities after {}\".format(diff))\n",
    "\n",
    "    G = compute_betweeness_centrality(G) # Adds values to nodes\n",
    "    t36 = datetime.now()\n",
    "    diff = t36 - t31\n",
    "    print(\"Finished compute_betweeness_centrality\".format(diff))\n",
    "    \n",
    "    stats = compute_overall_graph_stats(G, stats)\n",
    "    print(\"Finished compute_overall_graph_stats.\")\n",
    "    \n",
    "    stats = compute_influence_distribution(G, stats)\n",
    "    t44 = datetime.now()\n",
    "    diff = t44 - t36\n",
    "    print(\"Finished compute_influence_distribution after {}.\".format(diff))\n",
    "    \n",
    "    stats = compute_community_structure(G, stats)\n",
    "    t49 = datetime.now()\n",
    "    diff = t49 - t44\n",
    "    print(\"Finished compute_community_structure after {}.\".format(diff))\n",
    "          \n",
    "\n",
    "    new_fname = replace_fileextension(name, \"gexf\")\n",
    "    stats[\"graphFile\"] = new_fname\n",
    "    write_graph_to_gexf_file(G, path=\"./\", fname=new_fname)\n",
    "    t57 = datetime.now()\n",
    "    diff = t57 - start\n",
    "    print(\"Finished full process for {} after {} in total.\".format(name, diff))\n",
    "    print()\n",
    "    \n",
    "    return stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished preprocessing.\n",
      "Finished creating graph.\n",
      "Finished compute_communities.\n",
      "Finished compute_betweeness_centrality\n",
      "Finished compute_overall_graph_stats.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished compute_influence_distribution.\n",
      "Graph: Name: sample1.txt\n",
      "Type: Graph\n",
      "Number of nodes: 218\n",
      "Number of edges: 1071\n",
      "Average degree:   9.8257\n",
      "graph density: 0.04527966854098846\n",
      "modularity: 0.7029394716540522\n",
      "connected components: 10\n",
      "91.3% of words in largest connected component\n",
      "communities (louvain): 19\n",
      "words in top community: 29\n",
      "13.3% of words in largest community (louvain)\n",
      "Average Edges per communities: 56.36842105263158\n",
      "Average Nodes per communities: 11.473684210526315\n",
      "Finished compute_community_structure.\n",
      "Stored graph in gexf-file sample1.gexf.\n",
      "\n",
      "Finished preprocessing.\n",
      "Finished creating graph.\n",
      "Finished compute_communities.\n",
      "Finished compute_betweeness_centrality\n",
      "Finished compute_overall_graph_stats.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished compute_influence_distribution.\n",
      "Graph: Name: sample2.txt\n",
      "Type: Graph\n",
      "Number of nodes: 255\n",
      "Number of edges: 2305\n",
      "Average degree:  18.0784\n",
      "graph density: 0.07117492666357882\n",
      "modularity: 0.5791783260805571\n",
      "connected components: 3\n",
      "97.3% of words in largest connected component\n",
      "communities (louvain): 12\n",
      "words in top community: 39\n",
      "15.3% of words in largest community (louvain)\n",
      "Average Edges per communities: 192.08333333333334\n",
      "Average Nodes per communities: 21.25\n",
      "Finished compute_community_structure.\n",
      "Stored graph in gexf-file sample2.gexf.\n",
      "\n",
      "Finished preprocessing.\n",
      "Finished creating graph.\n",
      "Finished compute_communities.\n",
      "Finished compute_betweeness_centrality\n",
      "Finished compute_overall_graph_stats.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished compute_influence_distribution.\n",
      "Graph: Name: sample3.txt\n",
      "Type: Graph\n",
      "Number of nodes: 285\n",
      "Number of edges: 1988\n",
      "Average degree:  13.9509\n",
      "graph density: 0.04912280701754386\n",
      "modularity: 0.6632966572258876\n",
      "connected components: 3\n",
      "98.9% of words in largest connected component\n",
      "communities (louvain): 14\n",
      "words in top community: 38\n",
      "13.3% of words in largest community (louvain)\n",
      "Average Edges per communities: 142.0\n",
      "Average Nodes per communities: 20.357142857142858\n",
      "Finished compute_community_structure.\n",
      "Stored graph in gexf-file sample3.gexf.\n",
      "\n",
      "Finished preprocessing.\n",
      "Finished creating graph.\n",
      "Finished compute_communities.\n",
      "Finished compute_betweeness_centrality\n",
      "Finished compute_overall_graph_stats.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished compute_influence_distribution.\n",
      "Graph: Name: sample4.txt\n",
      "Type: Graph\n",
      "Number of nodes: 338\n",
      "Number of edges: 2847\n",
      "Average degree:  16.8462\n",
      "graph density: 0.049988587080575214\n",
      "modularity: 0.6725450473495436\n",
      "connected components: 5\n",
      "94.1% of words in largest connected component\n",
      "communities (louvain): 15\n",
      "words in top community: 64\n",
      "18.9% of words in largest community (louvain)\n",
      "Average Edges per communities: 189.8\n",
      "Average Nodes per communities: 22.533333333333335\n",
      "Finished compute_community_structure.\n",
      "Stored graph in gexf-file sample4.gexf.\n",
      "\n"
     ]
    },
    {
     "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>avDegree</th>\n",
       "      <th>avEdgesPerCom</th>\n",
       "      <th>avNodesPerCom</th>\n",
       "      <th>connComp</th>\n",
       "      <th>density</th>\n",
       "      <th>edges</th>\n",
       "      <th>graphFile</th>\n",
       "      <th>louvainComms</th>\n",
       "      <th>modularity</th>\n",
       "      <th>name</th>\n",
       "      <th>nodes</th>\n",
       "      <th>prcOfWordsInLargestConnComp</th>\n",
       "      <th>sentences</th>\n",
       "      <th>topnBCNodesCommDistrEntropy</th>\n",
       "      <th>total_words</th>\n",
       "      <th>unique_words</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.825688</td>\n",
       "      <td>56.368421</td>\n",
       "      <td>11.473684</td>\n",
       "      <td>10</td>\n",
       "      <td>0.045280</td>\n",
       "      <td>1071</td>\n",
       "      <td>sample1.gexf</td>\n",
       "      <td>19</td>\n",
       "      <td>0.702939</td>\n",
       "      <td>sample1.txt</td>\n",
       "      <td>218</td>\n",
       "      <td>91.284404</td>\n",
       "      <td>49</td>\n",
       "      <td>0.983714</td>\n",
       "      <td>715</td>\n",
       "      <td>378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18.078431</td>\n",
       "      <td>192.083333</td>\n",
       "      <td>21.250000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.071175</td>\n",
       "      <td>2305</td>\n",
       "      <td>sample2.gexf</td>\n",
       "      <td>12</td>\n",
       "      <td>0.579178</td>\n",
       "      <td>sample2.txt</td>\n",
       "      <td>255</td>\n",
       "      <td>97.254902</td>\n",
       "      <td>42</td>\n",
       "      <td>0.848002</td>\n",
       "      <td>1002</td>\n",
       "      <td>449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.950877</td>\n",
       "      <td>142.000000</td>\n",
       "      <td>20.357143</td>\n",
       "      <td>3</td>\n",
       "      <td>0.049123</td>\n",
       "      <td>1988</td>\n",
       "      <td>sample3.gexf</td>\n",
       "      <td>14</td>\n",
       "      <td>0.663297</td>\n",
       "      <td>sample3.txt</td>\n",
       "      <td>285</td>\n",
       "      <td>98.947368</td>\n",
       "      <td>48</td>\n",
       "      <td>0.963143</td>\n",
       "      <td>987</td>\n",
       "      <td>485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.846154</td>\n",
       "      <td>189.800000</td>\n",
       "      <td>22.533333</td>\n",
       "      <td>5</td>\n",
       "      <td>0.049989</td>\n",
       "      <td>2847</td>\n",
       "      <td>sample4.gexf</td>\n",
       "      <td>15</td>\n",
       "      <td>0.672545</td>\n",
       "      <td>sample4.txt</td>\n",
       "      <td>338</td>\n",
       "      <td>94.082840</td>\n",
       "      <td>49</td>\n",
       "      <td>0.918923</td>\n",
       "      <td>1025</td>\n",
       "      <td>539</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    avDegree  avEdgesPerCom  avNodesPerCom  connComp   density  edges  \\\n",
       "0   9.825688      56.368421      11.473684        10  0.045280   1071   \n",
       "1  18.078431     192.083333      21.250000         3  0.071175   2305   \n",
       "2  13.950877     142.000000      20.357143         3  0.049123   1988   \n",
       "3  16.846154     189.800000      22.533333         5  0.049989   2847   \n",
       "\n",
       "      graphFile  louvainComms  modularity         name  nodes  \\\n",
       "0  sample1.gexf            19    0.702939  sample1.txt    218   \n",
       "1  sample2.gexf            12    0.579178  sample2.txt    255   \n",
       "2  sample3.gexf            14    0.663297  sample3.txt    285   \n",
       "3  sample4.gexf            15    0.672545  sample4.txt    338   \n",
       "\n",
       "   prcOfWordsInLargestConnComp  sentences  topnBCNodesCommDistrEntropy  \\\n",
       "0                    91.284404         49                     0.983714   \n",
       "1                    97.254902         42                     0.848002   \n",
       "2                    98.947368         48                     0.963143   \n",
       "3                    94.082840         49                     0.918923   \n",
       "\n",
       "   total_words  unique_words  \n",
       "0          715           378  \n",
       "1         1002           449  \n",
       "2          987           485  \n",
       "3         1025           539  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats_list = []\n",
    "for fname in [\"sample1.txt\",\"sample2.txt\",\"sample3.txt\",\"sample4.txt\"]:\n",
    "    \n",
    "    text = open(fname).read()\n",
    "    \n",
    "    stats = run_graph_process_on_text(text, fname)\n",
    "    \n",
    "    stats_list.append(stats)\n",
    "\n",
    "df = pd.DataFrame(stats_list)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run process on blogs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First prepare to strip HTML from the blog posts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MLStripper(HTMLParser):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.reset()\n",
    "        self.fed = []\n",
    "    def handle_data(self, d):\n",
    "        self.fed.append(d)\n",
    "    def get_data(self):\n",
    "        return ''.join(self.fed)\n",
    "\n",
    "    \n",
    "def strip_tags(html): \n",
    "    s = MLStripper()\n",
    "    s.feed(html)\n",
    "    return s.get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "es = elasticsearch.Elasticsearch()\n",
    "\n",
    "es.indices.put_settings(index=\"blogposts\",\n",
    "                        body= {\"index\" : {\n",
    "                                \"max_result_window\" : 500000 # much higher than default value 1000\n",
    "                              }})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_url_from_bid(bid):\n",
    "    \"\"\"\n",
    "    bid: blog ID\n",
    "    return: tldextract obejct\n",
    "    \"\"\"\n",
    "    search = es.search(index='blogs', body={\n",
    "                                            'query': {\n",
    "                                                 'match': {\n",
    "                                                      'bid': bid,\n",
    "                                                          }\n",
    "                                                        }\n",
    "                                            })\n",
    "    url = search['hits']['hits'][0]['_source']['url']\n",
    "    \n",
    "    result = tldextract.extract(url)\n",
    "    domain = result.fqdn # tldextract.extract(\"http://katja.wordpress.info/jku_kl\") = katja.wordpress.info\n",
    "    return domain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def retrieve_all_text_from_blogid(bid):\n",
    "    \"\"\"Search Elasticsearch for a blogid and concatenate all results to a html-free string.\n",
    "    bid: integer representing field \"bid\" (blog ID) in bloggz dataset in elasticserach.\n",
    "    Return: string\n",
    "    \"\"\"\n",
    "    all_text = \"\"\n",
    "    \n",
    "    page = es.search(\n",
    "            index = 'blogposts',\n",
    "              doc_type = 'blogpost',\n",
    "              scroll = '2m',\n",
    "              size = 1000,\n",
    "              body = {\n",
    "                    'query': {\n",
    "                        'match': {\n",
    "                            'bid': bid,\n",
    "                                }\n",
    "                            }\n",
    "                        })\n",
    "\n",
    "    sid = page['_scroll_id']\n",
    "    scroll_size = page['hits']['total']\n",
    "    nrOfPosts = scroll_size\n",
    "\n",
    "    # First batch of pages to scroll through\n",
    "    hits = len(page[\"hits\"][\"hits\"])\n",
    "        \n",
    "    for item in page['hits']['hits']:\n",
    "        post_string = strip_tags(item[\"_source\"][\"html\"])\n",
    "        all_text += post_string + \"\\n\\n\" # Each post becomes a paragraph, in theory\n",
    "\n",
    "    # Continue with next pages\n",
    "    while (scroll_size > 0):\n",
    "        page = es.scroll(scroll_id = sid, scroll = '2m')\n",
    "        # Update the scroll ID\n",
    "        sid = page['_scroll_id']\n",
    "        # Get the number of results that we returned in the last scroll\n",
    "        scroll_size = len(page['hits']['hits'])\n",
    "        \n",
    "        post_string = strip_tags(item[\"_source\"][\"html\"])\n",
    "        all_text += post_string + \"\\n\\n\" # Each post becomes a paragraph, in theory\n",
    "        \n",
    "    return all_text, nrOfPosts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "politism.se\n",
      "1832\n",
      "Finished preprocessing.\n",
      "Finished creating graph after 0:00:01.334513\n",
      "Finished compute_communities after 0:00:07.378031\n",
      "Finished compute_betweeness_centrality\n",
      "Finished compute_overall_graph_stats.\n"
     ]
    },
    {
     "data": {
      "image/png": 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OnBna4lfVYbiS1SNx3VyLiX1Q32SY1RoyBxCRRcDV6q5xbLKAiDwHLFPVu+JMcyIueXaIONZgTEK2R2BMNecPrN8IPGVJwFSGJQJjqjERORx3tk4r4E8ZDsdUU9Y1ZIwxOc72CIwxJselu+BVpRQXF2uHDh0yHYYxxlQrEydOXKuqJYmmqxaJoEOHDkyYMCHTYRhjTLUiIosTT2VdQ8YYk/MsERhjTI6zRGCMMTnOEoExxuQ4SwTGGJPjLBEYY0yOs0RgjDE5rkYngv9NW8nL45ZkOgxjjMlqNToRvD91JX/83yy27NyT6VCMMSZrpTQRiEhjEXldRGaLyCwROV5EmorIhyIy1/9tkqr5XzuwE1t2lttegTHGxJHqPYI/A8NUtSvumqWzgNuBEaraGRjhH6fE0W0bMaC0mKfGLGTnnoO+8qExxtRIKUsEItIQOBF3qT5UdbeqbsRdI/V5P9nzwHmpigHgukGdKNuyi7cmJ7q+ujHG5KZU7hF0BMqAZ0Vksog8JSL1gBaquhLA/20erbGIXCMiE0RkQllZWaWD6NepGd3aNuLvo+ezt8KuvWCMMZFSmQjygZ7A46raA9hGEt1AqvqkqvZW1d4lJQmrqMYkIlw3sBOL1m1n2PRVlX4fY4ypqVKZCJbhLrg9zj9+HZcYVotIKwD/d00KYwDg9CNb0rG4Ho+Pnoddkc0YYw6UskSgqquApSLSxT91CjATeBe4zD93GfBOqmIIyasl/HhgR6Yv38yYeWtTPTtjjKlWUn3W0A3ASyIyFegO/AG4DzhNROYCp/nHKXdejza0aFjE46Pmp2N2xhhTbaT0CmWqOgXoHeWlU1I532iK8vO4ekBHfv/fWUxZupHu7RqnOwRjjMlKNXpkcaRLjmtPw9r5PGF7BcYYs09OJYL6Rflc1q8DH8xcxbw1WzMdjjHGZIWcSgQAl/frQFF+LZ78xPYKjDEGcjARNKtfxEW92/HW5OWs3LQj0+EYY0zG5VwiALj6hI5UKDz96cJMh2KMMRmXk4mgXdO6DDmmNS+PX8LG7bszHY4xxmRUTiYCgB8P7Mj23Xt54fPFmQ7FGGMyKmcTQdeWDTmla3OeHbuQ7bvLMx2OMcZkTM4mAnAlqjds38NrXy7NdCjGGJMxOZ0IendoSp8OTfjHpwvZs7ci0+EYY0xG5HQiALdXsHzjDt77akWmQzHGmIzI+URwUpfmdGnRgCdGz6fCLlxjjMlBOZ8IRITrBnVizuqtfDw75ZdGMMaYrJPziQDg7G6taNukDo+NsgvXGGNyjyUCID+vFtec2JFJSzby5aINmQ7HGGPSyhKB951e7WhWr5DHR83LdCjGGJNWlgi8OoV5XNG/AyO/LmPWys2ZDscYY9LGEkGYH/TtQL3CPJ4YbSWqjTG5wxJBmEZ1C/jece15f+pKVm/emelwjDEmLSwRRLjk2PbsrVDenrw806EYY0xaWCKI0LGkPr0OacLrE5fZqaTGmJxgiSCKC3u1Ze6arUxdtinToRhjTMpZIojirG6tKMqvxesTl2U6FGOMSTlLBFE0rF3A4KNa8u5XK9i5Z2+mwzHGmJSyRBDDhb3asmnHHkbMsvpDxpiazRJBDP06FdOqUW1en2gXrTHG1GwpTQQiskhEponIFBGZ4J9rKiIfishc/7dJKmOorLxawrd7tmH0nDLW2JgCY0wNlo49gpNUtbuq9vaPbwdGqGpnYIR/nJUu6NmWCoW3bEyBMaYGy0TX0LnA8/7+88B5GYghEBtTYIzJBalOBAoMF5GJInKNf66Fqq4E8H+bR2soIteIyAQRmVBWVpbiMGOzMQXGmJou1Ymgv6r2BM4ErheRE4M2VNUnVbW3qvYuKSlJXYQJ2JgCY0xNl9JEoKor/N81wFvAscBqEWkF4P9m9fmZNqbAGFPTpSwRiEg9EWkQug+cDkwH3gUu85NdBryTqhiqio0pMMbUZKncI2gBjBGRr4DxwH9UdRhwH3CaiMwFTvOPs5qNKTDG1GT5qXpjVV0AHBPl+XXAKamabyqExhQ8Pmo+azbvpHnD2pkOyRhjqoyNLA7IxhQYY2oqSwQB2ZgCY0xNZYkgCTamwBhTE1kiSIKNKTDG1ESWCJJgYwqMMTWRJYIk2ZgCY0xNY4kgSaExBW9Msu4hY0zNYIkgSXadAmNMTWOJoBIu6NmWvRXK21NsTIExpvqzRFAJNqbAGFOTWCKopAt7tWXO6q1MW25jCowx1ZslgkqyMQXGmJqiUolARFpWdSDVTWhMwTtTVrCr3MYUGGOqr8ruETxdpVFUUzamwBhTE1QqEajqWVUdSHXUr1MxLRvWtu4hY0y1ljARiEgnESny9weJyM9EpHHqQ8t+NqbAGFMTBNkjeAPYKyKluC6hQ4GXUxpVNXJBLxtTYIyp3oIkggpVLQfOB/6kqjcDrVIbVvXRqaQ+Pds3tjEFxphqK0gi2CMil+AuNP++f64gdSFVPxf2asec1VuZuHhDpkMxxpikBUkEVwDHA79X1YUicijwz9SGVb2c2701zeoV8tDwr22vwBhT7SRMBKo6ExgKTPKPF6rqfakOrDqpV5TPDSeX8sWC9YyeU5bpcIwxJilBzho6B5gCDPOPu4vIu6kOrLr53nGH0K5pHe4f9jUVFbZXYIypPoJ0Dd0NHAtsBFDVKbgzh0yYwvxa3HpaF2at3Mx7U1dkOhxjjAksSCIoV9XIymq2yRvFkGNac3irhjw8fA67yysyHY4xxgQSJBFMF5HvAXki0llE/gp8luK4qqVatYShg7uwZP12Xhm/JNPhGGNMIEESwQ3AkcAu4BVgM3BTKoOqzgYeVkLfjk3568dz2bqrPNPhGGNMQkHOGtquqr9U1T6q2tvfD1xPQUTyRGSyiLzvHx8qIuNEZK6I/EtECg/mA2QbEWHo4K6s3bqbpz5dkOlwjDEmofxYL4jIe8Q5FqCqQwLO40ZgFtDQP74feERVXxWRJ4CrgMcDvle10KN9EwYf2ZJ/fLKA7/c9hOL6RZkOyRhjYoq3R/AQ8DCwENgB/MPftgLTg7y5iLQFzgKe8o8FOBl43U/yPHBeZQLPdj8/ows79uzl0Y/nZToUY4yJK2YiUNXRqjoa6KGqF6nqe/72PWBAwPf/E3AbEDqFphmw0dcuAlgGtInWUESuEZEJIjKhrKz6DdIqbV6f7/Zux0vjFrN0/fZMh2OMMTEFOVhcIiIdQw98iYmSRI1E5GxgjapODH86yqRRu59U9Ul/TKJ3SUnC2WWlm049jFoi/N+HczIdijHGxBQkEdwMjBKRUSIyChiJ6/dPpD8wREQWAa/iuoT+BDQWkdCxibZAjR191bJRbS7v34G3pyxn1srNmQ7HGGOiCnLW0DCgM27lfyPQRVWHB2h3h6q2VdUOwMXAx6p6KS6RXOgnuwx4p5KxVws/GVhKg6J8Hhg2O9OhGGNMVEFqDRUAPwZ+5W8/8s9V1lDgFhGZhztmUKOvf9yobgE/OamUkV+XMW7BukyHY4wx3xCka+hxoBfwmL/1IsnTPVV1lKqe7e8vUNVjVbVUVb+jqruSDbq6ubxfB1o2rM19w2ZbmWpjTNYJkgj6qOplqvqxv10B9El1YDVJ7YI8bjq1M5OXbOSDGaszHY4xxhwgSCLYKyKdQg/8GUR7UxdSzXRhr7Z0KqnHgx/MpnyvFaQzxmSPIIngF8BIf9bQaOBj4NbUhlXz5OfV4hdndGF+2TbemLQs0+EYY8w+MUtMhKjqCBHpDHTBjQOYnQv9+qlwxpEt6d6uMY98OJdzu7ehdkFepkMyxphAewTgDhAfBRwDXCQiP0xdSDVXqCDdqs07ef6zRZkOxxhjgGCnj76Iqzs0AHeQuA/QO8Vx1VjHd2rGoC4lPDZqPpt27Ml0OMYYk7hrCLfSP0LtvMcqc9sZXTnrr5/yxOj5DB3cNdPhGGNyXKArlAEtUx1ILjmidUPOPaY1z45dyKK12zIdjjEmxwXZIygGZorIeNxVyoCkrkdgorj19C78d/oqBj00iq4tG9C/tJgBnYs57tCm1C0M8m8xxpiqIYl6fERkYLTnfYnqtOjdu7dOmDAhXbNLm/llW/lgxirGzlvLl4s2sLu8goI8oUf7JpxQWkz/zsV0a9OI/Lygx/SNMWY/EZmoqgmP6SZMBNmgpiaCcDv37OXLResZM28tY+etZcaKzahCg9r5HN+xGQM6F9O/tJiOxfVw1/cxxpj4giYC64PIErUL8jihcwkndHbXXli/bTefzXdJ4dO5axk+05WmaN2oNmcf05prB3aiab0adblnY0yG2B5BNbF43TbGzFvL6K/L+GjWauoV5nPNiR25csCh1CuyfG6M+aaD7hoSkRGqeoqI3K+qQ6s8wiRYIjjQ3NVbePCDrxk+czXF9Yv42SmlXNynPYX5dizBGLNfVSSCmcB1wBPA94i4zKSqTqqCOAOxRBDdxMUbuH/YbMYvXE/7pnW59fTDOKdba2rVsmMIxpiqSQQXAlfhRhRHroVVVU8+6CgDskQQm6oyak4ZDwz7mlkrN3N4q4bcNrgLgw4rsYPKxuS4KjtrSER+par3VllklWCJILGKCuW9qSt4ePgclqzfznGHNmXomV3p2b5JpkMzxmRIlZ4+KiJDgBP9w1Gq+v5BxpcUSwTB7S6v4NUvl/CXEXNZu3U3px/Rgl+c0YXOLRpkOjRjTJpV5R7BH4FjgZf8U5cAE1T1joOOMiBLBMnbtqucZ8Ys5O+fLGD77nK+d1x77j7nSBucZkwOqcpEMBXorqoV/nEeMFlVu1VJpAFYIqi89dt285cRc3nus0V8u2cbHrrwGDuYbEyOqOoBZY2B9f5+o0pHZdKuab1C7h5yJE3qFvLIR3NoWLuA35xzhB1INsbsEyQR/BGYLCIjcaeQngikrVvIVI2fnVLK5p17eHrMQhrWKeCW0w7LdEjGmCwR5FKVr4jIKNwFaQQYqqqrUh2YqVoiwl1nHc6WnXv4y4i5NKydz9UndMx0WMaYLBCoa0hVVwLvpjgWk2Iiwh+/3Y2tu8r53X9m0aB2Phf1aZ/psIwxGWZFanJMXi3hkYu6s3XXRO54cxr1iwo4q1urTIdljMkgO5cwBxXl5/HE93vSs30TbvrXZEZ9vSbTIRljMihuIhCRWiIyPV3BmPSpW5jP05f3oXPzBlz7z4l8uWh94kbGmBopbiLwYwe+EpGkO5JFpLaIjBeRr0Rkhojc458/VETGichcEfmXiFhR/QxpVKeAF646ltaN6nDls18yffmmTIdkjMmAIF1DrYAZIjJCRN4N3QK02wWcrKrHAN2BwSLSF7gfeERVOwMbcIXtTIYU1y/in1cfR8M6BVz2zHjml23NdEjGmDQLkgjuAc4Gfgs8HHaLS53QWqXA3xQ4GXjdP/88cF6SMZsq1rpxHV686lhE4AdPjWP5xh2ZDskYk0YJE4G/SP0ioMDf/xIIdC0CEckTkSnAGuBDYD6wUVXL/STLgDYx2l4jIhNEZEJZWVmQ2ZmD0LGkPi9ceRxbdpXz/afGUbZlV6ZDMsakScJEICI/wm3B/90/1QZ4O8ibq+peVe0OtMUVrjs82mQx2j6pqr1VtXdJSUmQ2ZmDdETrhjx3RR9WbdrJD58Zz6btezIdkjEmDYJ0DV0P9Ac2A6jqXKB5MjNR1Y3AKKAv0FhEQuMX2gIrknkvk1q9DmnKkz/sxfw1W7n8ufEs27A90yEZY1IsSCLYpaq7Qw/8SjzhRQxEpEREGvv7dYBTgVnASOBCP9llwDvJBm1S64TOJfzlku7MWLGZkx8azb3vz2T9tt2JGxpjqqUgiWC0iNwJ1BGR04B/A+8FaNcKGOnLWH8JfOgvaDMUuEVE5gHNgKcrF7pJpcFHtWLUzwdxXo/WPDt2IQMfGMlfR8xl267yxI2NMdVKkOsR1MKd4nk6rujcB8BTGuTSZlXErkeQWfPWbOHBD77mgxmrKa5fxM9OKeXiPu0pzLeB6cZks6q+VGUh0BXXJfR1eFdROlgiyA6Tlmzg/v/NZtzC9bRvWpdbTz+Mc7q1tgvdGJOlgiaCIGcNnYU77fMvwKPAPBE58+BDNNVNz/ZNePWavjx3RR/qF+Vz46tTOOuvYxj59RrSuINojKliQbot97QdAAAaCklEQVSGZgNnq+o8/7gT8B9V7ZqG+ADbI8hGFRXKe1NX8PDwOSxZv51jD23K7Wd2pWf7JpkOzRjjVdkeAbAmlAS8BbgBYiaH1aolnNu9DR/dMpB7zz2SBWXb+PZjn3HNCxMYMWs1W+2gsjHVRszrEYjIt/3dGSLyX+A13DGC7+DOAjKGwvxa/OD4Dny7Z1ueHbuQv49ewPCZq8mvJXRv15j+pcWc0LmYY9o1piDPDi4bk41idg2JyLNx2qmqXpmakL7Juoaqj5179jJp8QbGzFvL2Hlrmbp8E6pQrzCPvh2b7UsMpc3rI2IHmY1JpaBdQzH3CFT1iqoNyeSC2gV59Cstpl9pMQAbt+/m8/nr9iWGEbNdr2LzBkUMKC2mf2kxAzoX06Jh7UyGbUxOC3Kw+FDgBqADYYlDVYekNLIwtkdQcyxdv52x89YyZt5aPpu/bt+I5c7N67ukUFpM307NqF9kV1E15mBV2TgCEfkKN/p3GlARet5XIk0LSwQ1U0WFMnPl5n17C+MXrmdXeYUdXzCmilRlIhinqsdVWWSVYIkgNyQ6vjCgs9tjsOMLxgRz0McIwvxZRH4DDMdddQwAVQ10TQJjgkr2+MLpR7bg9CNa2shmYw5SkERwNPAD3JXFQl1DoSuNGZMyjesWcubRrTjz6FaAO77w2fy1fDp3LaPmlPHm5OUc2bohtw3uyomdi20vwZhKCjqyuFu66wuFs64hE2lvhfLuV8t5ePgclm3YwfEdm3Hb4C70sJHNxuxTlSOLvwIaH3xIxlSdvFrC+T3aMuLWgdx9zhHMWb2F8x/7jGtfnMi8NVsTv4ExZp8gXUMtgNki8iUHHiNI2+mjxsRSlJ/H5f0P5cLe7Xj604U8+cl8hs9cxXd7t+PGUzvTqlGdTIdoTNYL0jU0MNrzdvqoyUbrtu7i0ZHzeOmLJYjA5f06cN2gTjSuW5jp0IxJuyq9HkGmWSIwyVq6fjuPfDSHtyYvp35RPtcO7MSV/Q+lTmFepkMzJm2qchzBFvZfo7gQKAC2qWrDg44yIEsEprK+XrWFBz+YzUez1tC8QRE3nXoYF/dpZ6ecmpxQZQeLVbWBqjb0t9rABbgL1BiT9bq0bMBTl/Xh9WuP55BmdbnzrWlc99JEK5NtTJikx+2r6tvYGAJTzfTu0JTXfnw8d511OB/NWsP5fxvLwrXbMh2WMVkh4VlDYdclAJc4erO/q8iYakNEuPqEjhzRqiHXvzyJIY+O4S8X9+Ckrs0zHZoxGRVkj+CcsNsZwBbg3FQGZUwq9Sst5r0bBtC+aV2ufP5LHv14rl1z2eS0hHsEdl0CUxO1bVKX16/txx1vTuWh4XOYvnwzD333GCt/bXJSvEtV/jpOO1XVe1MQjzFpU6cwj0cu6s5RbRrxx//N5vy/jeXJH/bm0OJ6mQ7NmLSK1zW0LcoN4CpgaIrjMiYtQscNXrzyWNZu3cWQR8cw0lc5NSZXxEwEqvpw6AY8CdQBrgBeBTqmKT5j0qJfaTHv/nQA7ZrYcQOTe+IeLBaRpiLyO2Aqrhupp6oOVdWEm0wi0k5ERorILBGZISI3hr3nhyIy1/+1cpEmK7RrWpc3ruvHkGNa89DwOVz3z0k23sDkhJiJQEQeBL7EnSV0tKreraobknjvcuBWVT0c6AtcLyJHALcDI1S1MzDCPzYmK9QpzONPF3XnrrMOZ/jMVTbewOSEmCUmRKQCV220nAPHDQjuYHFSJSZE5B3ciORHgUGqulJEWgGjVLVLvLZWYsJkwmfz1nL9y5Mor1Au6NmW/qXF9O3YlAa1CzIdmjGBZFXRORHpAHwCHAUsUdXGYa9tUNVvdA+JyDXANQDt27fvtXjx4pTHaUykpeu3c897Mxk7by079uwlr5bQvV1j+pe66yd3b9eYwvykB+gbkxZZkwhEpD4wGvi9qr4pIhuDJIJwtkdgMm1X+V4mL9nImLlrGTNvLVOXbaRCoW5hHn07NtuXGA5rUd8umWmyRlVevP5ggigA3gBeUtU3/dOrRaRVWNeQnatnsl5Rvlvh9+3YjJ+f0YVNO/bw+fx1jJ23lrHz1vKxP+W0pEERA0qL9yWGlo1qZzhyYxJLWSIQt1n0NDBLVf8v7KV3gcuA+/zfd1IVgzGp0qhOAYOPasngo1oCsHzjDsb6vYVP5pTx1uTlAJQ2r78vMdjxBZOtUtY1JCIDgE+BaUCFf/pOYBzwGtAeWAJ8R1XXx3sv6xoy1UlFhTJ71RbGznOJYdzCdezcU/GN4ws92jemIM+OL5jUyZpjBFXBEoGpznaV72XS4o37EkPo+EK9wjyO88cXTuhcTOfmdnzBVC1LBMZkqcjjCwv8OIWSBkWc36MNPz+9i52JZKpEVhwsNsZ8U6zjCyO/XsOTnyxg4uINPH5pT5o3tAPNJj1ss8OYDGvTuA7f7dOOx7/fi799ryczV2zm7L+OYeLiZAbyG1N5lgiMySJndWvFW9f3o3ZBHhc/+TmvjF+S6ZBMDrBEYEyW6dqyIe/+tD/Hdyrmjjencedb09hdXpG4oTGVZInAmCzUuG4hz17eh+sGdeLlcUu45B9fsGbzzkyHZWooSwTGZKm8WsLQwV159Hs9mLliM+c8OoZJS+y4gal6lgiMyXJnd2vNmz/pR1F+Hhf//QteteMGpopZIjCmGji8lTtucFzHptz+5jR+accNTBWyRGBMNdG4biHPXXEs1w7sxEt23MBUIUsExlQjebWE28888LiBjTcwB8tGFhtTDZ3drTWdSurz4xcncsHjn9GxpB4nhKqcdmpGQ6tyapJgtYaMqcY2bd/DvycudVVOF6zfdxW1Y9o22lf+ukf7Jla7KEdZ0TljckzoKmqhKqdfLd1/FbXjDm3qyl93LqZLiwZW5TRHWCIwJsdt2rGHLxas25cYFpS5KqfF9YvoX9qMAT4xtGpUJ8ORmlSx6qPG5LhGdQo448iWnHGkq3K6YuMOxvjS12PnreWdKSsA7PiCsT0CY3KRasRV1Oz4Qo1kXUPGmMB2l1cwacmGuMcX+pcW0zJN10ioU5hH7YK8tMyrJrNEYIyptAOOL8zdfxW1dMn313ce0Nld3/mYdnZ958qwRGCMqTLLN+7gi/nr2LJzT1rmt2bLLsbOW8vU5ZtQhfpF+fTt6M98Ki2m1K7vHIgdLDbGVJk2jetwQa+2aZ/vxu27+WLBOj6d6w5wfzRrDQAtGhbtSwr9S4tpYZf1PCi2R2CMqTaWrt++7zjGZ/PXsX7bbgAOa1F/X2I4rmMz6hfZNi5Y15AxpoarqFBmrty8LzGMX7ieXeUV5NcSerRvTP/SYk7oXEy3trl7fMESgTEmp+zcs5dJizfsGysR7fjCCZ2L6VSSO8cX7BiBMSan1C7Io19pMf1KiwF3fOHz+ev2JYZYxxeK6xelJb5aQtYmINsjMMbkhFjHF9LlyNYNef3aftQpTN/4iIzvEYjIM8DZwBpVPco/1xT4F9ABWAR8V1WtmLoxJuXaNa3Lxce25+Jj21NRocxatZkvFqxn267ylM97265y/v7JAv48Yi63n9k15fNLViq7hp4DHgVeCHvudmCEqt4nIrf7x0NTGIMxxnxDrVrCka0bcWTrRmmb54btu3nq0wWc16M1XVs2TNt8g0jZoXRV/QRYH/H0ucDz/v7zwHmpmr8xxmSTO848nIZ1CrjjzWlUVGRXl3y6z6lqoaorAfzf5rEmFJFrRGSCiEwoKytLW4DGGJMKTeoVctdZhzN5yUZeHr8k0+EcIGtPrlXVJ1W1t6r2LikpyXQ4xhhz0M7v0YZ+nZpx/7DZrNm8M9Ph7JPuRLBaRFoB+L9r0jx/Y4zJGBHhd+cdxa7yCn77/sxMh7NPuhPBu8Bl/v5lwDtpnr8xxmRUx5L6/PSkUt6fupKRX2fHtnDKEoGIvAJ8DnQRkWUichVwH3CaiMwFTvOPjTEmp/x4YEc6ldTjV29PZ8fuvZkOJ6VnDV2iqq1UtUBV26rq06q6TlVPUdXO/m/kWUXGGFPjFeXn8Yfzj2bZhh38ecTcTIeTvQeLjTGmJjuuYzO+27stT326gNmrNmc0FksExhiTIdkytsASgTHGZEi2jC2wRGCMMRmUDWMLLBEYY0wGZcPYAksExhiTYZkeW2CJwBhjskAmxxZYIjDGmCyQybEFlgiMMSZLZGpsgSUCY4zJIpkYW2CJwBhjskgmxhZYIjDGmCyT7rEFlgiMMSbLhMYW9DqkCbvKK1I+v1RevN4YY0wldSypz3NXHJuWedkegTHG5DhLBMYYk+MsERhjTI6zRGCMMTnOEoExxuQ4SwTGGJPjLBEYY0yOs0RgjDE5TlQzd8HkoESkDFhcyebFwNo0tEnnvLK5TTrnZZ+p8m3SOa9sbpPOeaXzM4UcoqolCadS1Rp9Ayako00655XNbbI9PvtM1SM+Ww4H95mSvVnXkDHG5DhLBMYYk+NyIRE8maY26ZxXNrdJ57zsM1W+TTrnlc1t0jmvdH6mpFSLg8XGGGNSJxf2CIwxxsRhicAYY3JcjU4EIjJYRL4WkXkicnuA6Z8RkTUiMj2JebQTkZEiMktEZojIjQHa1BaR8SLylW9zTxLzyxORySLyfhJtFonINBGZIiITArZpLCKvi8hs/9mOTzB9F//+odtmEbkpwHxu9stguoi8IiK1A8Z3o28zI9Z8ov0/RaSpiHwoInP93yYB2nzHz6dCRHonMa8H/fKbKiJviUjjAG3u9dNPEZHhItI6UZuw134uIioixQHmc7eILA/7f30ryGfyz9/gf1czROSBAPP6V9h8FonIlABtuovIF6HvrIgcG6DNMSLyuf+uvyciDSPaRP2txvtOxGkT9zsRp13M70ScNnG/E1UiHeeoZuIG5AHzgY5AIfAVcESCNicCPYHpScynFdDT328AzAkwHwHq+/sFwDigb8D53QK8DLyfRIyLgOIkl9/zwNX+fiHQOMllvwo3mCXedG2AhUAd//g14PIA738UMB2oi7vK3kdA5yD/T+AB4HZ//3bg/gBtDge6AKOA3kG/O8DpQL6/f3/AeTUMu/8z4Ikg31GgHfABbuBlcYD53A38PNnfA3CSX95F/nHzZH5DwMPArwPMZzhwpr//LWBUgDZfAgP9/SuBeyPaRP2txvtOxGkT9zsRp13M70ScNnG/E1Vxq8l7BMcC81R1garuBl4Fzo3XQFU/AdYnMxNVXamqk/z9LcAs3AouXhtV1a3+YYG/JTxqLyJtgbOAp5KJMVl+S+pE4GkAVd2tqhuTeItTgPmqGmQ0eD5QR0TycSv2FQHaHA58oarbVbUcGA2cHzlRjP/nubgkh/97XqI2qjpLVb+OF1CMdsN9fABfAG0DtNkc9rAeEd+LON/RR4DbIqdP0CauGO2uA+5T1V1+mjVB5yUiAnwXeCVAGwVCW/SNiPhexGjTBfjE3/8QuCCiTazfaszvRKw2ib4TcdrF/E7EaRP3O1EVanIiaAMsDXu8jAQr6IMlIh2AHrgt/ETT5vld5DXAh6qasA3wJ9yPPdmrWSswXEQmisg1AabvCJQBz4rrhnpKROolMb+LifixRw1KdTnwELAEWAlsUtXhAd5/OnCiiDQTkbq4LcZ2AWNroaor/fxXAs0DtjtYVwL/CzKhiPxeRJYClwK/DjD9EGC5qn6VZEw/9V0Oz0R2kcVxGHCCiIwTkdEi0ieJ+Z0ArFbVuQGmvQl40C+Hh4A7ArSZDgzx979DnO9ExG810Hcimd93wHYxvxORbZL9TiSrJicCifJcys6VFZH6wBvATREZPCpV3auq3XFbBMeKyFEJ3v9sYI2qTqxEeP1VtSdwJnC9iJyYYPp83G7346raA9iG22VOSEQKcT/GfweYtglua+xQoDVQT0S+n6idqs7C7VZ/CAzDdfuVx22UQSLyS1x8LwWZXlV/qart/PQ/TfDedYFfkvzK4XGgE9Adl4QfDtguH2gC9AV+Abzmt/SDuIQAGwjedcDNfjncjN87TeBK3Pd7Iq5rZXe0iZL9rVa2Tbx28b4T0dok852ojJqcCJZx4BZBW4J1OyRNRApw/7iXVPXNZNr6LpdRwOAEk/YHhojIIlw318ki8s+A81jh/64B3sJ1m8WzDFgWtpfyOi4xBHEmMElVVweY9lRgoaqWqeoe4E2gX5CZqOrTqtpTVU/EdREE2coEWC0irQD83zUJpj8oInIZcDZwqfpO3iS8TET3RhSdcIn0K//daAtMEpGW8Rqp6mq/MVIB/IPE34mQZcCbvntzPG7vtDhBG3zX37eBfwWcz2W47wO4jYqE8anqbFU9XVV74RLO/ChxRPutxv1OVPb3HatdvO9EgHkF+U4krSYngi+BziJyqN9KvRh4t6pn4reGngZmqer/BWxTEjpbQETq4FaIs+O1UdU7VLWtqnbAfZaPVTXh1rOI1BORBqH7uINVcc+KUtVVwFIR6eKfOgWYmWheXjJbfUuAviJS1y/HU3D9ogmJSHP/tz1uBRN0nu/iVjL4v+8EbJc0ERkMDAWGqOr2gG06hz0cQuLvxTRVba6qHfx3YxnugOOqBPNpFfbwfBJ8J8K8DZzs3+Mw3IkEQapjngrMVtVlAeezAhjo759MgEQf9p2oBdwFPBHxeqzfaszvRGV+3/HaxftOxGmT1HeiUiKPHtekG67veA5uy+CXAaZ/BbebvAf3g7oqQJsBuC6nqcAUf/tWgjbdgMm+zXQizqIIMM9BBDxrCNff/5W/zQiyHHy77sAEH+PbQJMAbeoC64BGSXyWe/wXezrwIv5slADtPsUlp6+AU4L+P4FmwAjcimUE0DRAm/P9/V3AauCDgPOahztOFfpeRJ4BFK3NG35ZTAXewx0sDPwdJcoZYjHm8yIwzc/nXaBVwM9UCPzTxzgJODlIfMBzwLVJ/J8GABP9/3cc0CtAmxtxv/c5wH34ygmJfqvxvhNx2sT9TsRpF/M7EadN3O9EVdysxIQxxuS4mtw1ZIwxJgBLBMYYk+MsERhjTI6zRGCMMTnOEoExxuQ4SwQ5QkRaisirIjJfRGaKyH/9eeBZSURai8jr/n53iVIdM0F7EZG/iKs8O1VEvjEgTlyF1Z+EPR4kSVR1rSoisjXANJf6zzFVRD4TkWMiXk+6Kq1v11VcVcvJItIp4rU7A7Q/YBlGeT1m5VuJU8XUpJclghzgB6q8havg2ElVjwDuBFpkNrLYVHWFql7oH3bHnU+djDOBzv52Da6kQqTGQMyVWCwikpdsmyqwEFdZsxtwL9+8hOGNBByMF+E84B1V7aGqkSNxEyYCgi3Dk1S1u6ruK9csIifhyot0U9UjcfWETKZU9cAEu2XfDTcy85MYrwnwIG7AyjTgIv/8IFxVz9fYP0DnUmC8n66Tn+453Ep2JLAANxr0GdxK6bmw+WwNu39h6DXf/i/AZ779hf75Dj6mQtwI5DLcAJuLcAN/Svx0tXCDdCIHUf0duCTs8ddEDJrClerY4d/3Qf+ZR+FKaszG1XUJjbVZhKvnMwY3srs7rnrkVFySbeKnG4UvS4wrvbDI36/rl+VUXJmFcWHTbQV+jxs89QWuCFq8/2cTXJG50OO2uIFQJxNjoGG0eHHJdRWwHBgZMf19wF6/bF7yz93i/yfTcXVwvrEMo8x3UeT/xj//GnBqpn8bdvP/j0wHYLc0/JNdDfNHYrx2Aa54Wx5uD2EJri76IGCjv1/kVxb3+DY3An/y95/zKwPBbeFtBo72K+iJQHc/XbxE8G8//RG40uHgE4G/fznwaFj734StiE4H3ojyud4HBoQ9HkFE3fjwefjHg4BNfsVaC/g89B5+hXZb2LRT2V/7/rdhy2MU0RPBz4G/+/tH4QqOhaZT4Bx//wHgrgT/z58DT4U9fh3oRZwR53HivZsY1yWI+J/1wm0A1APq40ap94hchlHeYyFuBPJE4Jqw56fgRpWPw21w9Mn07ySXb9Y1ZAYAr6grQLYa/6P0r32prkb6LlyZjlCJ6Gm4FUDIe+p+3dNwZYanqStmNiNiuljeVtUKVZ1JsO6qZ4Af+vtXAs9Gmaay1WfHq+oyH/8UDoz/XwAi0gh3oZ7R/vnncddviGcALmGiqqFyASG7cYkL3AqzAzH4LpWrcPVqAlWlrWS80eJ/S1W3qbuWxpu4stKJxKp8ezBVTE0Vs0SQG2bgtuiiiffj2xV2vyLscQXuhxw5XUWUNqHpwlfCkZejDG+TcGWgqktxFSNPBo4jek33ylafDY9lLwd+zm0B2pez/3cV/jnjfa49PpFGm+f+NxDphrso0bmqus4/XemqtEmq1EpaY1e+rVQVU5Malghyw8dAkYj8KPSEiPQRkYG4Kzpd5M86KcFtKY5PQQyrReRwXxnyG1cTS2ALrr58uKdwxc9eU9W9Udq8C/zQnz3UF3fRm5UB3jchVd0EbBCR0BbxD3B7UuC6kEJJ98KwZmNwV+dCRI7AdZ8F5qusvgn8QFXnhMWSsCptgnjj2ePLIoP7npznK8XWw/0PPyXOMkxQ+bayVUxNClgiyAF+a/N84DR/+ugMXN/wCtxW2lTcgcqPcf3gcUsYV9LtuO6Pj3FVI5MxEjjCn4J4kX/uXVxfdbRuIYD/4g4+z8PV2//GmS1+q3qsiEwXkQeTjOky3BW0puIOxP7WP/8QcJ2IfMaBW7iPASV++qG4Zb4pifn9Glcl87Fop2IeRLzxPAlMFZGX1F1C8TncRsI43DGKyQmWYQtgjIh85dv9R1WH+deeATqKu/j8q8Blqqr+tOH/JvnZzEGy6qOmWhKR3rgD4EH6qTPOn3JaoKo7/fn6I4DD1F1P25iMitoXaUw2E5HbcZcyvDTTsSShLjDSd7UIcJ0lAZMtbI/AGGNynB0jMMaYHGeJwBhjcpwlAmOMyXGWCIwxJsdZIjDGmBz3/4mrQ80+zjVfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished compute_influence_distribution after 0:00:00.223850.\n",
      "Graph: Name: politism.se\n",
      "Type: Graph\n",
      "Number of nodes: 5669\n",
      "Number of edges: 97347\n",
      "Average degree:  34.3436\n",
      "graph density: 0.0060592136933611\n",
      "modularity: 0.37990391288592884\n",
      "connected components: 33\n",
      "99.0% of words in largest connected component\n",
      "communities (louvain): 56\n",
      "words in top community: 708\n",
      "12.5% of words in largest community (louvain)\n",
      "Average Edges per communities: 1738.3392857142858\n",
      "Average Nodes per communities: 101.23214285714286\n",
      "Finished compute_community_structure after 0:00:06.679040.\n",
      "Stored graph in gexf-file politism.gexf.\n",
      "Finished full process for politism.se after 0:06:11.365867 in total.\n",
      "\n",
      "jardenberg.se\n",
      "2249\n",
      "Finished preprocessing.\n",
      "Finished creating graph after 0:00:02.172376\n",
      "Finished compute_communities after 0:00:09.213153\n",
      "Finished compute_betweeness_centrality\n",
      "Finished compute_overall_graph_stats.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished compute_influence_distribution after 0:00:00.196556.\n",
      "Graph: Name: jardenberg.se\n",
      "Type: Graph\n",
      "Number of nodes: 7373\n",
      "Number of edges: 160722\n",
      "Average degree:  43.5975\n",
      "graph density: 0.005913924329350855\n",
      "modularity: 0.312254981717057\n",
      "connected components: 57\n",
      "99.0% of words in largest connected component\n",
      "communities (louvain): 75\n",
      "words in top community: 1775\n",
      "24.1% of words in largest community (louvain)\n",
      "Average Edges per communities: 2142.96\n",
      "Average Nodes per communities: 98.30666666666667\n",
      "Finished compute_community_structure after 0:00:11.665727.\n",
      "Stored graph in gexf-file jardenberg.gexf.\n",
      "Finished full process for jardenberg.se after 0:12:38.995832 in total.\n",
      "\n",
      "avpixlat.info\n",
      "4627\n",
      "Finished preprocessing.\n",
      "Finished creating graph after 0:00:01.336267\n",
      "Finished compute_communities after 0:00:10.673999\n",
      "Finished compute_betweeness_centrality\n",
      "Finished compute_overall_graph_stats.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished compute_influence_distribution after 0:00:00.219826.\n",
      "Graph: Name: avpixlat.info\n",
      "Type: Graph\n",
      "Number of nodes: 6626\n",
      "Number of edges: 96232\n",
      "Average degree:  29.0468\n",
      "graph density: 0.004384420436359908\n",
      "modularity: 0.5207069193389083\n",
      "connected components: 98\n",
      "98.0% of words in largest connected component\n",
      "communities (louvain): 114\n",
      "words in top community: 1450\n",
      "21.9% of words in largest community (louvain)\n",
      "Average Edges per communities: 844.140350877193\n",
      "Average Nodes per communities: 58.12280701754386\n",
      "Finished compute_community_structure after 0:00:11.827357.\n",
      "Stored graph in gexf-file avpixlat.gexf.\n",
      "Finished full process for avpixlat.info after 0:07:48.178701 in total.\n",
      "\n",
      "erixon.com\n",
      "3330\n",
      "Finished preprocessing.\n",
      "Finished creating graph after 0:00:02.148945\n",
      "Finished compute_communities after 0:00:24.077950\n",
      "Finished compute_betweeness_centrality\n",
      "Finished compute_overall_graph_stats.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished compute_influence_distribution after 0:00:00.362007.\n",
      "Graph: Name: erixon.com\n",
      "Type: Graph\n",
      "Number of nodes: 9039\n",
      "Number of edges: 239487\n",
      "Average degree:  52.9897\n",
      "graph density: 0.005862990844351029\n",
      "modularity: 0.2880713325480211\n",
      "connected components: 22\n",
      "99.7% of words in largest connected component\n",
      "communities (louvain): 44\n",
      "words in top community: 962\n",
      "10.6% of words in largest community (louvain)\n",
      "Average Edges per communities: 5442.886363636364\n",
      "Average Nodes per communities: 205.4318181818182\n",
      "Finished compute_community_structure after 0:00:26.641361.\n",
      "Stored graph in gexf-file erixon.gexf.\n",
      "Finished full process for erixon.com after 1 day, 17:38:03.122895 in total.\n",
      "\n",
      "www.erixon.com\n",
      "32\n",
      "Finished preprocessing.\n",
      "Finished creating graph after 0:00:00.043044\n",
      "Finished compute_communities after 0:00:00.222068\n",
      "Finished compute_betweeness_centrality\n",
      "Finished compute_overall_graph_stats.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished compute_influence_distribution after 0:00:00.184673.\n",
      "Graph: Name: www.erixon.com\n",
      "Type: Graph\n",
      "Number of nodes: 475\n",
      "Number of edges: 5855\n",
      "Average degree:  24.6526\n",
      "graph density: 0.052009771263602046\n",
      "modularity: 0.7581376719266381\n",
      "connected components: 9\n",
      "92.8% of words in largest connected component\n",
      "communities (louvain): 18\n",
      "words in top community: 69\n",
      "14.5% of words in largest community (louvain)\n",
      "Average Edges per communities: 325.27777777777777\n",
      "Average Nodes per communities: 26.38888888888889\n",
      "Finished compute_community_structure after 0:00:00.221546.\n",
      "Stored graph in gexf-file www.erixon.gexf.\n",
      "Finished full process for www.erixon.com after 0:00:01.948779 in total.\n",
      "\n",
      "ladydahmer.nu\n",
      "548\n",
      "Finished preprocessing.\n",
      "Finished creating graph after 0:00:01.346472\n",
      "Finished compute_communities after 0:00:06.265864\n",
      "Finished compute_betweeness_centrality\n",
      "Finished compute_overall_graph_stats.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished compute_influence_distribution after 0:00:00.215991.\n",
      "Graph: Name: ladydahmer.nu\n",
      "Type: Graph\n",
      "Number of nodes: 4824\n",
      "Number of edges: 74399\n",
      "Average degree:  30.8454\n",
      "graph density: 0.006395470982911141\n",
      "modularity: 0.31139693652926675\n",
      "connected components: 57\n",
      "98.5% of words in largest connected component\n",
      "communities (louvain): 75\n",
      "words in top community: 608\n",
      "12.6% of words in largest community (louvain)\n",
      "Average Edges per communities: 991.9866666666667\n",
      "Average Nodes per communities: 64.32\n",
      "Finished compute_community_structure after 0:00:09.141768.\n",
      "Stored graph in gexf-file ladydahmer.gexf.\n",
      "Finished full process for ladydahmer.nu after 0:04:27.852901 in total.\n",
      "\n",
      "thoralf.bloggplatsen.se\n",
      "856\n",
      "Finished preprocessing.\n",
      "Finished creating graph after 0:00:08.935417\n",
      "Finished compute_communities after 0:00:40.837355\n",
      "Finished compute_betweeness_centrality\n",
      "Finished compute_overall_graph_stats.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished compute_influence_distribution after 0:00:00.255545.\n",
      "Graph: Name: thoralf.bloggplatsen.se\n",
      "Type: Graph\n",
      "Number of nodes: 15883\n",
      "Number of edges: 404920\n",
      "Average degree:  50.9878\n",
      "graph density: 0.003210417368291363\n",
      "modularity: 0.2610784067225997\n",
      "connected components: 59\n",
      "99.5% of words in largest connected component\n",
      "communities (louvain): 74\n",
      "words in top community: 3292\n",
      "20.7% of words in largest community (louvain)\n",
      "Average Edges per communities: 5471.891891891892\n",
      "Average Nodes per communities: 214.63513513513513\n",
      "Finished compute_community_structure after 0:00:48.347987.\n",
      "Stored graph in gexf-file thoralf.bloggplatsen.gexf.\n",
      "Finished full process for thoralf.bloggplatsen.se after 1:34:47.930837 in total.\n",
      "\n",
      "kulturellammunition.blogspot.com\n",
      "604\n",
      "Finished preprocessing.\n",
      "Finished creating graph after 0:00:03.097333\n",
      "Finished compute_communities after 0:00:36.619252\n",
      "Finished compute_betweeness_centrality\n",
      "Finished compute_overall_graph_stats.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished compute_influence_distribution after 0:00:00.211508.\n",
      "Graph: Name: kulturellammunition.blogspot.com\n",
      "Type: Graph\n",
      "Number of nodes: 10517\n",
      "Number of edges: 373367\n",
      "Average degree:  71.0026\n",
      "graph density: 0.006751860714343453\n",
      "modularity: 0.36024309262576243\n",
      "connected components: 67\n",
      "99.0% of words in largest connected component\n",
      "communities (louvain): 101\n",
      "words in top community: 4250\n",
      "40.4% of words in largest community (louvain)\n",
      "Average Edges per communities: 3696.70297029703\n",
      "Average Nodes per communities: 104.12871287128714\n",
      "Finished compute_community_structure after 0:00:24.734153.\n",
      "Stored graph in gexf-file kulturellammunition.blogspot.gexf.\n",
      "Finished full process for kulturellammunition.blogspot.com after 0:38:05.797722 in total.\n",
      "\n",
      "motpol.wordpress.com\n",
      "46\n",
      "Finished preprocessing.\n",
      "Finished creating graph after 0:00:00.091719\n",
      "Finished compute_communities after 0:00:00.238878\n",
      "Finished compute_betweeness_centrality\n",
      "Finished compute_overall_graph_stats.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished compute_influence_distribution after 0:00:00.186641.\n",
      "Graph: Name: motpol.wordpress.com\n",
      "Type: Graph\n",
      "Number of nodes: 519\n",
      "Number of edges: 4713\n",
      "Average degree:  18.1618\n",
      "graph density: 0.03506148592853795\n",
      "modularity: 0.6743602501662564\n",
      "connected components: 12\n",
      "92.7% of words in largest connected component\n",
      "communities (louvain): 24\n",
      "words in top community: 73\n",
      "14.1% of words in largest community (louvain)\n",
      "Average Edges per communities: 196.375\n",
      "Average Nodes per communities: 21.625\n",
      "Finished compute_community_structure after 0:00:00.290165.\n",
      "Stored graph in gexf-file motpol.wordpress.gexf.\n",
      "Finished full process for motpol.wordpress.com after 0:00:02.176982 in total.\n",
      "\n",
      "enkelpoesi.wordpress.com\n",
      "125\n",
      "Finished preprocessing.\n",
      "Finished creating graph after 0:00:00.435598\n",
      "Finished compute_communities after 0:00:01.240675\n",
      "Finished compute_betweeness_centrality\n",
      "Finished compute_overall_graph_stats.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished compute_influence_distribution after 0:00:00.170600.\n",
      "Graph: Name: enkelpoesi.wordpress.com\n",
      "Type: Graph\n",
      "Number of nodes: 628\n",
      "Number of edges: 58092\n",
      "Average degree: 185.0064\n",
      "graph density: 0.29506597994697226\n",
      "modularity: 0.24306402478043587\n",
      "connected components: 2\n",
      "99.2% of words in largest connected component\n",
      "communities (louvain): 10\n",
      "words in top community: 121\n",
      "19.3% of words in largest community (louvain)\n",
      "Average Edges per communities: 5809.2\n",
      "Average Nodes per communities: 62.8\n",
      "Finished compute_community_structure after 0:00:01.307648.\n",
      "Stored graph in gexf-file enkelpoesi.wordpress.gexf.\n",
      "Finished full process for enkelpoesi.wordpress.com after 0:00:17.739219 in total.\n",
      "\n",
      "a4jesus.blogspot.com\n",
      "107\n",
      "Finished preprocessing.\n",
      "Finished creating graph after 0:00:00.788540\n",
      "Finished compute_communities after 0:00:01.869001\n",
      "Finished compute_betweeness_centrality\n",
      "Finished compute_overall_graph_stats.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished compute_influence_distribution after 0:00:00.159845.\n",
      "Graph: Name: a4jesus.blogspot.com\n",
      "Type: Graph\n",
      "Number of nodes: 1024\n",
      "Number of edges: 30996\n",
      "Average degree:  60.5391\n",
      "graph density: 0.05917796920821115\n",
      "modularity: 0.45709482281531605\n",
      "connected components: 3\n",
      "99.8% of words in largest connected component\n",
      "communities (louvain): 8\n",
      "words in top community: 741\n",
      "72.4% of words in largest community (louvain)\n",
      "Average Edges per communities: 3874.5\n",
      "Average Nodes per communities: 128.0\n",
      "Finished compute_community_structure after 0:00:01.550920.\n",
      "Stored graph in gexf-file a4jesus.blogspot.gexf.\n",
      "Finished full process for a4jesus.blogspot.com after 0:00:17.183062 in total.\n",
      "\n",
      "attitude4jesus.blogspot.com\n",
      "113\n",
      "Finished preprocessing.\n",
      "Finished creating graph after 0:00:00.320669\n",
      "Finished compute_communities after 0:00:00.614343\n",
      "Finished compute_betweeness_centrality\n",
      "Finished compute_overall_graph_stats.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished compute_influence_distribution after 0:00:00.185303.\n",
      "Graph: Name: attitude4jesus.blogspot.com\n",
      "Type: Graph\n",
      "Number of nodes: 824\n",
      "Number of edges: 11715\n",
      "Average degree:  28.4345\n",
      "graph density: 0.03454977645129705\n",
      "modularity: 0.5311232732552512\n",
      "connected components: 12\n",
      "98.4% of words in largest connected component\n",
      "communities (louvain): 26\n",
      "words in top community: 113\n",
      "13.7% of words in largest community (louvain)\n",
      "Average Edges per communities: 450.5769230769231\n",
      "Average Nodes per communities: 31.692307692307693\n",
      "Finished compute_community_structure after 0:00:00.637085.\n",
      "Stored graph in gexf-file attitude4jesus.blogspot.gexf.\n",
      "Finished full process for attitude4jesus.blogspot.com after 0:00:06.789100 in total.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\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>avDegree</th>\n",
       "      <th>avEdgesPerCom</th>\n",
       "      <th>avNodesPerCom</th>\n",
       "      <th>connComp</th>\n",
       "      <th>density</th>\n",
       "      <th>domain</th>\n",
       "      <th>edges</th>\n",
       "      <th>graphFile</th>\n",
       "      <th>louvainComms</th>\n",
       "      <th>modularity</th>\n",
       "      <th>name</th>\n",
       "      <th>nodes</th>\n",
       "      <th>nrOfPosts</th>\n",
       "      <th>prcOfWordsInLargestConnComp</th>\n",
       "      <th>sentences</th>\n",
       "      <th>topnBCNodesCommDistrEntropy</th>\n",
       "      <th>total_words</th>\n",
       "      <th>unique_words</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34.343623</td>\n",
       "      <td>1738.339286</td>\n",
       "      <td>101.232143</td>\n",
       "      <td>33</td>\n",
       "      <td>0.006059</td>\n",
       "      <td>politism.se</td>\n",
       "      <td>97347</td>\n",
       "      <td>politism.gexf</td>\n",
       "      <td>56</td>\n",
       "      <td>0.379904</td>\n",
       "      <td>politism.se</td>\n",
       "      <td>5669</td>\n",
       "      <td>1832</td>\n",
       "      <td>99.047451</td>\n",
       "      <td>1452</td>\n",
       "      <td>0.950353</td>\n",
       "      <td>28299</td>\n",
       "      <td>9028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>43.597450</td>\n",
       "      <td>2142.960000</td>\n",
       "      <td>98.306667</td>\n",
       "      <td>57</td>\n",
       "      <td>0.005914</td>\n",
       "      <td>jardenberg.se</td>\n",
       "      <td>160722</td>\n",
       "      <td>jardenberg.gexf</td>\n",
       "      <td>75</td>\n",
       "      <td>0.312255</td>\n",
       "      <td>jardenberg.se</td>\n",
       "      <td>7373</td>\n",
       "      <td>2249</td>\n",
       "      <td>99.009901</td>\n",
       "      <td>3090</td>\n",
       "      <td>0.850365</td>\n",
       "      <td>53884</td>\n",
       "      <td>12392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29.046785</td>\n",
       "      <td>844.140351</td>\n",
       "      <td>58.122807</td>\n",
       "      <td>98</td>\n",
       "      <td>0.004384</td>\n",
       "      <td>avpixlat.info</td>\n",
       "      <td>96232</td>\n",
       "      <td>avpixlat.gexf</td>\n",
       "      <td>114</td>\n",
       "      <td>0.520707</td>\n",
       "      <td>avpixlat.info</td>\n",
       "      <td>6626</td>\n",
       "      <td>4627</td>\n",
       "      <td>97.992756</td>\n",
       "      <td>2546</td>\n",
       "      <td>0.865366</td>\n",
       "      <td>37735</td>\n",
       "      <td>11443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>52.989711</td>\n",
       "      <td>5442.886364</td>\n",
       "      <td>205.431818</td>\n",
       "      <td>22</td>\n",
       "      <td>0.005863</td>\n",
       "      <td>erixon.com</td>\n",
       "      <td>239487</td>\n",
       "      <td>erixon.gexf</td>\n",
       "      <td>44</td>\n",
       "      <td>0.288071</td>\n",
       "      <td>erixon.com</td>\n",
       "      <td>9039</td>\n",
       "      <td>3330</td>\n",
       "      <td>99.701294</td>\n",
       "      <td>2572</td>\n",
       "      <td>0.931272</td>\n",
       "      <td>57458</td>\n",
       "      <td>15768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.652632</td>\n",
       "      <td>325.277778</td>\n",
       "      <td>26.388889</td>\n",
       "      <td>9</td>\n",
       "      <td>0.052010</td>\n",
       "      <td>www.erixon.com</td>\n",
       "      <td>5855</td>\n",
       "      <td>www.erixon.gexf</td>\n",
       "      <td>18</td>\n",
       "      <td>0.758138</td>\n",
       "      <td>www.erixon.com</td>\n",
       "      <td>475</td>\n",
       "      <td>32</td>\n",
       "      <td>92.842105</td>\n",
       "      <td>49</td>\n",
       "      <td>0.959545</td>\n",
       "      <td>1211</td>\n",
       "      <td>721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>30.845357</td>\n",
       "      <td>991.986667</td>\n",
       "      <td>64.320000</td>\n",
       "      <td>57</td>\n",
       "      <td>0.006395</td>\n",
       "      <td>ladydahmer.nu</td>\n",
       "      <td>74399</td>\n",
       "      <td>ladydahmer.gexf</td>\n",
       "      <td>75</td>\n",
       "      <td>0.311397</td>\n",
       "      <td>ladydahmer.nu</td>\n",
       "      <td>4824</td>\n",
       "      <td>548</td>\n",
       "      <td>98.528192</td>\n",
       "      <td>2507</td>\n",
       "      <td>0.911128</td>\n",
       "      <td>40848</td>\n",
       "      <td>9423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>50.987849</td>\n",
       "      <td>5471.891892</td>\n",
       "      <td>214.635135</td>\n",
       "      <td>59</td>\n",
       "      <td>0.003210</td>\n",
       "      <td>thoralf.bloggplatsen.se</td>\n",
       "      <td>404920</td>\n",
       "      <td>thoralf.bloggplatsen.gexf</td>\n",
       "      <td>74</td>\n",
       "      <td>0.261078</td>\n",
       "      <td>thoralf.bloggplatsen.se</td>\n",
       "      <td>15883</td>\n",
       "      <td>856</td>\n",
       "      <td>99.515205</td>\n",
       "      <td>18093</td>\n",
       "      <td>0.791913</td>\n",
       "      <td>276135</td>\n",
       "      <td>33006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>71.002567</td>\n",
       "      <td>3696.702970</td>\n",
       "      <td>104.128713</td>\n",
       "      <td>67</td>\n",
       "      <td>0.006752</td>\n",
       "      <td>kulturellammunition.blogspot.com</td>\n",
       "      <td>373367</td>\n",
       "      <td>kulturellammunition.blogspot.gexf</td>\n",
       "      <td>101</td>\n",
       "      <td>0.360243</td>\n",
       "      <td>kulturellammunition.blogspot.com</td>\n",
       "      <td>10517</td>\n",
       "      <td>604</td>\n",
       "      <td>98.982600</td>\n",
       "      <td>1940</td>\n",
       "      <td>0.571431</td>\n",
       "      <td>49349</td>\n",
       "      <td>15803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>18.161850</td>\n",
       "      <td>196.375000</td>\n",
       "      <td>21.625000</td>\n",
       "      <td>12</td>\n",
       "      <td>0.035061</td>\n",
       "      <td>motpol.wordpress.com</td>\n",
       "      <td>4713</td>\n",
       "      <td>motpol.wordpress.gexf</td>\n",
       "      <td>24</td>\n",
       "      <td>0.674360</td>\n",
       "      <td>motpol.wordpress.com</td>\n",
       "      <td>519</td>\n",
       "      <td>46</td>\n",
       "      <td>92.678227</td>\n",
       "      <td>137</td>\n",
       "      <td>0.885647</td>\n",
       "      <td>2571</td>\n",
       "      <td>810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>185.006369</td>\n",
       "      <td>5809.200000</td>\n",
       "      <td>62.800000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.295066</td>\n",
       "      <td>enkelpoesi.wordpress.com</td>\n",
       "      <td>58092</td>\n",
       "      <td>enkelpoesi.wordpress.gexf</td>\n",
       "      <td>10</td>\n",
       "      <td>0.243064</td>\n",
       "      <td>enkelpoesi.wordpress.com</td>\n",
       "      <td>628</td>\n",
       "      <td>125</td>\n",
       "      <td>99.203822</td>\n",
       "      <td>104</td>\n",
       "      <td>0.847885</td>\n",
       "      <td>6612</td>\n",
       "      <td>1005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>60.539062</td>\n",
       "      <td>3874.500000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.059178</td>\n",
       "      <td>a4jesus.blogspot.com</td>\n",
       "      <td>30996</td>\n",
       "      <td>a4jesus.blogspot.gexf</td>\n",
       "      <td>8</td>\n",
       "      <td>0.457095</td>\n",
       "      <td>a4jesus.blogspot.com</td>\n",
       "      <td>1024</td>\n",
       "      <td>107</td>\n",
       "      <td>99.804688</td>\n",
       "      <td>344</td>\n",
       "      <td>0.455675</td>\n",
       "      <td>13571</td>\n",
       "      <td>1796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>28.434466</td>\n",
       "      <td>450.576923</td>\n",
       "      <td>31.692308</td>\n",
       "      <td>12</td>\n",
       "      <td>0.034550</td>\n",
       "      <td>attitude4jesus.blogspot.com</td>\n",
       "      <td>11715</td>\n",
       "      <td>attitude4jesus.blogspot.gexf</td>\n",
       "      <td>26</td>\n",
       "      <td>0.531123</td>\n",
       "      <td>attitude4jesus.blogspot.com</td>\n",
       "      <td>824</td>\n",
       "      <td>113</td>\n",
       "      <td>98.422330</td>\n",
       "      <td>556</td>\n",
       "      <td>0.898904</td>\n",
       "      <td>9376</td>\n",
       "      <td>1551</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      avDegree  avEdgesPerCom  avNodesPerCom  connComp   density  \\\n",
       "0    34.343623    1738.339286     101.232143        33  0.006059   \n",
       "1    43.597450    2142.960000      98.306667        57  0.005914   \n",
       "2    29.046785     844.140351      58.122807        98  0.004384   \n",
       "3    52.989711    5442.886364     205.431818        22  0.005863   \n",
       "4    24.652632     325.277778      26.388889         9  0.052010   \n",
       "5    30.845357     991.986667      64.320000        57  0.006395   \n",
       "6    50.987849    5471.891892     214.635135        59  0.003210   \n",
       "7    71.002567    3696.702970     104.128713        67  0.006752   \n",
       "8    18.161850     196.375000      21.625000        12  0.035061   \n",
       "9   185.006369    5809.200000      62.800000         2  0.295066   \n",
       "10   60.539062    3874.500000     128.000000         3  0.059178   \n",
       "11   28.434466     450.576923      31.692308        12  0.034550   \n",
       "\n",
       "                              domain   edges  \\\n",
       "0                        politism.se   97347   \n",
       "1                      jardenberg.se  160722   \n",
       "2                      avpixlat.info   96232   \n",
       "3                         erixon.com  239487   \n",
       "4                     www.erixon.com    5855   \n",
       "5                      ladydahmer.nu   74399   \n",
       "6            thoralf.bloggplatsen.se  404920   \n",
       "7   kulturellammunition.blogspot.com  373367   \n",
       "8               motpol.wordpress.com    4713   \n",
       "9           enkelpoesi.wordpress.com   58092   \n",
       "10              a4jesus.blogspot.com   30996   \n",
       "11       attitude4jesus.blogspot.com   11715   \n",
       "\n",
       "                            graphFile  louvainComms  modularity  \\\n",
       "0                       politism.gexf            56    0.379904   \n",
       "1                     jardenberg.gexf            75    0.312255   \n",
       "2                       avpixlat.gexf           114    0.520707   \n",
       "3                         erixon.gexf            44    0.288071   \n",
       "4                     www.erixon.gexf            18    0.758138   \n",
       "5                     ladydahmer.gexf            75    0.311397   \n",
       "6           thoralf.bloggplatsen.gexf            74    0.261078   \n",
       "7   kulturellammunition.blogspot.gexf           101    0.360243   \n",
       "8               motpol.wordpress.gexf            24    0.674360   \n",
       "9           enkelpoesi.wordpress.gexf            10    0.243064   \n",
       "10              a4jesus.blogspot.gexf             8    0.457095   \n",
       "11       attitude4jesus.blogspot.gexf            26    0.531123   \n",
       "\n",
       "                                name  nodes  nrOfPosts  \\\n",
       "0                        politism.se   5669       1832   \n",
       "1                      jardenberg.se   7373       2249   \n",
       "2                      avpixlat.info   6626       4627   \n",
       "3                         erixon.com   9039       3330   \n",
       "4                     www.erixon.com    475         32   \n",
       "5                      ladydahmer.nu   4824        548   \n",
       "6            thoralf.bloggplatsen.se  15883        856   \n",
       "7   kulturellammunition.blogspot.com  10517        604   \n",
       "8               motpol.wordpress.com    519         46   \n",
       "9           enkelpoesi.wordpress.com    628        125   \n",
       "10              a4jesus.blogspot.com   1024        107   \n",
       "11       attitude4jesus.blogspot.com    824        113   \n",
       "\n",
       "    prcOfWordsInLargestConnComp  sentences  topnBCNodesCommDistrEntropy  \\\n",
       "0                     99.047451       1452                     0.950353   \n",
       "1                     99.009901       3090                     0.850365   \n",
       "2                     97.992756       2546                     0.865366   \n",
       "3                     99.701294       2572                     0.931272   \n",
       "4                     92.842105         49                     0.959545   \n",
       "5                     98.528192       2507                     0.911128   \n",
       "6                     99.515205      18093                     0.791913   \n",
       "7                     98.982600       1940                     0.571431   \n",
       "8                     92.678227        137                     0.885647   \n",
       "9                     99.203822        104                     0.847885   \n",
       "10                    99.804688        344                     0.455675   \n",
       "11                    98.422330        556                     0.898904   \n",
       "\n",
       "    total_words  unique_words  \n",
       "0         28299          9028  \n",
       "1         53884         12392  \n",
       "2         37735         11443  \n",
       "3         57458         15768  \n",
       "4          1211           721  \n",
       "5         40848          9423  \n",
       "6        276135         33006  \n",
       "7         49349         15803  \n",
       "8          2571           810  \n",
       "9          6612          1005  \n",
       "10        13571          1796  \n",
       "11         9376          1551  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1399701: http://politism.se\n",
    "# 539440: jardenberg 1,559 posts\n",
    "# 1397160: avpixlat 4,627 posts\n",
    "# 259573: http://erixon.com/blogg\n",
    "# 652891: http://www.erixon.com\n",
    "# 1402060: http://ladydahmer.nu/\n",
    "# 1396686: http://thoralf.bloggplatsen.se\n",
    "# 1169238: http://kulturellammunition.blogspot.com/\n",
    "# 503722: http://motpol.wordpress.com\n",
    "# 693937: https://enkelpoesi.wordpress.com/\n",
    "# 633350: http://a4jesus.blogspot.com/\n",
    "# 631537: http://attitude4jesus.blogspot.com/\n",
    "\n",
    "stats_list = []\n",
    "bids_list = [\"1399701\",\"539440\",\"1397160\",\"259573\",\"652891\",\"1402060\", \"1396686\", \"1169238\", \"503722\",\"693937\",\"633350\",\"631537\"]\n",
    "for bid in bids_list:\n",
    "    \n",
    "    domain = get_url_from_bid(bid)\n",
    "    print(domain)\n",
    "    \n",
    "    text, nrOfPosts = retrieve_all_text_from_blogid(bid)\n",
    "    print(nrOfPosts)\n",
    "    \n",
    "    stats = run_graph_process_on_text(text, name=domain)\n",
    "    \n",
    "    stats[\"nrOfPosts\"] = nrOfPosts\n",
    "    stats[\"domain\"] = domain\n",
    "    \n",
    "    stats_list.append(stats)\n",
    "df = pd.DataFrame(stats_list)\n",
    "df_name = \"_\".join(bids_list)\n",
    "df.to_pickle(\"\" + \".pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>domain</th>\n",
       "      <th>total_words</th>\n",
       "      <th>topnBCNodesCommDistrEntropy</th>\n",
       "      <th>modularity</th>\n",
       "      <th>louvainComms</th>\n",
       "      <th>nrOfPosts</th>\n",
       "      <th>nodes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>politism.se</td>\n",
       "      <td>28299</td>\n",
       "      <td>0.950353</td>\n",
       "      <td>0.379904</td>\n",
       "      <td>56</td>\n",
       "      <td>1832</td>\n",
       "      <td>5669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>jardenberg.se</td>\n",
       "      <td>53884</td>\n",
       "      <td>0.850365</td>\n",
       "      <td>0.312255</td>\n",
       "      <td>75</td>\n",
       "      <td>2249</td>\n",
       "      <td>7373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>avpixlat.info</td>\n",
       "      <td>37735</td>\n",
       "      <td>0.865366</td>\n",
       "      <td>0.520707</td>\n",
       "      <td>114</td>\n",
       "      <td>4627</td>\n",
       "      <td>6626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>erixon.com</td>\n",
       "      <td>57458</td>\n",
       "      <td>0.931272</td>\n",
       "      <td>0.288071</td>\n",
       "      <td>44</td>\n",
       "      <td>3330</td>\n",
       "      <td>9039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>www.erixon.com</td>\n",
       "      <td>1211</td>\n",
       "      <td>0.959545</td>\n",
       "      <td>0.758138</td>\n",
       "      <td>18</td>\n",
       "      <td>32</td>\n",
       "      <td>475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ladydahmer.nu</td>\n",
       "      <td>40848</td>\n",
       "      <td>0.911128</td>\n",
       "      <td>0.311397</td>\n",
       "      <td>75</td>\n",
       "      <td>548</td>\n",
       "      <td>4824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>thoralf.bloggplatsen.se</td>\n",
       "      <td>276135</td>\n",
       "      <td>0.791913</td>\n",
       "      <td>0.261078</td>\n",
       "      <td>74</td>\n",
       "      <td>856</td>\n",
       "      <td>15883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>kulturellammunition.blogspot.com</td>\n",
       "      <td>49349</td>\n",
       "      <td>0.571431</td>\n",
       "      <td>0.360243</td>\n",
       "      <td>101</td>\n",
       "      <td>604</td>\n",
       "      <td>10517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>motpol.wordpress.com</td>\n",
       "      <td>2571</td>\n",
       "      <td>0.885647</td>\n",
       "      <td>0.674360</td>\n",
       "      <td>24</td>\n",
       "      <td>46</td>\n",
       "      <td>519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>enkelpoesi.wordpress.com</td>\n",
       "      <td>6612</td>\n",
       "      <td>0.847885</td>\n",
       "      <td>0.243064</td>\n",
       "      <td>10</td>\n",
       "      <td>125</td>\n",
       "      <td>628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>a4jesus.blogspot.com</td>\n",
       "      <td>13571</td>\n",
       "      <td>0.455675</td>\n",
       "      <td>0.457095</td>\n",
       "      <td>8</td>\n",
       "      <td>107</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>attitude4jesus.blogspot.com</td>\n",
       "      <td>9376</td>\n",
       "      <td>0.898904</td>\n",
       "      <td>0.531123</td>\n",
       "      <td>26</td>\n",
       "      <td>113</td>\n",
       "      <td>824</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              domain  total_words  \\\n",
       "0                        politism.se        28299   \n",
       "1                      jardenberg.se        53884   \n",
       "2                      avpixlat.info        37735   \n",
       "3                         erixon.com        57458   \n",
       "4                     www.erixon.com         1211   \n",
       "5                      ladydahmer.nu        40848   \n",
       "6            thoralf.bloggplatsen.se       276135   \n",
       "7   kulturellammunition.blogspot.com        49349   \n",
       "8               motpol.wordpress.com         2571   \n",
       "9           enkelpoesi.wordpress.com         6612   \n",
       "10              a4jesus.blogspot.com        13571   \n",
       "11       attitude4jesus.blogspot.com         9376   \n",
       "\n",
       "    topnBCNodesCommDistrEntropy  modularity  louvainComms  nrOfPosts  nodes  \n",
       "0                      0.950353    0.379904            56       1832   5669  \n",
       "1                      0.850365    0.312255            75       2249   7373  \n",
       "2                      0.865366    0.520707           114       4627   6626  \n",
       "3                      0.931272    0.288071            44       3330   9039  \n",
       "4                      0.959545    0.758138            18         32    475  \n",
       "5                      0.911128    0.311397            75        548   4824  \n",
       "6                      0.791913    0.261078            74        856  15883  \n",
       "7                      0.571431    0.360243           101        604  10517  \n",
       "8                      0.885647    0.674360            24         46    519  \n",
       "9                      0.847885    0.243064            10        125    628  \n",
       "10                     0.455675    0.457095             8        107   1024  \n",
       "11                     0.898904    0.531123            26        113    824  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"domain\",\"total_words\",\"topnBCNodesCommDistrEntropy\",\"modularity\",\"louvainComms\",\"nrOfPosts\",\"nodes\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Skip for now:"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# Naaah. Should use a Named Entity Extractor instead. Skip.\n",
    "def extract_ngrams(sentences_list):\n",
    "    \"\"\"\n",
    "    sentences_list: list of lists.\n",
    "    Return: list of bi- and trigrams found in text.\n",
    "    \"\"\"\n",
    "    str_list = []\n",
    "    for sent in sentences_list:\n",
    "        str_sent = \" \".join(sent)\n",
    "        str_list.append(str_sent)\n",
    "    \n",
    "    bigrams_list = []\n",
    "    for string in str_list:\n",
    "        blob = TextBlob(string)\n",
    "        bigrams = blob.ngrams(2)\n",
    "        bigrams_list.extend(list(bigrams))\n",
    "    \n",
    "    bigram_string_list = []\n",
    "    for WordList in bigrams_list:\n",
    "        bigram_string = \"\"\n",
    "        for w in WordList:\n",
    "            bigram_string += w + \" \"\n",
    "        bigram_string = bigram_string.strip()\n",
    "        bigram_string_list.append(bigram_string)\n",
    "    bigram_cnt = Counter(bigram_string_list)\n",
    "    \n",
    "    actual_bigrams = []\n",
    "    for count in bigram_cnt.most_common(100):\n",
    "        print(count)\n",
    "        #for bg, c in count:\n",
    "        #    if c >1:\n",
    "        #        actual_bigrams.append(bg)\n",
    "    \n",
    "    return actual_bigrams"
   ]
  }
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