Data_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm_notebook\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle5 as pickle\n",
    "from glob import glob\n",
    "from datetime import datetime\n",
    "import re\n",
    "import numpy as np\n",
    "from matplotlib.colors import ListedColormap\n",
    "from scipy.stats import mannwhitneyu\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold={'hatespeech':0.9,'fearspeech':0.7,'normal':0.7}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_labels={'hatespeech':2,'fearspeech':1,'normal':0}\n",
    "reverse_dict_labels={dict_labels[key]:key for key in dict_labels.keys()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import urllib.parse as urlparse\n",
    "import emoji\n",
    "\n",
    "\n",
    "GENERIC_TLDS = [\n",
    "    'aero', 'asia', 'biz', 'com', 'coop', 'edu', 'gov', 'info', 'int', 'jobs', \n",
    "    'mil', 'mobi', 'museum', 'name', 'net', 'org', 'pro', 'tel', 'travel', 'cat'\n",
    "    ]\n",
    "\n",
    "def extract_emojis(line):\n",
    "    return [c for c in line if c in emoji.UNICODE_EMOJI['en']]\n",
    "\n",
    "def return_meta_info(element):\n",
    "    dict_meta_info={}\n",
    "    dict_meta_info['id']=element['id']\n",
    "    dict_meta_info['reposts']=element['repost_count']\n",
    "    dict_meta_info['replies']=element['reply_count']\n",
    "    dict_meta_info['like_count']=element['like_count']\n",
    "    return dict_meta_info\n",
    "\n",
    "\n",
    "def get_domain(url):\n",
    "    hostname = urlparse.urlparse(url.lower()).netloc\n",
    "    if hostname == '':\n",
    "        # Force the recognition as a full URL\n",
    "        hostname = urlparse.urlparse('http://' + url).netloc\n",
    "\n",
    "    # Remove the 'user:passw', 'www.' and ':port' parts\n",
    "    hostname = hostname.split('@')[-1].split(':')[0].lstrip('www.').split('.')\n",
    "\n",
    "    num_parts = len(hostname)\n",
    "    if (num_parts < 3) or (len(hostname[-1]) > 2):\n",
    "        return '.'.join(hostname[:-1])\n",
    "    if len(hostname[-2]) > 2 and hostname[-2] not in GENERIC_TLDS:\n",
    "        return '.'.join(hostname[:-1])\n",
    "    if num_parts >= 3:\n",
    "        return '.'.join(hostname[:-2])\n",
    "\n",
    "\n",
    "\n",
    "def return_text_info(element):\n",
    "    temp=element['post_body']\n",
    "    all_emojis=extract_emojis(temp)\n",
    "    #all_urls= re.findall('(?:(?:https?|ftp):\\/\\/)?[\\w/\\-?=%.]+\\.[\\w/\\-&?=%.]+', temp)\n",
    "    all_urls = re.findall('https?://(?:[-\\w.]|(?:%[\\da-fA-F]{2}))+', temp)\n",
    "    all_hashtags=re.findall(r\"#(\\w+)\", temp)\n",
    "    \n",
    "    dict_text_info={\n",
    "        'num_hashtags':len(all_hashtags),\n",
    "        'num_urls':len(all_urls),\n",
    "        'num_emojis':len(all_emojis)\n",
    "    }\n",
    "    \n",
    "    \n",
    "    emoji_dict={}\n",
    "    url_dict={}\n",
    "    hashtag_dict={}\n",
    "    \n",
    "    for emoji in all_emojis:\n",
    "        try:\n",
    "            emoji_dict[emoji]+=1\n",
    "        except KeyError:\n",
    "            emoji_dict[emoji]=1\n",
    "            \n",
    "    for hashtag in all_hashtags:\n",
    "        try:\n",
    "            hashtag_dict[hashtag]+=1\n",
    "        except KeyError:\n",
    "            hashtag_dict[hashtag]=1\n",
    "    for url in all_urls:\n",
    "        try:\n",
    "            url_dict[get_domain(url)]+=1\n",
    "        except KeyError:\n",
    "            url_dict[get_domain(url)]=1\n",
    "        \n",
    "\n",
    "    return dict_text_info,emoji_dict,url_dict,hashtag_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-5-df87712f04eb>:16: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for file in tqdm_notebook(files,total=len(files)):\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4dbb97c7ffd24b38adcf2157d8d7c069",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/212 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-df87712f04eb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     24\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m             \u001b[0mdt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfromisoformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0melement\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'post_create_time'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m             \u001b[0mkey_new\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmonth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'/'\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myear\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     27\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m             \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "fear=0\n",
    "hate=0\n",
    "both=0\n",
    "total=0\n",
    "dict_per_date={}\n",
    "dict_post_meta_info={}\n",
    "dict_text_property={}\n",
    "dict_emoji={'fearspeech':{},'hatespeech':{},'both':{},'total':{}}\n",
    "dict_domain={'fearspeech':{},'hatespeech':{},'both':{},'total':{}}\n",
    "dict_hashtag={'fearspeech':{},'hatespeech':{},'both':{},'total':{}}\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "files = sorted(glob('../../Gab_Data/new_features_old_gab/gab_fear_hate_features*.pickle'))\n",
    "for file in tqdm_notebook(files,total=len(files)):\n",
    "    with open(file, 'rb') as handle:\n",
    "        Gab_keyword_match = pickle.load(handle)\n",
    "        \n",
    "    if('predicted_probab' not in Gab_keyword_match[1].keys()):\n",
    "        continue\n",
    "    \n",
    "    for element in Gab_keyword_match:\n",
    "        try:\n",
    "            dt = datetime.fromisoformat(element['post_create_time'])\n",
    "            key_new=str(dt.month)+'/'+str(dt.year)\n",
    "        except KeyError:\n",
    "            continue\n",
    "            \n",
    "        dict_meta_info_temp=return_meta_info(element)\n",
    "        dict_text_property_temp,dict_emoji_temp,dict_domain_temp,dict_hashtag_temp=return_text_info(element)\n",
    "        \n",
    "        \n",
    "        \n",
    "        dict_meta_info_temp['label_probab']={}\n",
    "        \n",
    "        try:\n",
    "            temp=dict_per_date[key_new]\n",
    "        except:\n",
    "            dict_per_date[key_new]={'fearspeech':0,'hatespeech':0,'both':0,'none':0,'total':0}\n",
    "\n",
    "        labels=[]\n",
    "\n",
    "        for i in range(len(element['predicted_probab'])):\n",
    "            dict_meta_info_temp['label_probab'][reverse_dict_labels[i]]=element['predicted_probab'][i]\n",
    "        \n",
    "            if(element['predicted_probab'][i]>threshold[reverse_dict_labels[i]]):\n",
    "                labels.append(reverse_dict_labels[i])\n",
    "        \n",
    "        dict_return_info=return_meta_info(element)\n",
    "        \n",
    "        \n",
    "        if(('fearspeech' in labels) and ('hatespeech' in labels)):\n",
    "            dict_per_date[key_new]['both']+=1\n",
    "            dict_meta_info_temp['label']='both'\n",
    "            both+=1\n",
    "        elif('fearspeech' in labels):\n",
    "            dict_per_date[key_new]['fearspeech']+=1\n",
    "            dict_meta_info_temp['label']='fearspeech'\n",
    "            fear+=1\n",
    "        elif('hatespeech' in labels):\n",
    "            dict_per_date[key_new]['hatespeech']+=1\n",
    "            dict_meta_info_temp['label']='hatespeech'\n",
    "            hate+=1\n",
    "        else:\n",
    "            dict_per_date[key_new]['none']+=1\n",
    "            dict_meta_info_temp['label']='none'\n",
    "        dict_per_date[key_new]['total']+=1\n",
    "        \n",
    "        dict_text_property_temp['label']=dict_meta_info_temp['label']\n",
    "        \n",
    "        for key in dict_emoji_temp:\n",
    "            if(dict_meta_info_temp['label']!='none'):\n",
    "                try:\n",
    "                    dict_emoji[dict_meta_info_temp['label']][key]+=dict_emoji_temp[key]\n",
    "                except KeyError:\n",
    "                    dict_emoji[dict_meta_info_temp['label']][key]=dict_emoji_temp[key]\n",
    "\n",
    "            \n",
    "            try:\n",
    "                dict_emoji['total'][key]+=dict_emoji_temp[key]\n",
    "            except KeyError:\n",
    "                dict_emoji['total'][key]=dict_emoji_temp[key]\n",
    "        \n",
    "        \n",
    "        \n",
    "        for key in dict_domain_temp:\n",
    "            if(dict_meta_info_temp['label']!='none'):\n",
    "                try:\n",
    "                    dict_domain[dict_meta_info_temp['label']][key]+=dict_domain_temp[key]\n",
    "                except KeyError:\n",
    "                    dict_domain[dict_meta_info_temp['label']][key]=dict_domain_temp[key]\n",
    "\n",
    "            \n",
    "            try:\n",
    "                dict_domain['total'][key]+=dict_domain_temp[key]\n",
    "            except KeyError:\n",
    "                dict_domain['total'][key]=dict_domain_temp[key]\n",
    "        \n",
    "        \n",
    "        for key in dict_hashtag_temp:\n",
    "            if(dict_meta_info_temp['label']!='none'):\n",
    "                try:\n",
    "                    dict_hashtag[dict_meta_info_temp['label']][key]+=dict_hashtag_temp[key]\n",
    "                except KeyError:\n",
    "                    dict_hashtag[dict_meta_info_temp['label']][key]=dict_hashtag_temp[key]\n",
    "\n",
    "            \n",
    "            try:\n",
    "                dict_hashtag['total'][key]+=dict_hashtag_temp[key]\n",
    "            except KeyError:\n",
    "                dict_hashtag['total'][key]=dict_hashtag_temp[key]\n",
    "        \n",
    "        \n",
    "        \n",
    "        total+=1\n",
    "        key_id=total\n",
    "        dict_post_meta_info[key_id]=dict_meta_info_temp\n",
    "        dict_text_property[key_id]=dict_text_property_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7493"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dict_domain['hatespeech'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dict_domain['both'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "131289"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dict_domain['total'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'rundblick-unna': 1}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_domain_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': 16634209,\n",
       " 'reposts': 0,\n",
       " 'replies': 2,\n",
       " 'like_count': 3,\n",
       " 'label_probab': {'normal': 0.75161284,\n",
       "  'fearspeech': 0.048907783,\n",
       "  'hatespeech': 0.2500133},\n",
       " 'label': 'none'}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_post_meta_info[key_id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_fear={}\n",
    "dict_hate={}\n",
    "dict_normal={}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import random\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "data = []\n",
    "for key in tqdm_notebook(dict_post_meta_info, total=len(dict_post_meta_info)):\n",
    "    element=dict_post_meta_info[key]\n",
    "    data.append(element['label_probab']['hatespeech'])\n",
    "    \n",
    "    \n",
    "    if(element['label_probab']['fearspeech']>0.5):\n",
    "        final=int(10*element['label_probab']['fearspeech'])\n",
    "        try:\n",
    "            dict_fear[final].append(element['id'])\n",
    "        except KeyError:\n",
    "            dict_fear[final]=[element['id']]\n",
    "    if(element['label_probab']['hatespeech']>0.5):\n",
    "        final=int(10*element['label_probab']['hatespeech'])\n",
    "        try:\n",
    "            dict_hate[final].append(element['id'])\n",
    "        except KeyError:\n",
    "            dict_hate[final]=[element['id']]\n",
    "    if(element['label_probab']['normal']>0.5):\n",
    "        final=int(10*element['label_probab']['normal'])\n",
    "        try:\n",
    "            dict_normal[final].append(element['id'])\n",
    "        except KeyError:\n",
    "            dict_normal[final]=[element['id']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_dict_hate={}\n",
    "\n",
    "for key in dict_hate:\n",
    "    sample_dict_hate[key]=random.sample(dict_hate[key],20)\n",
    "    \n",
    "\n",
    "sample_dict_fear={}\n",
    "\n",
    "for key in dict_fear:\n",
    "    sample_dict_fear[key]=random.sample(dict_fear[key],20)\n",
    "    \n",
    "   \n",
    "    \n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "#my_dict = { 'Ali': 9, 'Sid': 1, 'Luna': 7, 'Sim': 12, 'Pooja': 4, 'Jen': 2}\n",
    "with open('../Results/fearspeech_thresholding.json', 'wb') as fp:\n",
    "    pickle.dump(sample_dict_fear, fp)\n",
    "    \n",
    "with open('../Results/hatespeech_thresholding.json', 'wb') as fp:\n",
    "    pickle.dump(sample_dict_hate, fp)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fixed bin size\n",
    "\n",
    "plt.hist(data,color='orange')\n",
    "plt.title('Random Gaussian data (fixed bin size)')\n",
    "plt.xlabel('variable X (bin size = 5)')\n",
    "plt.ylabel('count')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "element"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "426455 709358 200 21200212\n"
     ]
    }
   ],
   "source": [
    "print(fear+both,\n",
    "hate+both,\n",
    "both,\n",
    "total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "with open('../Results/dict_per_date.json', 'w') as fp:\n",
    "    json.dump(dict_per_date, fp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05356611528224341"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(fear+hate+both)/total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_per_date_normalised={}\n",
    "for key in dict_per_date:\n",
    "    dict_per_date_normalised[key]={'fearspeech':0,'hatespeech':0,'both':0,'none':0,'total':0}\n",
    "    for key1 in dict_per_date_normalised[key].keys():\n",
    "        dict_per_date_normalised[key][key1]=dict_per_date[key][key1]/(dict_per_date[key]['total'])\n",
    "        \n",
    "#         if(dict_per_date_normalised[key][key1]>0.2):\n",
    "#             dict_per_date_normalised[key][key1]=0.2\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(dict_per_date_normalised).transpose()\n",
    "df.index=pd.to_datetime(df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "matplotlib.rcParams['figure.figsize'] = (20, 3)\n",
    "\n",
    "sns.set(context='paper',style='whitegrid',font_scale=2.0, rc={\"lines.linewidth\": 2.0,\"lines.markersize\": 10})\n",
    "sns.lineplot(data = df[[\"fearspeech\"]],palette=['red'],markers=['o'])\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"% of fear speech\")\n",
    "path='../Results/fearspeech_temporal.pdf'\n",
    "plt.savefig(path,bbox_inches='tight',dpi=400)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "sns.lineplot(data = df[[\"hatespeech\"]],palette=['orange'],markers=['X'])\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"% of hate speech\")\n",
    "path='../Results/hatespeech_temporal.pdf'\n",
    "plt.savefig(path,bbox_inches='tight',dpi=400)\n",
    "\n",
    "# plt.figure()\n",
    "# sns.lineplot(data = df[[\"both\"]],palette=['brown'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_count(dict_new,title_text,select_column):\n",
    "    x_values=['fearspeech','hatespeech','total']\n",
    "    y_values=[]\n",
    "    for x_val in x_values:\n",
    "        if(x_val=='total'):\n",
    "            count=0\n",
    "            for key in tqdm_notebook(dict_new,total=len(dict_new)):\n",
    "                if(dict_new[key][select_column]!=0):\n",
    "                    count+=1\n",
    "            y_values.append(100*count/len(dict_new))\n",
    "        else:\n",
    "            count=0\n",
    "            count_total=0\n",
    "            for key in tqdm_notebook(dict_new,total=len(dict_new)):\n",
    "                if((dict_new[key]['label']==x_val)):\n",
    "                    if(dict_new[key][select_column]!=0):\n",
    "                        count+=1\n",
    "                    count_total+=1\n",
    "                   \n",
    "            y_values.append(100*count/count_total)\n",
    "    c= ['red', 'orange', 'green']\n",
    "    fig = plt.figure(figsize = (4,4))\n",
    "     \n",
    "    # creating the bar plot\n",
    "    plt.bar(x_values,y_values, color =c,\n",
    "        width = 0.4)\n",
    "\n",
    "   \n",
    "\n",
    "    #plt.legend(x_values)\n",
    "    plt.xlabel(\"Speech type\")\n",
    "    plt.ylabel(\"% of post that are \"+ select_column)\n",
    "    plt.title(title_text)\n",
    "    path='../Results/'+'_'.join(title_text.split())+'.pdf'\n",
    "    plt.savefig(path,bbox_inches='tight',dpi=400)\n",
    "#     plt.show()\n",
    "\n",
    "    \n",
    "def plot_mean(dict_new,title_text,select_column):\n",
    "    x_values=['fearspeech','hatespeech','total']\n",
    "    y_values=[]\n",
    "    y_err=[]\n",
    "    \n",
    "    y_values_list=[]\n",
    "    \n",
    "    for x_val in x_values:\n",
    "        if(x_val=='total'):\n",
    "            count=[]\n",
    "            for key in tqdm_notebook(dict_new,total=len(dict_new)):\n",
    "                if(dict_new[key][select_column]!=0):\n",
    "                    count.append(dict_new[key][select_column])\n",
    "            y_values.append(np.mean(count))\n",
    "            y_err.append(np.std(count))\n",
    "            y_values_list.append(count)\n",
    "        else:\n",
    "            count=[]\n",
    "            for key in tqdm_notebook(dict_new,total=len(dict_new)):\n",
    "                if((dict_new[key]['label']==x_val)):\n",
    "                    if(dict_new[key][select_column]!=0):\n",
    "                        count.append(dict_new[key][select_column])\n",
    "                   \n",
    "            y_values.append(np.mean(count))\n",
    "            y_err.append(np.std(count))\n",
    "            y_values_list.append(count)\n",
    "    c= ['red', 'orange', 'green']\n",
    "    fig = plt.figure(figsize = (4,4))\n",
    "    \n",
    "    \n",
    "    \n",
    "    stat, p = mannwhitneyu(y_values_list[0],y_values_list[1],alternative='greater')\n",
    "    print(\"p-value between fear speech mean and hate speech mean\",p)\n",
    "    \n",
    "    stat, p = mannwhitneyu(y_values_list[1],y_values_list[2],alternative='greater')\n",
    "    print(\"p-value between hate speech mean and normal mean\",p)\n",
    "    \n",
    "    \n",
    "    stat, p = mannwhitneyu(y_values_list[0],y_values_list[2],alternative='greater')\n",
    "    print(\"p-value between fear speech mean and normal mean\",p)\n",
    "    \n",
    "    # creating the bar plot\n",
    "    x_values_plot=['F','H','T']\n",
    "    \n",
    "    plt.bar(x_values_plot,y_values,color =c,capsize=1,align='center',width = 0.5)\n",
    "    #plt.errorbar(x_values,y_values,y_err)\n",
    "   \n",
    "\n",
    "    #plt.legend(x_values)\n",
    "    #plt.xlabel(\"Speech type\")\n",
    "    plt.ylabel(\"#\" + select_column + \"/post\")\n",
    "    #plt.title(title_text)\n",
    "    \n",
    "    \n",
    "    path='../Results/'+'_'.join(title_text.split())+'.pdf'\n",
    "    plt.savefig(path,bbox_inches='tight',dpi=400)\n",
    "    #plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reposts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-17-048a8908f62d>:14: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for key in tqdm_notebook(dict_new,total=len(dict_new)):\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e3aebbe6445148658fbf39dc39056d70",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/21200212 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "158aa2282e4b4f398f9ec9422c750f2d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/21200212 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-17-048a8908f62d>:7: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for key in tqdm_notebook(dict_new,total=len(dict_new)):\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b594b230b95744e184e38ff1ebd6e60d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/21200212 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(context='paper',style='whitegrid',font_scale=1.5)\n",
    "\n",
    "plot_count(dict_post_meta_info,'repost count analysis','reposts')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-29-fbc3101f165f>:57: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for key in tqdm_notebook(dict_new,total=len(dict_new)):\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ea94bf51ecfc406d863f6bca18602076",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/21200212 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1f2c69cc389244cda10a5ed406e911bc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/21200212 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-29-fbc3101f165f>:49: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for key in tqdm_notebook(dict_new,total=len(dict_new)):\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e16c5e721c024f1b8a61f98ca1817584",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/21200212 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value between fear speech mean and hate speech mean 0.0\n",
      "p-value between hate speech mean and normal mean 1.0\n",
      "p-value between fear speech mean and normal mean 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(context='paper',style='whitegrid',font_scale=2.5)\n",
    "\n",
    "plot_mean(dict_post_meta_info,'repost mean analysis','reposts')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-70-2191c9c053b4>:14: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for key in tqdm_notebook(dict_new,total=len(dict_new)):\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c4e91ad8f4c04caa99725a4ae71de71c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/21200212 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8f7b70a471224a7a96e671f15f8f7a15",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/21200212 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-70-2191c9c053b4>:7: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for key in tqdm_notebook(dict_new,total=len(dict_new)):\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d80731f86ac9424086cdf2604af7f7b5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/21200212 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_count(dict_post_meta_info,'replies count analysis','replies')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-29-fbc3101f165f>:57: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for key in tqdm_notebook(dict_new,total=len(dict_new)):\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1c8c7a16e7e54b91adc2302696000fad",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/21200212 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8a62bce58d664ecaa1864bf2444e0cee",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/21200212 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-29-fbc3101f165f>:49: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for key in tqdm_notebook(dict_new,total=len(dict_new)):\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "777e170df8bd4682a6744819d687a878",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/21200212 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value between fear speech mean and hate speech mean 2.856388765838556e-268\n",
      "p-value between hate speech mean and normal mean 0.001537385590995161\n",
      "p-value between fear speech mean and normal mean 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_mean(dict_post_meta_info,'replies mean analysis','replies')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Favourites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-29-fbc3101f165f>:14: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for key in tqdm_notebook(dict_new,total=len(dict_new)):\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f3fe3256b4b64181953944344c7e8621",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/21200212 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1d68f894ddc54dad94ed90034715d809",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/21200212 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-29-fbc3101f165f>:7: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for key in tqdm_notebook(dict_new,total=len(dict_new)):\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "20dd0c03b9f04b34a77be7d711a38e4f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/21200212 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_count(dict_post_meta_info,'likes count analysis','like_count')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-29-fbc3101f165f>:57: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for key in tqdm_notebook(dict_new,total=len(dict_new)):\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ed3b9cd2092345918ccd3e683735a39d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/21200212 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bef6dc84f7484b0d9b882b4535a48306",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/21200212 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-29-fbc3101f165f>:49: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for key in tqdm_notebook(dict_new,total=len(dict_new)):\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0698828336a84e50ac42b8daa59d36af",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/21200212 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value between fear speech mean and hate speech mean 0.0\n",
      "p-value between hate speech mean and normal mean 2.3488971307475242e-163\n",
      "p-value between fear speech mean and normal mean 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_mean(dict_post_meta_info,'like mean analysis','like_count')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Text analytics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Emojis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-70-2191c9c053b4>:14: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for key in tqdm_notebook(dict_new,total=len(dict_new)):\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8a2d65358eeb4808ba1410e708a6cc67",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/21200212 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "96412f7dea794d70b57c424d2072f934",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/21200212 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-70-2191c9c053b4>:7: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for key in tqdm_notebook(dict_new,total=len(dict_new)):\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4d201710c288443fa07e2f3759aaf0bc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/21200212 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_count(dict_text_property,'Emoji count analysis','num_emojis')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-29-fbc3101f165f>:57: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for key in tqdm_notebook(dict_new,total=len(dict_new)):\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bbcae55fe7ed4823be904f9540ece855",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/21200212 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1e68ff33b5a14e72a001e57edc7cd226",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/21200212 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-29-fbc3101f165f>:49: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for key in tqdm_notebook(dict_new,total=len(dict_new)):\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e6a35cf487294ef19791c1431d4cfc51",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/21200212 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value between fear speech mean and hate speech mean 1.0\n",
      "p-value between hate speech mean and normal mean 2.6644057696597405e-171\n",
      "p-value between fear speech mean and normal mean 1.4417579841715884e-05\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_mean(dict_text_property,'Emoji mean analysis','num_emojis')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hashtags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-70-2191c9c053b4>:14: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for key in tqdm_notebook(dict_new,total=len(dict_new)):\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0df98606a92540d3b80bc6407da105bb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/21200212 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "89a37949c31740e0b7aeddcf9c3d09e3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/21200212 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-70-2191c9c053b4>:7: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for key in tqdm_notebook(dict_new,total=len(dict_new)):\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2ab39a0a549b4733b406f740545ca97a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/21200212 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_count(dict_text_property,'Hashtag count analysis','num_hashtags')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-29-fbc3101f165f>:57: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for key in tqdm_notebook(dict_new,total=len(dict_new)):\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c44bb8a5b8324087a7d9e48c70d5db7c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/21200212 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "31ca81ebf37f492b90c961ece27405f2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/21200212 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-29-fbc3101f165f>:49: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for key in tqdm_notebook(dict_new,total=len(dict_new)):\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ef3a9b0e96e84c3e8dd1b6bb3da4b226",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/21200212 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value between fear speech mean and hate speech mean 0.0\n",
      "p-value between hate speech mean and normal mean 1.0\n",
      "p-value between fear speech mean and normal mean 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_mean(dict_text_property,'Hashtag mean analysis','num_hashtags')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Urls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-70-2191c9c053b4>:14: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for key in tqdm_notebook(dict_new,total=len(dict_new)):\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4f9fe71e990e44a997db0c4c7472cf6c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/21200212 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "46188a81e5294023adaae045bdc6054a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/21200212 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-70-2191c9c053b4>:7: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for key in tqdm_notebook(dict_new,total=len(dict_new)):\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fe14bfb47d504849aeb6d93b8bf5e15a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/21200212 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plot_count(dict_text_property,'Url count analysis','num_urls')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-29-fbc3101f165f>:57: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for key in tqdm_notebook(dict_new,total=len(dict_new)):\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a70798ae712b4dab993b9105776f5dbb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/21200212 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "845bb11ad50141359bd476d40cc90ed4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/21200212 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-29-fbc3101f165f>:49: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for key in tqdm_notebook(dict_new,total=len(dict_new)):\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "531f2db203494fe3ab4d37b9f845417c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/21200212 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value between fear speech mean and hate speech mean 0.0\n",
      "p-value between hate speech mean and normal mean 1.0\n",
      "p-value between fear speech mean and normal mean 5.361354022994717e-247\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_mean(dict_text_property,'Url mean analysis','num_urls')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gzip\n",
    "with gzip.open('../../../../Gab_Data_old/Reposts.pkl.gz','rb') as fp:\n",
    "    dict_repost = pickle.load(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24609334"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(dict_repost.keys())[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key in dict_repost.keys():\n",
    "    temp=dict_repost[key]\n",
    "    \n",
    "    for ele in temp:\n",
    "        if('2017-01' in ele['reposting_time']):\n",
    "            print(ele['reposting_time'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def return_text_info(element):\n",
    "    temp=element['post_body']\n",
    "    all_emojis=extract_emojis(temp)\n",
    "    #all_urls= re.findall('(?:(?:https?|ftp):\\/\\/)?[\\w/\\-?=%.]+\\.[\\w/\\-&?=%.]+', temp)\n",
    "    all_urls = re.findall('https?://(?:[-\\w.]|(?:%[\\da-fA-F]{2}))+', temp)\n",
    "    all_hashtags=re.findall(r\"#(\\w+)\", temp)\n",
    "    \n",
    "    dict_text_info=\n",
    "    {\n",
    "        'num_hashtags':len(all_hashtags),\n",
    "        'num_urls':len(all_urls),\n",
    "        'num_emojis':len(all_emojis)\n",
    "    }\n",
    "    \n",
    "    \n",
    "    emoji_dict={}\n",
    "    url_dict={}\n",
    "    hashtag_dict={}\n",
    "    \n",
    "    for emoji in all_emojis:\n",
    "        try:\n",
    "            emoji_dict[emoji]+=1\n",
    "        except KeyError:\n",
    "            emoji_dict[emoji]=1\n",
    "            \n",
    "    for hashtag in all_hashtags:\n",
    "        try:\n",
    "            hashtag_dict[hashtag]+=1\n",
    "        except KeyError:\n",
    "            hashtag_dict[hashtag]=1\n",
    "    for url in all_urls:\n",
    "        try:\n",
    "            url_dict[get_domain(url)]+=1\n",
    "        except KeyError:\n",
    "            url_dict[get_domain(url)]=1\n",
    "        \n",
    "\n",
    "    return dict_text_info,emoji_dict,url_dict,hashtag_dict"
   ]
  }
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