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{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Identifier les leaders d’opinion du domaine de l’IA sur Twitter\n", "\n", "Auteur : Jiayue LIU (MSc Data Management, Paris School of Business)\n", "\n", "Date : 18 Avril 2021 " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Installer toutes les librairies nécessaires à l'exercice\n", "import tweepy\n", "import pandas as pd\n", "pd.options.mode.chained_assignment = None\n", "import igraph as ig\n", "import datetime\n", "\n", "# Authentification API\n", "auth = tweepy.OAuthHandler(\n", " 'g5ktEfyoenGVaxGFbbz5Xt6CH', \n", " 'D5RFlzzO5FMDvFFkUf5piWFF1mNKpgzEZpZEjC40uP7ZA4QhrY')\n", "auth.set_access_token(\n", " '1313171160973139973-eVa2VAFWUoha0lLgUzVwCQwQycWJ0c', \n", " 'c4DdmZV6DWV2NwjpBTy5cZlN9tdPvwACbUrwWQyj3RKfX')\n", "api = tweepy.API(auth,wait_on_rate_limit=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Extraire les tweets contenant les mots-clés définis\n", "hashtags = ['#IA', '#IntelligenceArtificielle']\n", "results = tweepy.Cursor(api.search, q=hashtags, lang='fr').items()\n", "\n", "# Convertir les résultats de recherche du json en dataframe\n", "json_data = [r._json for r in results]\n", "results_df = pd.json_normalize(json_data)\n", "\n", "results_df.to_csv(\"tweets_database.csv\", sep=\",\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Garder des informations qui nous intéresseraient en renommant les colonnes\n", "simple_results = results_df[['created_at',\n", " 'user.location',\n", " 'user.screen_name',\n", " 'user.followers_count',\n", " 'entities.user_mentions']]\n", "simple_results.columns = ['time',\n", " 'location',\n", " 'user_id',\n", " 'num_followers',\n", " 'mentions']\n", "\n", "# Afficher le résultat brute mais simplifié\n", "today = datetime.date.today()\n", "week_ago = today - datetime.timedelta(days=7)\n", "print(\"Pendant la semaine du\", week_ago.strftime(\"%d/%m/%Y\"),\n", " \"au\", today.strftime(\"%d/%m/%Y\"),\n", " \", les tweets en français et ayant pour hashtags #IA ou #IntelligenceArtificielle sont les suivants : \\n\",\n", " simple_results)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Convertir la colonne \"mentions\" en liste simple\n", "mentioned_users = []\n", "for mention in simple_results.mentions:\n", " mentioned_users.append(list(map(lambda d: d['screen_name'], mention)))\n", "simple_results['mentions'] = mentioned_users\n", "\n", "# Stocker tous les edges et nodes dans des dataframes\n", "edges_df = simple_results.loc[:, ['mentions', 'user_id', 'num_followers']]\n", "edges_df = edges_df.explode('mentions').reset_index().drop('index',1)\n", "\n", "mention_list = edges_df.mentions.to_list()\n", "user_list = edges_df.user_id.to_list()\n", "nodes_list = set(user_list + mention_list)\n", "\n", "edges = edges_df.dropna().reset_index().drop('index',1)\n", "nodes = pd.DataFrame(nodes_list)\n", "nodes.columns = (['user_id'])\n", "nodes = pd.merge(nodes, edges, on='user_id', how='left')\n", "nodes = nodes.drop(columns=['mentions']).groupby(by='user_id').mean().reset_index()\n", "\n", "print(\"La liste des mentions entre les utilisateurs : \\n\",\n", " edges)\n", "print(\"La liste des utilisateurs Twitter ayant publié du contenu relatif à l'IA durant la semaine passée : \\n\",\n", " nodes)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Générer le graphe représentant le réseau social avec le package iGraph\n", "\n", "kol_map = ig.Graph.DataFrame(edges,\n", " directed = True,\n", " vertices = nodes)\n", "kol_map.vs['name'] = nodes['user_id']\n", "kol_map.vs['num_followers'] = nodes['num_followers']*0.001" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Comparer le nombre d'abonnés des utilisateurs du réseau\n", "nodes['num_followers'] = nodes['num_followers'].astype(pd.Int64Dtype())\n", "rank_followers = nodes.sort_values(by='num_followers',\n", " ascending=False)\n", "rank_followers" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Calculer la centralité de degré en utilisant le package igraph\n", "out_degrees = pd.DataFrame({'node': nodes['user_id'],\n", " 'degree':kol_map.degree(mode=\"out\")})\n", "out_degrees = out_degrees.sort_values(by='degree',\n", " ascending=False)\n", "\n", "print(\"Les dix comptes Twitter ayant été le plus mentionnés durant la semaine passée sont : \\n\",\n", " out_degrees.head(10))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Apppliquer la méthode \"Fruchterman-Reingold force-directed\" pour construire le réseau\n", "layout = kol_map.layout('fr')\n", "\n", "visual_style = {}\n", "visual_style[\"vertex_size\"] = kol_map.degree()\n", "visual_style[\"vertex_color\"] = \"#1DA1F2\"\n", "visual_style[\"vertex_label\"] = kol_map.vs[\"name\"]\n", "visual_style[\"vertex_label_size\"] = 5\n", "visual_style[\"edge_arrow_size\"] = 0.5\n", "visual_style[\"layout\"] = layout\n", "visual_style[\"bbox\"] = (500, 500)\n", "visual_style[\"margin\"] = 20\n", "\n", "kol_map0 = kol_map.copy()\n", "visual_style0 = visual_style.copy()\n", "visual_style0[\"vertex_size\"] = kol_map.vs['num_followers']\n", "\n", "# Afficher et sauvegarder les graphes générés\n", "print(\"Carte représentant le réseau d'influence des comptes Twitter du domaine de l'IA : \\n\",\n", " \"(la taille des noeuds est proportionnelle à leur degré sortant) \\n\")\n", "ig.plot(kol_map, \"twitter_network_mapping_degree.pdf\", **visual_style)\n", "\n", "print(\"Carte représentant le réseau d'influence des comptes Twitter du domaine de l'IA : \\n\"\n", " \"(la taille des noeuds est proportionnelle à leur nombre d'abonnés) \\n\")\n", "ig.plot(kol_map0, \"twitter_network_mapping_follower.pdf\", **visual_style0)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4}
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