tag graphs
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Tykayn
tykayn
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# Mastodon tags links
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prends les 100 derniers pouets qui comportent le tag "mastoart" de l'instance https://mastodon.cipherbliss.com, en prenant les pouets suivants si besoin, et regarde dedans quels sont les ancres présentes dans le corps de texte. fais ensuite un graphique de répartition des noms de domaines en fonction de leur décompte.
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https://mastodon.cipherbliss.com/tags/mastoart
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```bash
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pip install mastodon.py
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```
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```bash
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python index.py --access-token=YOUR_ACCESS_TOKEN --hashtag=cargocarryshitolympics
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```
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# Liens entre les URL
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Pour étudier la diversification et les biais de sources d'informations sur Mastodon, on peut voir à quel point les lignes éditoriales des liens donnés sur un sujet via un hashtag en particulier sont uniques ou polarisantes.
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# Trouver des relations entre tags
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Voir les liens entre les différents tags à partir d'un tag donné permet de voir les associations d'idées.
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Une dataframe Pandas permet de mesurer précisément ces liens en les rangeant par nombre d'occurence décroissant.
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On prend les 100 derniers pouets d'une timeline sur un sujet donné, et on fait un rendu en forme de graphique de force pour voir les liens entre les tags (propriété des status donnés par l'api de mastodon.py).
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Le tout est sauvegardé dans un graphique png nommé par le nom du tag d'origine en recherche, tel que graph_tags_le-hashtag.png
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<?xml version="1.0" encoding="utf-8" standalone="no"?>
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<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN"
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"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
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<svg xmlns:xlink="http://www.w3.org/1999/xlink" width="460.8pt" height="345.6pt" viewBox="0 0 460.8 345.6" xmlns="http://www.w3.org/2000/svg" version="1.1">
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<metadata>
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<rdf:RDF xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:cc="http://creativecommons.org/ns#" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
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<cc:Work>
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<dc:type rdf:resource="http://purl.org/dc/dcmitype/StillImage"/>
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<dc:date>2024-06-11T12:17:24.842901</dc:date>
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<dc:format>image/svg+xml</dc:format>
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<dc:creator>
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<cc:Agent>
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<dc:title>Matplotlib v3.6.3, https://matplotlib.org/</dc:title>
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</cc:Agent>
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</dc:creator>
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</metadata>
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<defs>
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<style type="text/css">*{stroke-linejoin: round; stroke-linecap: butt}</style>
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</defs>
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<g id="figure_1">
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<g id="patch_1">
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<path d="M 0 345.6
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" style="fill: #ffffff"/>
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</g>
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</g>
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</svg>
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import mastodon
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import pandas as pd
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import networkx as nx
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import matplotlib.pyplot as plt
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import argparse
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from urllib.parse import urlparse
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import seaborn as sns
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# Parse les arguments du script
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parser = argparse.ArgumentParser(description="Produit un graphique des tags")
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parser.add_argument("--access-token", type=str, help="The access token of your mastodon app", required=True)
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parser.add_argument("--hashtag", type=str, help="The hashtag to search for (required)", required=True)
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args = parser.parse_args()
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MASTODON_INSTANCE = 'https://mastodon.cipherbliss.com'
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ACCESS_TOKEN = args.access_token
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hashtag = args.hashtag
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MAX_RESULTS = 500
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mastodon = mastodon.Mastodon(
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access_token=ACCESS_TOKEN,
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api_base_url=MASTODON_INSTANCE
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)
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posts = mastodon.timeline_hashtag(hashtag, limit=MAX_RESULTS)
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print('posts trouvés:', len(posts))
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##################################
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urls = []
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domains = []
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for status in posts:
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content = status['content']
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urls = [item['url'] for item in status.get('media\_attachments', []) + status.get('cards', [])] if status.get('media\_attachments') or status.get('cards') else []
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if urls:
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domain = urllib.parse.urlparse(urls[-1]).netloc
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domains.append(domain)
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urls.pop()
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df = pd.DataFrame({'URL': urls, 'Domain': domains})
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plt.figure(figsize=(8,6))
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ax = sns.countplot(data=df, x='Domain')
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ax.set_title('Occurrences of Domains in Fetched Posts')
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ax.set_xticklabels(ax.get_xticks(), rotation=90)
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# Enregistrement du graphe sous forme de fichier PNG
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plt.savefig(f'barres_liens_tag_{hashtag}.png')
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##################################
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# Création d'une dataframe pour stocker les associations entre tags
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df = pd.DataFrame(columns=['post_id', 'tags'])
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# Parcours des posts et stockage des informations dans la dataframe
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for post in posts:
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post_id = post['id']
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tags = [tag['name'] for tag in post['tags']]
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df.loc[len(df)] = [post_id, ', '.join(tags)]
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df_list = []
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for index, row in df.iterrows():
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tags = row['tags'].split(',')
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df_list.append(tags)
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df_tags = pd.DataFrame(df_list)
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#########################
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# Génère le réseau social
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G = nx.Graph()
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for i, row in df.iterrows():
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for tag in row['tags'].split(','):
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G.add_node(tag)
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for other_tag in row['tags'].split(','):
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if tag != other_tag:
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G.add_edge(tag, other_tag)
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plt.figure(figsize=(20,15))
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# Génère les positions des noeuds
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pos = nx.spring_layout(G)
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# Définisse la taille des noeuds en fonction du nombre de liens
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node_sizes = [len(list(G.neighbors(node))) for node in G.nodes()]
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# Determine the maximum node size
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max_node_size = max(node_sizes)
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# Scale the node sizes so that they are all relatively equal, but not too large
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node_sizes_scaled = [size / max_node_size * 1000 for size in node_sizes]
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# Dessine le graphique de force
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nx.draw_networkx_nodes(G, pos, node_size=node_sizes_scaled, node_color='skyblue', alpha=0.7)
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# Dessine les liens entre les noeuds
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# nx.draw_networkx_edges(G, pos, width=2, edge_color='#ccc')
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# Génère les arêtes du graphe avec des épaisseurs de traits qui dépendent du poids des noeuds
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edge_widths = [len(list(G.neighbors(node)))/3 for node in G.nodes()]
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nx.draw_networkx_edges(G, pos, width=edge_widths, edge_color='gray')
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# Ajoute un contour blanc aux labels des noeuds pour améliorer la lisibilité
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labels = nx.get_node_attributes(G, 'name')
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nx.draw_networkx_labels(G, pos, font_size=14, font_color='black' )
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print('longueur du graphe: ',len(G))
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plt.savefig(f'network_graph_tag_{hashtag}.svg')
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##############################
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# Génération d'un graphique en barre des occurrences des tags
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tag_counts = df['tags'].str.split(',').explode().value_counts().sort_values(ascending=False)
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plt.figure(figsize=(10, 6))
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plt.barh(tag_counts.index, tag_counts.values)
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plt.xlabel('Occurrences')
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plt.ylabel('Tags')
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plt.title('Tag Occurrences')
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# Enregistrement du graphe sous forme de fichier PNG
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plt.savefig(f'barres_tag_{hashtag}.png')
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<?xml version="1.0" encoding="utf-8" standalone="no"?>
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Reference in New Issue