PyCon APAC 2014 - Social Network Analysis Using Python (David Chiu)
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This document discusses using Python for social network analysis on Facebook data. It provides examples of: - Connecting to the Facebook API and obtaining an access token - Retrieving user and friend data via API calls - Analyzing likes on posts to determine who likes a user's posts the most - Performing text mining on post messages using NLTK and Jieba to determine popular topics - Modeling the friendship network as a graph and using NetworkX and community detection to identify groups within the social network.
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PyCon APAC 2014 - Social Network Analysis Using Python (David Chiu)
- 1. Social Network Analysis With Python @ PyCon APAC 2014David Chiu
- 2. About Me Co-founder of Ex-Trend Micro Engineer NumerInfo ywchiu.com
- 5. What do we want to know? Who knows whom, and which people are common to their social networks? How frequently are particular people communicating with one another? Which social network connections generate the most value for a particular niche? How does geography affect your social connections in an online world? Who are the most influential/popular people in a social network? What are people chatting about (and is it valuable)? What are people interested in based upon the human language that they use in a digital world?
- 7. OAuth2 Flow Open standard for authorization. OAuth provides a method for clients to access server resources on behalf of a resource owner
- 10. User Permission
- 11. Permission List User Data Permissions: user_hometown user_location user_interests user_likes user_relationships Friends Data Permissions: friends_hometown friends_location friends_interests friends_likes friends_relationships Extended Permissions: read_friendlists
- 12. Copy Token
- 13. Social Network Analysis With Python Let's Hack
- 14. Get Information From Facebook
- 15. Test On API Explorer
- 16. Required Packages requests Sending HTTP Request to Retrieve Data From Facebook json For Parsing JSON Format
- 17. Facebook Connect import requests import json access_token="<access_token>" url = "https://graph.facebook.com/me?access_token=%s" response = requests.get(url%(access_token)) fb_data = json.loads(response.text) print fb_data
- 18. Question: Who Likes My Post The Most?
- 19. Get Likes Count of Posts access_token = '<access_token>' url="https://graph.facebook.com/me/posts?access_token=%s" response = requests.get(url%(access_token)) fb_data = json.loads(response.text) count_dic = {} for post in fb_data['data']: if 'likes' in post: for rec in post['likes']['data']: if rec['name'] in count_dic: count_dic[rec['name']] += 1 else: count_dic[rec['name']] = 1
- 20. Simple Ha! Ask Harder Question!
- 21. Question: What's People Talking About
- 22. Take Cross-Strait Agreement As Example keyword_dic = {} posts_url = 'https://graph.facebook.com/%s/posts?access_token=%s' post_response = rs.get(posts_url%(userid, access_token)) post_json = json.loads(post_response.text) for post in post_json['data']: if 'message' in post: m = re.search('服貿', post['message'].encode('utf-8')) if m: if userid not in keyword_dic: keyword_dic[userid] = 1 else: keyword_dic[userid] += 1
- 25. Sorry! My Facebook Friends Speak In Mandarin Jieba!
- 26. Using Jieba For Word Tokenization import jieba data = post_json['data'] dic = {} for rec in post_json['data']: if 'message' in rec: seg_list = jieba.cut(rec['message']) for seg in seg_list: if seg in dic: dic[seg] = dic[seg] + 1 else: dic[seg] = 1
- 27. Question: How to Identify Social Groups?
- 28. Required Packages networkx Analyze Social Network community Community Detection Using Louvain Method
- 29. Social Network Man As , Connection AsNode Edge
- 30. Build Friendship Matrix import networkx as nx mutual_friends = {} for friend in friends_obj['data']: mutual_url = "https://graph.facebook.com/%s/mutualfriends?access_token=%s" res = requests.get( mutual_url % (friend['id'], access_token) ) response_data = json.loads(res.text)['data'] mutual_friends[friend['name']] = [ data['name'] for data in response_data ] nxg = nx.Graph() [ nxg.add_edge('me', mf) for mf in mutual_friends ] [ nxg.add_edge(f1, f2) for f1 in mutual_friends for f2 in mutual_friends[f1] ]
- 33. Community Detection import community def find_partition(graph): g = graph partition = community.best_partition(g) return partition new_G = find_partition(nxg)
- 34. Draw Social Network Communities import matplotlib.pyplot as plt size = float(len(set(new_G.values()))) pos = nx.spring_layout(nxg) count = 0. for com in set(new_G.values()) : count = count + 1. list_nodes = [nodes for nodes in new_G.keys() if new_G[nodes] == com] nx.draw_networkx_nodes(nxg, pos, list_nodes, node_size = 20, node_color = str(count / size)) nx.draw_networkx_edges(nxg,pos, alpha=0.5) plt.show()
- 36. Gephi Gephi, an open source graph visualization and manipulation software
- 37. One More Thing
- 38. To build your own data service
- 39. jsnetworkx A JavaScript port of the NetworkX graph library.
- 40. juimee.com
- 41. THANK YOU