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.

1. Social Network Analysis With
Python
@ PyCon APAC 2014David Chiu

2. About Me
Co-founder of
Ex-Trend Micro Engineer
NumerInfo
ywchiu.com

3. Social Network
http://libeltyseo.com/wp-content/uploads/2013/03/social-networking.png

4. Human Nature
http://cdn.macado.com/assets/2010/03/peeping-tom.gif

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?

6. Explore Facebook

7. OAuth2 Flow
Open standard for authorization. OAuth provides a method for clients to
access server resources on behalf of a resource owner

8. Connect to Facebook
https://developers.facebook.com/

9. Get Access Token
https://developers.facebook.com/tools/explorer/

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

24. Text Mining
NLTK!

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] ]

31. Draw Network Plot
nx.draw(nxg)

32. Calculate Network Property
betweenness_centrality(nxg)
degree_centrality(nxg)
closeness_centrality(nxg)

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()

35. Community Partitioned Plot

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