1. A Comparative Study of Users’
Microblogging Behavior on Sina Weibo
and Twitter
UMAP, Montréal, July 2012
Qi Gao1, Fabian Abel1, Geert-Jan Houben1,Yong Yu2
1 Web Information Systems, Delft University of Technology
2 APEX lab, Shanghai Jiao Tong University
Delft
University of
Technology

2. What we study: microblogging behavior
340,000,000 100,000,000
500,000,000 250,000,000
What are the differences in Chinese and
Western users’ microblogging behavior?
Wikipedia:Twitter Wikipedia:Sina_Weibo
2 Microblogging Behavior on Sina Weibo and Twitter

3. Cultural differences
standing up for himself acting as a member of a group
Flickr:semicharmed Flickr:webei
3 Microblogging Behavior on Sina Weibo and Twitter

4. Data sources
follower/followee
network
limited length of
a post
repost/reply/like
use of URLs
and hashtags
meta information
(time, source)
4 Microblogging Behavior on Sina Weibo and Twitter

5. User modeling and analysis framework
syntactic analysis sentiment analysis
Cultural
Analytics
semantic analysis temporal analysis
Data Processing and User Profiling
data acquisition multilingual NER
metadata extraction semantic enrichment
topic/interest modeling user profile construction
Microblogging Platforms Semantic Web
5 Microblogging Behavior on Sina Weibo and Twitter

6. User profiling
Profile
What could go wrong? RT
?
@DRUGE_REPORT
Washington: White House
seeks ‘Control’ over
communication during ‘crisis’
#obama drudge.tw/LVZygj
6 Microblogging Behavior on Sina Weibo and Twitter

7. User profiling – syntactic characteristics
Profile
What could go wrong? RT # hashtag/URL
@DRUGE_REPORT
Washington: White House
seeks ‘Control’ over
communication during ‘crisis’
#obama drudge.tw/LVZygj
7 Microblogging Behavior on Sina Weibo and Twitter

8. User profiling – semantic characteristics
Profile
What could go wrong? RT # hashtag/URL
@DRUGE_REPORT
entity
Washington: White House
seeks ‘Control’ over
communication during ‘crisis’
#obama drudge.tw/LVZygj
8 Microblogging Behavior on Sina Weibo and Twitter

9. User profiling – semantic characteristics
Profile
What could go wrong? RT # hashtag/URL
@DRUGE_REPORT
entity
Washington: White House
seeks ‘Control’ over T topic
communication during ‘crisis’
#obama drudge.tw/LVZygj
topic:person
topic:location
topic:organization
9 Microblogging Behavior on Sina Weibo and Twitter

10. User profiling – sentiment characteristics
Profile
What could go wrong? RT # hashtag/URL
@DRUGE_REPORT
entity
Washington: White House
seeks ‘Control’ over T topic
communication during ‘crisis’ sentiment
#obama drudge.tw/LVZygj
positive
negative
neutral
10 Microblogging Behavior on Sina Weibo and Twitter

11. User profiling – temporal characteristics
Profile
What could go wrong? RT # hashtag/URL
@DRUGE_REPORT
entity
Washington: White House
seeks ‘Control’ over T topic
communication during ‘crisis’ sentiment
#obama drudge.tw/LVZygj
temporal information
11 Microblogging Behavior on Sina Weibo and Twitter

12. Analysis of users’ microblogging behavior
• Datasets
• Microblog data collected from Sina Weibo and Twitter over a period of
three months
• > 46 million micropost overall – 22m from Sina Weibo and 24m from
Twitter
• a sample of 2616 Sina Weibo users and 1200 Twitter users
• Analyze and compare user behavior on Sina Weibo and
Twitter
• on two levels (i) the entire user population and (ii) individual users
• from different angles (i) syntactic, (ii) semantic, (iii) sentiment and
(iv) temporal analysis
• relate our findings to theories about cultural stereotypes (Hofstede’s
cultural dimensions)
12 Microblogging Behavior on Sina Weibo and Twitter

13. Cultural model: Hofstede’s cultural dimensions
• Describes stereotypical cultural characteristics of nationalities
• Five core dimensions:
• Power Distance (PDI)
• Individualism versus Collectivism (IDV)
• Masculinity versus Femininity (MAS)
• Uncertainty Avoidance (UAI)
• Long-Term Orientation (LTO)
• Scores are relative wrt. other
nationalities
geert-hofstede.com
13 Microblogging Behavior on Sina Weibo and Twitter

14. Syntactic analysis – what are the syntactical
characteristics of messages?
hashtags/URLs per post
Hashtag-Weibo hashtag-Twitter
URL-Weibo URL-Twitter
avg. number of
1 Hashtag-Twitter
URL-Twitter
0.1
0.01
URL-Weibo
hashtag-Weibo
0
0% 20% 40% 60% 80% 100%
users
Hashtags and URLs are less Users on Twitter are more triggered by
frequently applied on Sina hashtags and URLs when propagating
Weibo than on Twitter. information than on Sina Weibo.
14 Microblogging Behavior on Sina Weibo and Twitter

15. Syntactic analysis – what are the syntactical
characteristics of messages?
high collectivism Cultural high individualism
(Sina Weibo) Differences (Twitter)
Hashtags and URLs are less Users on Twitter are more triggered by
frequently applied on Sina hashtags and URLs when propagating
Weibo than on Twitter. information than on Sina Weibo.
15 Microblogging Behavior on Sina Weibo and Twitter

16. Semantic analysis – what kind of topics are discussed?
avg. number of entities per post
10
Weibo Weibo
1
Twitter
0.1 Twitter
0.01
0.001
0
0% 20% 40% 60% 80% 100%
users
The topics that users discuss on Sina Weibo are to a large
extent related to locations and persons. In contrast to Sina
Weibo, users on Twitter are talking more about
organizations (such as companies, political parties).
16 Microblogging Behavior on Sina Weibo and Twitter

17. Semantic analysis – what kind of topics are discussed?
high collectivism Cultural high individualism
(Sina Weibo) Differences (Twitter)
The topics that users discuss on Sina Weibo are to a large
extent related to locations and persons. In contrast to Sina
Weibo, users on Twitter are talking more about
organizations (such as companies, political parties).
17 Microblogging Behavior on Sina Weibo and Twitter

18. Sentiment analysis – what are the sentiment
characteristics of microposts? Weibo
100%
ratio of positve posts
Weibo
80％
Twitter
Twitter
60% more positive posts
more negative posts
40%
20%
0%
0% 20% 40% 60% 80% 100%
users
Sina Weibo users have a stronger tendency to publish
positive messages than Twitter users.
18 Microblogging Behavior on Sina Weibo and Twitter

19. Combining semantic and sentiment analysis
The difference is amplified when discussing ‘people’ or
‘location’, with Sina Weibo users even more positive and
Twitter users more negative.
19 Microblogging Behavior on Sina Weibo and Twitter

20. Combining semantic and sentiment analysis
long-term orientation Cultural short-tem orientation
Differences
(Sina Weibo) (Twitter)
The difference is amplified when discussing ‘people’ or
‘location’, with Sina Weibo users even more positive and
Twitter users more negative.
20 Microblogging Behavior on Sina Weibo and Twitter

21. Temporal analysis – how quickly do users propagate
information?
time distance (in hours)
Weibo Weibo
1000
Twitter
100
10
1 Twitter
time distance =
0.1
trepost - toriginal post
0
0% 20% 40% 60% 80% 100%
users
Twitter users repost messages faster than
Sina Weibo users.
21 Microblogging Behavior on Sina Weibo and Twitter

22. Temporal analysis – how quickly do users propagate
information?
large degree of Cultural low degree of
power distance Differences power distance
(Sina Weibo) (Twitter)
Twitter users repost messages faster than
Sina Weibo users.
22 Microblogging Behavior on Sina Weibo and Twitter

23. Qi Gao et al. Information Propagation Cultures on Sina Weibo and Twitter. In
Proceedings of ACM Web Science Conference 2012. Evanston, USA.
23 Microblogging Behavior on Sina Weibo and Twitter

24. Conclusion and future work
• What we did
• user modeling framework for culture-aware user modeling based on
microblogging data
• data-intensive analyses deliver valuable insights for multilingual and culture
-aware user modeling
• Findings
• key differences between Chinese and US/Western users’ microblogging behavior
– e.g. Chinese microblogging activities are more positive and less ‘political’
• some of the differences can be explained with cultural model from social science
research – e.g. Hofstede: individualism vs. collectivism
• Future work:
• develop personalized applications that are able to adapt to the cultural factors
24 Microblogging Behavior on Sina Weibo and Twitter

25. Thank You!
Q&A
Qi Gao, Fabian Abel, Geert-Jan Houben, Yong Yu
q.gao@tudelft.nl
@wisdelft
25 Microblogging Behavior on Sina Weibo and Twitter

26. Interpretation
Individualism Cultural
/Collectivism Differences
Twitter users seem to be
more eager to let their posts
appear in the public
discussion – possibly a higher
demand to profile themselves
(individualism)
26 Microblogging Behavior on Sina Weibo and Twitter

27. Interpretation
Individualism Cultural
/Collectivism Differences
The finding is in line with the
low commitment to an
organization in China, which is
one of the typical indicator for
a highly collectivist culture.
27 Microblogging Behavior on Sina Weibo and Twitter

28. Interpretation
Long Term Cultural
Orientation Differences
The positive nature of the
information on Sina Weibo
might point at the long term
orientation that is attributed
to the Chinese culture.
28 Microblogging Behavior on Sina Weibo and Twitter

29. Interpretation
Power Cultural
Distance Differences
Twitter users may have the
impression that they play an
important role in the
information propagation
process, i.e. they act as if they
are in the power of spreading
information (power distance).
29 Microblogging Behavior on Sina Weibo and Twitter