mapping the landscape of research on chinese...
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Mapping the Landscape of Research on Chinese Social Media: What We Know and What We Don’t Know
Jonathan J. H. Zhu with T. Q. Peng, H. Liang, J. Qin, Z. Z. Wang, H. X. Chen, & B. L. Li
Research Seminar, October 6, 2014 Dept. of Media & Communication, CityU
“The Mapping Project”
Mapping the landscape of Internet studies (Peng et al., 2013, New Media & Society)
Mapping the intellectual structure of e-Health research (Jiang et al., 2015, International Journal of Medical Informatics)
Mapping the landscape of research on Chinese social media
2
3
Source: http://www.techinasia.com/china-social-media-landscape-infographic-2014/
Mapping Landscape of Chinese Social Media
• IM • Vdeo/Music • Blog • Microblog • SNS • BBS • Mobile Social • Social Life • E-Commerce
4
Mapping Research on Chinese Social Media?
We need an organizational framework!
The 5W Model for Assessment
Harold Lasswell (1948): Communication is a
process of who says what to whom through which
channel with what effects.
5
Communicator Content Channel Audience Effects
6
SCI/SSCI Publications on Chinese Social Media (N=138)
0%
20%
40%
60%
2009 2010 2011 2012 2013 2014
a. Annual Growth
SCI (103)
SSCI (35)
d. Leading Institutions Rank SCI SSCI
1 CAS Nanyang 2 Tsinghua Tsinghua 3 Peking Peking 3 Beihang Beihang 3 BUPT CityU HK 3 SJTU HUST
26% 46%
12% 14% 14%
30%
44% 14%
0%20%40%60%80%
100%
SCI SSCI
b. Leading Disciplines
Computer
Engineering
Business
Psych
Communication
Others
c. Leading Nationals
SCI SSCI
Distribution across 5Ws
Communicator 16%
Content 15% Channel
5%
Audience 29%
Effects 16%
System Design 19%
For Communicator
4%
For Audience 7%
For Content 6%
Research Method 17%
7
Differences between SCI and SSCI
SCI (N=103) SSCI (N=35)
Method 19%
System 20% Effects
33%
Audience 25% Audience
24%
Content 19%
% o
f Stu
dies
8
9
Common Themes Emerged
7. Opinion Leaders 8. Privacy Concerns 9. Recommender System 10. Social Capital, Social
Movements, Collective Actions
11. Spammer/Zombie Detection
12. User Behavior, Usage Pattern
1. Classification/Clustering 2. Cross-Site Comparison 3. Community /Group
Detection 4. Hot/Crisis Events 5. Information Diffusion
/Propagation 6. Keywords /Opinion
/Sentiment Extraction
Common Themes by 5Ws
Communicator Content Audience Channel Effects
Opinion Leaders Topic Classification Information Diffusion
Spammer /Zombie
Detection
Keywords /Opinion
/Sentiment Extraction
User Behavior;
Usage Pattern
Cross-Site Comparison
Social Capital; Collective
Actions
Recommender System
Hot/Crisis Events
Community /Group
Detection
Privacy Concerns
10 SCI; SSCI; Both
11
1. WHO (Communicator)
What we know: Media professionals Elite opinion leaders Government mouthpieces Rumor mills (“50-cent
Party”, “Water Army”, Spammers, etc.)
What we don’t know: Grassroots opinion
leaders E-marketers Foreign
individuals/entities
Communicators on Early Weibo (N=10)
0
5
10
15
20
Media (N=2) Ordinary Users (N=4) Grassroots OpinionLeaders (N=2)
Celebrities & VIPs(N=2)
N o
f Pos
ts/D
ay
Source: Xie (2010, in Chinese). Structure and mechanisms of interaction on Sina Weibo. 12
Government Presence on Social Media
Unique features: o Centralized review process o Widespread “Water Army” o Adaption of international practices
Fundamental problem: o Open-networked society vs. closed-centralized government
13 Source: Zhang (2013). Social media in Chinese government.
14
Growth of Weibo Accounts by Police Bureaus
Source: Ma (2013). The diffusion of government microblogging.
15
Predictors of Use of Weibo by Police Bureaus
Significant predictors: Government size Population size Adoption of neighbors Adoption of upper-level
Non-significant predictors: Revenue GRP Openness Safety level E-government efforts User size
Source: Ma (2013). The diffusion of government microblogging.
16
2. Says WHAT (Content)
What we know: Multiple duplicates Contentious Censorship
What we don’t know: Benchmark categorization Non-political content
(e.g., entertainment) Forced vs. self-censorship
Categories of Early Weibo Posts (N=438)
News 31%
Diaries 28%
Jokes 19%
Quotes 10%
Promotions 6%
Others 4%
Comments 3%
17 Source: Xie (2010, in Chinese). Structure and mechanisms of interaction on Sina Weibo.
Who Say What on Early Weibo
52% 36%
33%
71%
42%
36%
22%
19% 8%
0%
25%
50%
75%
100%
Media Ordinary Users GrassrootsOpinion Leaders
Celebrities &VIPs
CommentsOthersPromotionsQuotesJokesDiariesNews
18 Source: Xie (2010, in Chinese). Structure and mechanisms of interaction on Sina Weibo.
Sina Weibo vs. Twitter on Content
Weibo Twitter
Trending Topics Trivial jokes News events around the world
Trend Setters Unverified accounts Leading news media
Trendy Topics Retweeted 62% 31%
Presentation content Images, videos, links Text
Source: Yu et al. (2011). What trends in Chinese social media. 19
What Might Cause the Differences?
The higher rate of retweeting is likely to be created by spammers, i.e., the accounts that were set up to retweet (promote) targeted users, which also promote the relevant topics to be trending.
About 1% of the users are suspected spammers, who contribute 1/3 of the total posts or half of the retweeted posts.
If the spammers are removed, the evolution patterns of trending topics on Weibo becomes similar to that on Twitter.
20 Source: Yu et al. (2012). Artificial Inflation.
Detecting Spammers by User-Retweet Ratio
0%
25%
50%
75%
100%
35 25 15 10 9 8 7 6 5 4 3 2 1
% o
f Sam
ple
N of Users Retweeted by the Account
Inactive AccountActive Account
21 Source: Yu et al. (2012). Artificial Inflation.
Chinese Censorship
King et al. (2013): The largest selective suppression of human expression
in history: o implemented manually (within a few hours of posting) o by 200,000 workers o located in government and inside social media firms
A huge censorship organization: o (obviously) designed to suppress information o (paradoxically) very revealing about the goals, intentions,
and actions of the Chinese leadership
22 Source: King et al. (2013). Reverse engineering Chinese censorship.
23
What’s Censored
Source: King et al. (2013).
24
On the Ongoing Events in Hong Kong
25
3. Through WHICH Channel
What we know: Weibo concentrated Compared with Twitter Compared with old media
What we don’t know: Other social media, e.g.,
o IM (WeChat) o BBS (Tianya) o SNS (QQ) o Blogs, dating, gaming, etc.
Competition/cooperation among social media
26
Blogs vs. Microblogs
Method: 300 Sina blog posts on
traveling to Hong Kong 300 Sina Weibo posts on
the same topic Manual content analysis
of the frequency, direction, and intensity of keywords, themes, visuals, etc.
Findings: Blog posts largely to
recount past experience Weibo posts mainly to
express desire to visit in advance
No formal comparison made as the two samples treated as replicates rather than contrasts
Source: Tse & Zhang (2012). Analysis of Blogs and Microblogs
27
Competitor or Partners
PPM Model: Method: Drew a random sample of
Sina users Mined their blog/Weibo
posts to measure behavior Invited them to answer an
online questionnaire to measure motivation
Found most users to co-use both simultaneously
Source: Lu (2013). The sustainability of UGC.
Switching from Blogging to
Microblogging?
Push Forces (satisfaction)
Pull Forces (alternative
attraction, etc.)
Mooring Forces (perceived
popularity, etc.)
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4. To WHOM (Audience)
What we know: Massive size Skewed participation Polarized views
What we don’t know: User authenticity Passive audience Inactive audience
Massive Size of Social Media Users
0
250
500
750
1000
Social media users(CNNIC)
Sina Weibo WeChat QQ
Mill
ion
Sources: CNNIC survey report; Sina/Tencent annual reports
Registered Monthly Active Daily Active
29
Changes in Popularity of Chinese Social Media
0%
25%
50%
75%
100%
Dec-08 Dec-09 Dec-10 Dec-11 Dec-12 Dec-13 Jun-14
% o
f Web
Use
rs S
urve
yed
Source: CNNIC Annual Survey Reports (www.cnnic.cn)
IMBlogMicroblogSNSBBS
30
31
Political Preference of Dominant Groups
Source: Wu (in press). Ideological polarization.
32
A 3-step Flow Model of Information Diffusion
Source: Chen (2013). Opinion Leaders: The Driving Force of Political Discussion in Social Media
Distinctions between Communicator and Audience
Participation Duration Content Creation Content Consumption
Continuous I. Active Communicator III. Active Audience
Interrupted II. Inactive Communicator (traceable)
IV. Inactive Audience (completely ignored)
33 Source: Zhu et al. (2014, in Chinese). Computational social science.
34
5. With WHAT Effects
What we know: Registration = Exposure Exposure = Conversion Expression = Participation
What we don’t know: Definition of effects Links to content Links to offline life Effects on individuals vs.
society
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Expression vs. Participation
Arguments: Weibo helps rural migration workers to mobilize to improve their
life in cities; release emotional
dissatisfactions with reality;
influenced by journalists, scholars, and officials (unreciprocal weak ties?).
Method: Searched “new generation
of rural workers” (新生代农民工) to obtain 4,000+ posts;
Content analyzed the themes of the posts;
Results considered to support/derive the arguments.
Source: Zhang (2013). Social inclusion or exclusion?
Evidence for Arguments 1-2 (?)
0%
10%
20%
30%
40%
50%
Information Emotions Actions Others
Non-Emotion Emotion
Source: Zhang (2013). Social inclusion or exclusion? 36
37
Agenda-setting Effects based on N of Posts
Hypotheses: “Safety valve” effects:
bloggers express discontent on issues initiated by the media;
“Pressure cooker” effects: bloggers lead issues to increase social tensions.
Method: Randomly searched
through Google 2,000+ blog posts and 4,000+ newspaper stories;
Computerized content analysis of issue themes;
Time series analysis to determine causal directions between the two agendas.
Source: Hassid (2012). Safety valve or pressure cooker.
38
Safety Valve or Pressure Cooker?
Findings: Rather than simply being a ‘‘safety valve’’ that reduces bloggers’ anger over political or social problems, or a pressure cooker that increases these tensions, the results of this study suggested a more nuanced perspective in which the Chinese blogosphere can act as either, depending on the topic.
Safety value issues: o Energy; o Politics; o Corruption and illegality; o Sino-Japan relations
Pressure cooker issues: o Internet; o Religion; o Rural; o Arts; o Disasters.
Source: Hassid (2012). Safety valve or pressure cooker.
39
Summary of Empirical Evidence
What we know enough: Massive size of users Content censored or
manipulated Active and influential
opinion leaders
What we know little: Does the size matter? Is the censorship
effective? Causality: Does social
media bring social changes?
Discussion 1: State of the Art
Research on Chinese social media is a fast growing and increasingly popular field; prevailed by optimism by liberalists, marketers,
investors, and even the new generation of Chinese leadership;
commonly examined with case studies, focusing on crisis events, controversial issues, or active groups;
shortage of credible evidence for causal connections.
40
Discussion 2: Common and Old Problems
The conceptual enthusiasm and empirical shortfall are not specific to research on Chinese social media;
The same patterns occur in research on social media in general, probably at a slightly milder scale;
These are also longstanding issues in research on traditional media worldwide;
One common root of the above domains lies in the difficulty in getting necessary and quality data for the relevant research questions.
41
Price-Earning Ratio of Social Media
-50
0
50
100
150
Sina Tencent Renren Weibo Facebook Google Twitter
42
Industry Average Price-Earning Ratio
0
10
20
30
40
50
60
70
80
90
100
Internet Telecomm Oil Banking Real Estate
Pric
e/Ea
rnin
g
ChinaU.S.
43
Discussion 3: Call for Smarter Research Design
Need thoughtful and creative research designs, e.g., o online experiments to ensure causal inference o individual-level analysis to ensure ecological validity o cross-national comparisons to enhance generalizability
44
45
Discussion 4: Opportunities and Challenges
Opportunities: Unique characteristics of
the ecosystem behind GFW
Active and sizable scholars in China for possible collaboration
Increasing availability of open/affordable data for academic research
Challenges: How to engage in and
contribute to mainstream research?
How to find a dream collaborator with complementarity?
How to evaluate data quality and interpret peculiar or counter-intuitive results?
Cited Works 1. Chen, C. (2013). Opinion leaders: The driving force of political discussion in social media. Master’s
thesis, Middle Tennessee State University. 2. Hassid, J. (2012). Safety valve or pressure cooker? Blogs in Chinese political life. Journal of
Communication, 62, 212-230. 3. King, G., Pan, J., & Roberts, M. E. (2013). Reverse engineering Chinese censorship through
randomized experimentation and participant observation. Presented at the annual meeting of American Political Science Association, Chicago.
4. Lu, H. (2013). The sustainability of UGC: Understanding continuance and switching produsage Behavior. Ph.D. Dissertation, City University of Hong Kong.
5. Tse, T. S. M., & Zhang, E. Y. L. (2013) Analysis of blogs and microblogs: A case study of Chinese bloggers sharing their Hong Kong travel experiences. Asia Pacific Journal of Tourism Research, 18(4), 314-329.
6. Wu, A.X. (In press). Ideological polarization over a China-as-superpower mindset: An exploratory charting of belief systems among Chinese Internet users, 2008-2011. International Journal of Communication.
7. Xie, Y. H. (2010, in Chinese). Structure and mechanism of interactions on Sina Weibo: An empirical study. Journalism & Communication Research (Xinwen yu Chuanbo Yanjiu), 4, 60-69.
8. Yang, G. B. (2010). The power of the Internet in China: Citizen activism online. Columbia University Press.
46
Cited Works (cont’d) 9. Yin, L. G. (2010, in Chinese). Framing the Deng Yujiao case: How online opinion impacts offline
media reports. International Journalism (Guoji Xinwenjia), 2010(9), 25-31.
10. Yu, L., Asur, S., & Huberman, B. A. (2011). What trends in Chinese social media. Proceedings of The fifth ACM workshop on Social Network Mining and Analysis. ACM.
11. Yu, L. L., Asur, S., & Huberman, B. A. (2012). Artificial inflation: The real story of trends and trend-setters in Sina Weibo. ASE/IEEE International Conference on Social Computing.
12. Zhang, P. Y. (2013). Social inclusion or exclusion? When Weibo (Microblogging) meets the “New Generation” of rural migrant workers. Library Trends, 62(1), 63-80.
13. Zhang, L. (In press). Social media in Chinese government: Drivers, challenges and capabilities. Government Information Quarterly.
14. Zhou, Y. Q., & Moy, P. (2007). Parsing framing processes: The interplay between online public opinion and media coverage. Journal of Communication, 57, 79-98.
15. Zhu, J. J. H., Peng, T. Q., Liang, H., Wang, C. J., Qin, J., & Chen, H. X. (2014, in Chinese). Computational social science in communication research. e-Science Technology and Applications (Keyan Xinxihua Jishu he Yingyong), 5(2), 3-13.
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THANK YOU & CONTACT US @
mailto: [email protected] http://weblab.com.cityu.edu.hk
Diverse Research Outlets Official statistical reports:
o e.g., CNNIC biannual surveys Listed-firm financial reports:
o e.g., Sina, Tencent, etc. Web traffic monitors:
o e.g., Alexa, Dratio (wrating.com), etc. Marketing/consulting research reports:
o e.g., McKinsey, iResearch, etc. Academic research papers:
o English language; Chinese language
49
50
Accurate Data vs. Insightful Analysis Ac
cura
cy
Insightfulness
Revenue Model of Top 4 Social Media, 2011
0
50
100
150
200
250
300
Tencent Easenet Sina Sohu
OthersGamesAd
Sources: the 2011 financial reports of Tencent, Easenent, Sina, and Sohu 51
52
CNNIC: The 34th Internet User Survey Report (July 2014)
By June 2014:
Total users = 632M
Penetration rate = 47%
Mobile users = 83%
0%
25%
50%
75%
100%
IM Blog
Microblog
SNS
BBS
Users of Social Media
53
McKinsey Global Institute Report (July 2014) China’s digital transformation: The Internet’s impact on productivity and growth
Acknowledgement: “... This independent MGI initiative drew on data and expertise from the Alibaba Group Research Center and Baidu Development Research Center. Part of our analysis was made possible through this collaboration...”
Cover-page data:
54
Power of Social Media “Readers seeking predictions about how all of these changes will affect political outcomes in China over the next few decades are likely to be frustrated. This is not intended as a predictive book, and Mr. Yang wisely makes no attempt to forecast future events based on current trends. But for an in-depth understanding of how life has changed for China’s 300 million plus Internet users, and how these personal transformations have in turn affected contemporary Chinese society and culture, Mr. Yang’s work is essential reading.”
Growth of SSCI Publications on Social Media
0
10
20
30
40
50
60
70
80
90
100
0
200
400
600
800
1000
1200
2006 2007 2008 2009 2010 2011 2012 2013
N o
f Art
icle
s on
PRC
per Y
ear
N o
f Tot
al A
rtic
les p
er Y
ear
TotalChina
55
Disciplines Involved in Social Media Research
0%
25%
50%
75%
100%
Total (N=4600+) China (N=280)
OtherHumanitiesCommunicationHealth & EnvirnonmentEducationSocial SciencesBusiness & EconomicsScience & TechnologyComputer & Information
56
57
WB
58
59
60
61
62
63
64
Studies on Chinese Social Media at WISA2014
Jing Luo and Lizhen Xu: Identification of Microblog Opinion Leader Based on User Feature and Interaction Network
Chong Kuang, Zhiyuan Liu, Maosong Sun, Feng Yu and Pengfei Ma: Quantifying Chinese Happiness via Large-Scale Microblogging Data
The 5W Model for Assessment
Harold Lasswell (1948): Communication is a process of
who says what to whom through which channel with
what effects.
66
WHAT Effects
WHOM Audience
WHICH Channel
WHAT Content
WHO Communi
-cator
Communicator Content Channel Audience Effects