This document summarizes a study that analyzed Twitter data from the 2012 Wisconsin gubernatorial recall election to predict which users would be influential opinion leaders. The study found that users who were more central in the retweet network, more locally involved in the election issue, and contributed more engaging tweets, were more likely to have their tweets retweeted by others, supporting the hypotheses. Characteristics like centrality and involvement predicted influence, as in traditional models, showing their continued relevance for social media opinion leadership.

1. Predicting Opinion Leaders in Twitter Activism Networks:
The Case of the Wisconsin Recall Election
Weiai Wayne Xu (Univ. at Buffalo)
Yoonmo Sang (Univ. of Texas-Austin)
Stacy Blasiola (Univ. of Illinois at Chicago)
Dr. Han Woo Park (YeungNam University, S. Korea)
Presentation for #SMSociety2014 (September 27-28, Toronto)
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2. 1. Networked opinion leadership
• Wisconsin recall election
• #wirecall
• User-to-user follows relationship
• On Twitter, opinion leadership
means getting your message
retweeted.
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3. 2. Our goal
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• _____?
• _____?
• _____?
• What user characteristics and
behaviors predict opinion
leadership on Twitter?

4. 3. Classic opinion leadership model (Rogers, 2003)
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• Social connectivity
• Involvement
• Knowledge
• Status
• Etc.
Are these attributes still relevant in digital age?
Rogers, E. M. (2003). Diffusion of innovations (5th ed. ed.). New York: Free Press.

5. 4. Linking the opinion leadership model to Twitter
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• Social connectivity
• Twitter forms information flow networks
through follows, retweet and mention.
• A higher betweenness centrality is
indicative of a higher level of
connectivity

6. 4. Linking the opinion leadership model to Twitter
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• Involvement, knowledge, status?

7. 4. Linking the opinion leadership model to Twitter
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• Involvement, knowledge, status?
action
community
Explicitly ask other users to engage in certain acts
information
Providing original feedback, interactive
Non-directed, one-to-many, simply passing along
others’ messages
Engaging
tweets

8. 5. Key hypotheses
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• Hypothesis 1: Users’ centrality in Twitter networks is related to
influence on the diffusion of political information such that the higher
the centrality, the more likely users’ messages are retweeted by other
users.
• Hypothesis 2: The more politically involved the users are, based on the
In short, level we of self-hypothesize disclosure that of personal more connected political and information, involved the users more are likely
more
successful users’ in messages influencing are information retweeted by flow other within users.
Twitter networks.
• Hypothesis 3: The more involved the users are in a given political issue,
based on their geographic proximity to the political event, the more
likely their messages are retweeted by other users.
• Hypothesis 4: The more involved the users are in a political issue,
based on their contribution of engaging tweets, the more likely their
messages are retweeted by other users.

9. 6. Data collection
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• Most recent 1500 tweets every two hours, from 5-29-2012 to 6-
5-2012.
• 1000 users randomly sampled from 8957 Twitter users that
tweeted #wirecall during the timeframe
• The sampled users sent 3546 tweets containing the hashtag
#wirecall

10. 7. Results
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The results provided general support for the hypotheses:
• The model explained 26% of the variance, F(6,593) = 8.22, p< .001.
• Betweenness centrality was positively related to the number of RTs
(β = .26).
• local users were more likely to be retweeted (β = .20).
• issue involvement based on engaging tweets (β = .21) positively
predicted the number of RTs.
• political involvement DOES NOT predict RT.

11. 8. Takeaway
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Opinion leadership in social media is contingent upon both
network and context factors.
• Characteristics associated with traditional opinion
leaderships are still relevant in Twitter communication.
• Integrating network analysis and content analysis

12. 9. Future directions
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• Combining behavior data and perception data (content
analysis + network analysis + survey)
• Connectivity in various types of networks (issue network
vs. general Twitter network)
• Non-issue specific
• Longitudinal analysis

13. Contact
• Weiai Wayne Xu: weiaixu@buffalo.edu http://curiositybits.com/
• Yoonmo Sang: yoonmosang@gmail.com http://rtf.utexas.edu/graduate/phd-year-
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http://abs.sagepub.com/content/58/10/1278
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• Stacy Blasiola: sblasi2@uic.edu http://blasiola.wordpress.com/
• Dr. Han Woo Park: http://www.hanpark.net/