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Predicting Opinion Leaders in Twitter Activism Networks: 
The Case of the Wisconsin Recall Election 
Weiai Wayne Xu (Univ....
1. Networked opinion leadership 
• Wisconsin recall election 
• #wirecall 
• User-to-user follows relationship 
• On Twitt...
2. Our goal 
3 
• _____? 
• _____? 
• _____? 
• What user characteristics and 
behaviors predict opinion 
leadership on Tw...
3. Classic opinion leadership model (Rogers, 2003) 
4 
• Social connectivity 
• Involvement 
• Knowledge 
• Status 
• Etc....
4. Linking the opinion leadership model to Twitter 
5 
• Social connectivity 
• Twitter forms information flow networks 
t...
4. Linking the opinion leadership model to Twitter 
6 
• Involvement, knowledge, status?
4. Linking the opinion leadership model to Twitter 
7 
• Involvement, knowledge, status? 
action 
community 
Explicitly as...
5. Key hypotheses 
8 
• Hypothesis 1: Users’ centrality in Twitter networks is related to 
influence on the diffusion of p...
6. Data collection 
9 
• Most recent 1500 tweets every two hours, from 5-29-2012 to 6- 
5-2012. 
• 1000 users randomly sam...
7. Results 
10 
The results provided general support for the hypotheses: 
• The model explained 26% of the variance, F(6,5...
8. Takeaway 
11 
Opinion leadership in social media is contingent upon both 
network and context factors. 
• Characteristi...
9. Future directions 
12 
• Combining behavior data and perception data (content 
analysis + network analysis + survey) 
•...
Contact 
• Weiai Wayne Xu: weiaixu@buffalo.edu http://curiositybits.com/ 
• Yoonmo Sang: yoonmosang@gmail.com http://rtf.u...
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Predicting opinion leadership on twitter

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Predicting opinion leadership on twitter

  1. 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) 1
  2. 2. 1. Networked opinion leadership • Wisconsin recall election • #wirecall • User-to-user follows relationship • On Twitter, opinion leadership means getting your message retweeted. 2
  3. 3. 2. Our goal 3 • _____? • _____? • _____? • What user characteristics and behaviors predict opinion leadership on Twitter?
  4. 4. 3. Classic opinion leadership model (Rogers, 2003) 4 • 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. 5. 4. Linking the opinion leadership model to Twitter 5 • 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. 6. 4. Linking the opinion leadership model to Twitter 6 • Involvement, knowledge, status?
  7. 7. 4. Linking the opinion leadership model to Twitter 7 • 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. 8. 5. Key hypotheses 8 • 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. 9. 6. Data collection 9 • 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. 10. 7. Results 10 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. 11. 8. Takeaway 11 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. 12. 9. Future directions 12 • 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. 13. Contact • Weiai Wayne Xu: weiaixu@buffalo.edu http://curiositybits.com/ • Yoonmo Sang: yoonmosang@gmail.com http://rtf.utexas.edu/graduate/phd-year- 13 http://abs.sagepub.com/content/58/10/1278 4 • Stacy Blasiola: sblasi2@uic.edu http://blasiola.wordpress.com/ • Dr. Han Woo Park: http://www.hanpark.net/

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