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Opinion leadership on twitter xu ica2013
Opinion leadership on twitter xu ica2013
Opinion leadership on twitter xu ica2013
Opinion leadership on twitter xu ica2013
Opinion leadership on twitter xu ica2013
Opinion leadership on twitter xu ica2013
Opinion leadership on twitter xu ica2013
Opinion leadership on twitter xu ica2013
Opinion leadership on twitter xu ica2013
Opinion leadership on twitter xu ica2013
Opinion leadership on twitter xu ica2013
Opinion leadership on twitter xu ica2013
Opinion leadership on twitter xu ica2013
Opinion leadership on twitter xu ica2013
Opinion leadership on twitter xu ica2013
Opinion leadership on twitter xu ica2013
Opinion leadership on twitter xu ica2013
Opinion leadership on twitter xu ica2013
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Opinion leadership on twitter xu ica2013

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Opinion leadership on twitter xu ica2013

Opinion leadership on twitter xu ica2013

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  • 1. Predicting Opinion Leadership in Twitter Networks The Case of the Wisconsin Recall Election • Weiai Xu, Department of Communication, SUNY-Buffalo weiaixu@buffalo.edu • Yoonmo Sang, Department of Radio-Television-Film, University of Texas – Austin • Stacy Blasiola, Department of Communication, University of Illinois at Chicago • Corresponding author: Dr.Han Woo Park, Associate Professor, the Department of Media & Communication, YeungNam University, South Korea 1
  • 2. Opinion leadership in digital age is the ability to influence online information flow. It means grab others’ attention and persuade others to maximize the attention. On Twitter, opinion leadership means getting your message retweeted. 2
  • 3. Characteristics associated with opinion leaders (see Rogers, 2003): opinion leaders are: 1. well-informed 2.socially connected 3. involved in specific issues or topics 4. highly regarded by others, etc. Are these characteristics still relevant in digital age? Rogers, E. M. (2003). Diffusion of innovations (5th ed. ed.). New York: Free Press. 3
  • 4. In this project, we test how opinion leadership in Twitter relates to 1. social connectivity 2. involvement 3. user identity 4
  • 5. What does connectivity means? • Twitter forms information flow networks • Graph: Twitter hashtag #wirecall A higher betweenness centrality means a higher level of connectivity 5
  • 6. What does involvement means? • General involvement: Twitter users’ political involvement • Situational involvement: issue involvement, whether a user is interested in or personally affected by the WI recall election 6
  • 7. Three ways to infer whether a user is involved: • user’s geographic location (in WI or not) • Self-disclosure of political identity on Twitter profile • The content of tweets 7
  • 8. Location info on Twitter profile Self-disclosure of political identity on Twitter profile 8
  • 9. Tweets indicating a hierarchy of involvement Engaging tweets action Explicitly ask other users to engage in certain acts community information Providing original feedback Simply passing along others’ messages Higher proportion of engaging tweets = more involved 9
  • 10. What does identity means? • Individual user or organizational users (e.g. news media, advocacy groups, government agencies, etc.) • News media or non-news-media 10
  • 11. Data Profile • Data-mining: 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 11
  • 12. Findings • connectivity positively predicts the ability in getting retweeted by other users. 12
  • 13. • Involvement indicated by geographic location positively predicts the ability in getting retweeted. 13
  • 14. • Involvement indicated by self-disclosure of political identity: no significant results. 14
  • 15. • Organizational users more likely to get retweeted. • But no significant differences in the ability to get retweets between Twitter users of traditional news media and nonmedia. 15
  • 16. The takeaway • Characteristics associated with traditional opinion leaderships are still relevant in Twitter communication • the flexibility of inferring online users’ characteristics (e.g. involvement and social connectivity) using actual behavioral data instead of self-reported data • Methodologically, combing network analysis with content analysis in studying online behavior 16
  • 17. Future directions • 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 17
  • 18. Thank You! • Questions to weiaixu@buffalo.edu 18

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