Predicting Opinion Leaders in Twitter Activism Networks: The Case of the Wisconsin Recall Election

Weiai Xu, Yoonmo SANG, Stacy Blasiola, Hanwoo Park

Research output: Contribution to journalArticle

65 Citations (Scopus)

Abstract

This study employs content and network analysis techniques to explore the predictors of opinion leadership in a political activism network on Twitter. The results demonstrate the feasibility of using user-generated content to measure user characteristics. The characteristics were analyzed to predict users’ performance in the network. According to the results, Twitter users with higher connectivity and issue involvement are better at influencing information flow on Twitter. User connectivity was measured by betweenness centrality, and issue involvement was measured by a user’s geographic proximity to a given event and the contribution of engaging tweets. In addition, the results show that tweets by organizations had greater influence than those by individual users.
Original languageEnglish
Pages (from-to)1278-1293
Number of pages16
JournalAmerican Behavioral Scientist
Volume58
Issue number10
DOIs
Publication statusPublished - 2014
Externally publishedYes

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opinion leader
twitter
election
study contents
information flow
network analysis
content analysis
leadership
event

Cite this

Xu, Weiai ; SANG, Yoonmo ; Blasiola, Stacy ; Park , Hanwoo . / Predicting Opinion Leaders in Twitter Activism Networks: The Case of the Wisconsin Recall Election. In: American Behavioral Scientist. 2014 ; Vol. 58, No. 10. pp. 1278-1293.
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Predicting Opinion Leaders in Twitter Activism Networks: The Case of the Wisconsin Recall Election. / Xu, Weiai ; SANG, Yoonmo; Blasiola, Stacy; Park , Hanwoo .

In: American Behavioral Scientist, Vol. 58, No. 10, 2014, p. 1278-1293.

Research output: Contribution to journalArticle

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