Predicting Opinion Leaders in Twitter Activism Networks

The Case of the Wisconsin Recall Election

Datos Bibliográficos

ID5118330
AutoresWeiai Wayne Xu (0000-0002-7369-3886, State University of New York, Buffalo, NY, USA), Yoonmo Sang (0000-0003-3439-0417, University of Texas, Austin, TX, USA), Stacy Blasiola (University of Illinois at Chicago, Chicago, IL, USA), Han Woo Park (0000-0002-1378-2473, Yeungnam University, Gyeongsan, Korea)
Año2014
Volumen58
Número10
Páginas1278-1293
Fecha de publicación2014-09-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaAmerican Behavioral Scientist (JOURNAL)
Identificadores de la revistaISSN: 0002-7642 • E-ISSN: 1552-3381
EditorialSAGE Publications Inc (PUBLISHER)
DOI10.1177/0002764214527091
OpenAlexW2314983174
IdiomaEN
Citas recibidas51
Referencias citadas40

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

Betweenness centrality · Centrality · Data mining · Data science · Event (particle physics) · Internet privacy · Measure (data warehouse) · Opinion leadership · Political science · Politics · Public relations · Social media · Social network analysis · User-generated content · World Wide Web · Computer Science · Opinion Dynamics and Social Influence · Social Capital and Networks · Social Media and Politics