A computational approach to measuring the correlation between expertise and social media influence for celebrities on microblogs

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• 作者：Wayne Xin Zhao ; Jing Liu ; Yulan He ; Chin-Yew Lin ; Ji-Rong Wen
• 刊名：World Wide Web
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：19
• 期：5
• 页码：865-886
• 全文大小：717 KB
• 刊物类别：Computer Science
• 刊物主题：Information Systems Applications and The Internet
  Database Management
  Operating Systems
  
• 出版者：Springer Netherlands
• ISSN：1573-1413
• 卷排序：19

文摘

Social media influence analysis, sometimes also called authority detection, aims to rank users based on their influence scores in social media. Existing approaches of social influence analysis usually focus on how to develop effective algorithms to quantize users’ influence scores. They rarely consider a person’s expertise levels which are arguably important to influence measures. In this paper, we propose a computational approach to measuring the correlation between expertise and social media influence, and we take a new perspective to understand social media influence by incorporating expertise into influence analysis. We carefully constructed a large dataset of 13,684 Chinese celebrities from Sina Weibo (literally ”Sina microblogging”). We found that there is a strong correlation between expertise levels and social media influence scores. Our analysis gave a good explanation of the phenomenon of “top across-domain influencers”. In addition, different expertise levels showed influence variation patterns: e.g., (1) high-expertise celebrities have stronger influence on the “audience” in their expertise domains; (2) expertise seems to be more important than relevance and participation for social media influence; (3) the audiences of top expertise celebrities are more likely to forward tweets on topics outside the expertise domains from high-expertise celebrities.KeywordsSocial media influenceExpertiseMicroblog