World Wide Web

, Volume 19, Issue 5, pp 865–886 | Cite as

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

  • Wayne Xin Zhao
  • Jing Liu
  • Yulan He
  • Chin-Yew Lin
  • Ji-Rong Wen
Article
  • 1.4k Downloads

Abstract

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.

Keywords

Social media influence Expertise Microblog 

Notes

Acknowledgments

Xin Zhao and Ji-Rong Wen were partially supported by the Fundamental Research Funds for the Central Universities / the Research Funds of Renmin University of China under Grant 15XNLQ01, and the National Key Basic Research Program (973 Program) of China under Grant No. 2014CB340403. This work was partially done when Xin Zhao visited Microsoft Research.

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Wayne Xin Zhao
    • 1
  • Jing Liu
    • 2
  • Yulan He
    • 3
  • Chin-Yew Lin
    • 2
  • Ji-Rong Wen
    • 1
  1. 1.School of InformationRenmin University of ChinaBeijingPeople’s Republic of China
  2. 2.Microsoft ResearchBeijingPeople’s Republic of China
  3. 3.School of Engineering and Applied ScienceAston UniversityBirminghamUK

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