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


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.

Publication DOI: https://doi.org/10.1007/s11280-015-0364-y
Divisions: College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
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Additional Information: The final publication is available at Springer via http://dx.doi.org/10.1007/s11280-015-0364-y Funding: Fundamental Research Funds for the Central Universities / the Research Funds of Renmin University of China (Grant 15XNLQ01); and National Key Basic Research Program (973 Program) of China (Grant No.2014CB340403).
Uncontrolled Keywords: expertise,microblog,social media influence,Computer Networks and Communications,Hardware and Architecture,Software
Publication ISSN: 1573-1413
Last Modified: 19 Apr 2024 07:12
Date Deposited: 03 Sep 2015 08:25
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2016-09
Published Online Date: 2015-08-11
Accepted Date: 2015-07-09
Authors: Zhao, Wayne Xin
Liu, Jing
He, Yulan (ORCID Profile 0000-0003-3948-5845)
Lin, Chin Yew
Wen, Ji-Rong



Version: Accepted Version

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