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

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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.

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Notes

  1. http://klout.com

  2. http://weibo.com/

  3. http://cn.bing.com/yingxiangli/

  4. Sina Weibo has verified most of these celebrities, thus we can simply match their actual names with account names.

  5. We have tried other numbers, but found 100 was the optimal choice for our dataset in terms of topic interpretation.

  6. The system link is http://playbigdata.com/batmanfly/weiborank.

  7. http://en.wikipedia.org/wiki/Expert

  8. Although we cannot obtain the entire set of retweets of a tweet, Sina API provides the exact number of being retweetd for each tweet. Thus, the statistics of retweet numbers are accurate and not based on incomplete propagator sets in Table 6.

  9. http://en.wikipedia.org/wiki/Spearman%27s%5frank%5fcorrelation%5fcoefficient

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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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Authors and Affiliations

  1. School of Information, Renmin University of China, Beijing, People’s Republic of China

    Wayne Xin Zhao & Ji-Rong Wen

  2. Microsoft Research, Beijing, People’s Republic of China

    Jing Liu & Chin-Yew Lin

  3. School of Engineering and Applied Science, Aston University, Birmingham, UK

    Yulan He

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  1. Wayne Xin Zhao
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  2. Jing Liu
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  3. Yulan He
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  4. Chin-Yew Lin
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Correspondence to Wayne Xin Zhao.

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Zhao, W.X., Liu, J., He, Y. et al. A computational approach to measuring the correlation between expertise and social media influence for celebrities on microblogs. World Wide Web 19, 865–886 (2016). https://doi.org/10.1007/s11280-015-0364-y

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