Abstract
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. In addition, different expertise levels showed influence variation patterns: high-expertise celebrities have stronger influence on the 'audience' in their expertise domains.
Original language | English |
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Title of host publication | Proceedings of the 2014 IEEE/ACM international conference on Advances in Social Networks Analysis and Mining |
Subtitle of host publication | ASONAM 2014 |
Editors | Xindong Wu, Martin Ester, Guandong Xu |
Place of Publication | Piscataway, NJ (US) |
Publisher | IEEE |
Pages | 460-463 |
Number of pages | 4 |
ISBN (Print) | 978-1-4799-5877-1, 978-1-4799-5876-4 |
DOIs | |
Publication status | Published - 31 Dec 2014 |
Event | 2014 IEEE/ACM international conference on Advances in Social Networks Analysis and Mining - Beijing, China Duration: 17 Aug 2014 → 20 Aug 2014 |
Conference
Conference | 2014 IEEE/ACM international conference on Advances in Social Networks Analysis and Mining |
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Abbreviated title | ASONAM 2014 |
Country/Territory | China |
City | Beijing |
Period | 17/08/14 → 20/08/14 |
Bibliographical note
Funding: EPSRC grant EP/L010690/1Keywords
- expertise
- microblog
- social media influence