Joint European Conference on Machine Learning and Knowledge Discovery in Databases

ECML PKDD 2015: Machine Learning and Knowledge Discovery in Databases pp 559-575

Discovering Audience Groups and Group-Specific Influencers

  • Shuyang Lin
  • Qingbo Hu
  • Jingyuan Zhang
  • Philip S. Yu
Conference paper

DOI: 10.1007/978-3-319-23525-7_34

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9285)
Cite this paper as:
Lin S., Hu Q., Zhang J., Yu P.S. (2015) Discovering Audience Groups and Group-Specific Influencers. In: Appice A., Rodrigues P., Santos Costa V., Gama J., Jorge A., Soares C. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2015. Lecture Notes in Computer Science, vol 9285. Springer, Cham

Abstract

Recently, user influence in social networks has been studied extensively. Many applications related to social influence depend on quantifying influence and finding the most influential users of a social network. Most existing work studies the global influence of users, i.e. the aggregated influence that a user has on the entire network. It is often overlooked that users may be significantly more influential to some audience groups than others. In this paper, we propose AudClus, a method to detect audience groups and identify group-specific influencers simultaneously. With extensive experiments on real data, we show that AudClus is effective in both the task of detecting audience groups and the task of identifying influencers of audience groups. We further show that AudClus makes possible for insightful observations on the relation between audience groups and influencers. The proposed method leads to various applications in areas such as viral marketing, expert finding, and data visualization.

Keywords

Social influence Influencer detection Audience group 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Shuyang Lin
    • 1
  • Qingbo Hu
    • 1
  • Jingyuan Zhang
    • 1
  • Philip S. Yu
    • 1
    • 2
  1. 1.University of Illinois at ChicagoChicagoUSA
  2. 2.Institute for Data ScienceTsinghua UniversityBeijingChina

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