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Modeling Indirect Influence on Twitter

Author

Listed:
  • Xin Shuai

    (Department of Infomatics, School of Informatics and Computing, Indiana University Bloomington, Bloomington, IN, USA)

  • Ying Ding

    (School of Library and Information Science, Indiana University Bloomington, Bloomington, IN, USA)

  • Jerome Busemeyer

    (Department of Psychological and Brain Sciences, Decision Research Laboratory, Indiana University Bloomington, Bloomington, IN, USA)

  • Shanshan Chen

    (School of Library and Information Science, Indiana University Bloomington, Bloomington, IN, USA)

  • Yuyin Sun

    (School of Library and Information Science, Indiana University Bloomington, Bloomington, IN, USA)

  • Jie Tang

    (Department of Psychology and Brain Science, Tsinghua University, Beijing, China)

Abstract

Social influence in social networks has been extensively researched. Most studies have focused on direct influence, while another interesting question can be raised as whether indirect influence exists between two users who’re not directly connected in the network and what affects such influence. In addition, the theory of complex contagion tells us that more spreaders will enhance the indirect influence between two users. The authors’ observation of intensity of indirect influence, propagated by n parallel spreaders and quantified by retweeting probability in two Twitter social networks, shows that complex contagion is validated globally but is violated locally. In other words, the retweeting probability increases non-monotonically with some local drops. A quantum cognition based probabilistic model is proposed to account for these local drops.

Suggested Citation

  • Xin Shuai & Ying Ding & Jerome Busemeyer & Shanshan Chen & Yuyin Sun & Jie Tang, 2012. "Modeling Indirect Influence on Twitter," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 8(4), pages 20-36, October.
    • Handle: RePEc:igg:jswis0:v:8:y:2012:i:4:p:20-36
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