TTRank: User Influence Rank Based on Tendency Transformation

Duan Songqing; Wu Bin; Wang Bai

doi:10.7544/issn1000-1239.2014.20131570

Journal of Computer Research and Development > 2014 > 51(10): 2225-2238. > DOI: 10.7544/issn1000-1239.2014.20131570 CSTR: 32373.14.issn1000-1239.2014.20131570

Duan Songqing, Wu Bin, Wang Bai. TTRank: User Influence Rank Based on Tendency Transformation[J]. Journal of Computer Research and Development, 2014, 51(10): 2225-2238. DOI: 10.7544/issn1000-1239.2014.20131570

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TTRank: User Influence Rank Based on Tendency Transformation

(School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876)

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Published Date: September 30, 2014

Graphical Abstract

Abstract

Abstract

In recent years, many scholars have studied the problem of how to analyze user influence from the perspective of reply relationships. But there are various problems, such as reply relations are scarce, the content of posts are ignored, don’t support dynamic updates and so on. To compensate for these deficiencies, a user influence analysis method based on tendency transformation is proposed. Firstly, the post influence is calculated, the concept of “local reply chain” (LRC) is proposed, and the calculation method of reply indirect relationships is introduced, so the number of reply relationship is drastically increased. Secondly, the process of user tendency transformation is analyzed based on LRC, and the degrees of user impacting on others and being affected by others are gotten. Finally, the user influence ranking within the specified range is obtained. Compared with 10 kinds of classical influence analysis algorithms and used to analyze realistic data, the algorithm can characterize the user image better from the other point of view.
- social media,
- forum,
- influence analysis,
- user influence,
- tendency transformation

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