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Identification of Influential Users Based on Topic-Behavior Influence Tree in Social Networks

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Natural Language Processing and Chinese Computing (NLPCC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10619))

Abstract

Identifying influential users in social networks is of significant interest, as it can help improve the propagation of ideas or innovations. Various factors can affect the relationships and the formulation of influence between users. Although many studies have researched this domain, the effect of the correlation between messages and behaviors in measuring users’ influence in social networks has not been adequately focused on. As a result, influential users can not be accurately evaluated. Thus, we propose a topic-behavior influence tree algorithm that identifies influential users using six types of relationships in the following factors: message content, hashtag titles, retweets, replies, and mentions. By maximizing the number of affected users and minimizing the propagation path, we can improve the accuracy of identifying influential users. The experimental results compared with state-of-the-art algorithms on various datasets and visualization on TUAW dataset validate the effectiveness of the proposed algorithm.

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Notes

  1. 1.

    \(dist\left( i, j\right) \) is the Cosine Similarity between vector i and vector j.

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Acknowledgments

This work is supported by National Science and Technology Major Project under Grant No. 2017YFB0803003, The National Key Research and Development Program of China (grant No. 2016YFB0801003), Natural Science Foundation of China (No. 61702508).

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Authors and Affiliations

  1. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100093, China

    Jianjun Wu, Ying Sha, Rui Li, Qi Liang, Bo Jiang, Jianlong Tan & Bin Wang

  2. School of Cyber Security, University of Chinese Academy of Sciences, Beijing, 100029, China

    Jianjun Wu, Ying Sha, Rui Li, Qi Liang, Bo Jiang, Jianlong Tan & Bin Wang

Authors
  1. Jianjun Wu
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  2. Ying Sha
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  3. Rui Li
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  4. Qi Liang
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  5. Bo Jiang
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  6. Jianlong Tan
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  7. Bin Wang
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Corresponding author

Correspondence to Ying Sha .

Editor information

Editors and Affiliations

  1. Fudan University, Shanghai, China

    Xuanjing Huang

  2. Singapore Management University, Singapore, Singapore

    Jing Jiang

  3. Peking University, Beijing, China

    Dongyan Zhao

  4. Peking University, Beijing, China

    Yansong Feng

  5. Soochow University, Suzhou, China

    Yu Hong

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Cite this paper

Wu, J. et al. (2018). Identification of Influential Users Based on Topic-Behavior Influence Tree in Social Networks. In: Huang, X., Jiang, J., Zhao, D., Feng, Y., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2017. Lecture Notes in Computer Science(), vol 10619. Springer, Cham. https://doi.org/10.1007/978-3-319-73618-1_40

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  • DOI: https://doi.org/10.1007/978-3-319-73618-1_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73617-4

  • Online ISBN: 978-3-319-73618-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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