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Hot Topic Community Discovery on Cross Social Networks

Author

Listed:
  • Xuan Wang

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

  • Bofeng Zhang

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

  • Furong Chang

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
    School of Computer Science and Technology, Kashgar University, Kashgar 844006, China)

Abstract

The rapid development of online social networks has allowed users to obtain information, communicate with each other and express different opinions. Generally, in the same social network, users tend to be influenced by each other and have similar views. However, on another social network, users may have opposite views on the same event. Therefore, research undertaken on a single social network is unable to meet the needs of research on hot topic community discovery. “Cross social network” refers to multiple social networks. The integration of information from multiple social network platforms forms a new unified dataset. In the dataset, information from different platforms for the same event may contain similar or unique topics. This paper proposes a hot topic discovery method on cross social networks. Firstly, text data from different social networks are fused to build a unified model. Then, we obtain latent topic distributions from the unified model using the Labeled Biterm Latent Dirichlet Allocation (LB-LDA) model. Based on the distributions, similar topics are clustered to form several topic communities. Finally, we choose hot topic communities based on their scores. Experiment result on data from three social networks prove that our model is effective and has certain application value.

Suggested Citation

  • Xuan Wang & Bofeng Zhang & Furong Chang, 2019. "Hot Topic Community Discovery on Cross Social Networks," Future Internet, MDPI, vol. 11(3), pages 1-16, March.
    • Handle: RePEc:gam:jftint:v:11:y:2019:i:3:p:60-:d:210719
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    References listed on IDEAS

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    1. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
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    Cited by:

    1. Shen, Han & Tu, Lilan & Guo, Yifei & Chen, Juan, 2022. "The influence of cross-platform and spread sources on emotional information spreading in the 2E-SIR two-layer network," Chaos, Solitons & Fractals, Elsevier, vol. 165(P2).

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