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Multi-Topic Tracking Model for dynamic social network

Author

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  • Li, Yuhua
  • Liu, Changzheng
  • Zhao, Ming
  • Li, Ruixuan
  • Xiao, Hailing
  • Wang, Kai
  • Zhang, Jun

Abstract

The topic tracking problem has attracted much attention in the last decades. However, existing approaches rarely consider network structures and textual topics together. In this paper, we propose a novel statistical model based on dynamic bayesian network, namely Multi-Topic Tracking Model for Dynamic Social Network (MTTD). It takes influence phenomenon, selection phenomenon, document generative process and the evolution of textual topics into account. Specifically, in our MTTD model, Gibbs Random Field is defined to model the influence of historical status of users in the network and the interdependency between them in order to consider the influence phenomenon. To address the selection phenomenon, a stochastic block model is used to model the link generation process based on the users’ interests to topics. Probabilistic Latent Semantic Analysis (PLSA) is used to describe the document generative process according to the users’ interests. Finally, the dependence on the historical topic status is also considered to ensure the continuity of the topic itself in topic evolution model. Expectation Maximization (EM) algorithm is utilized to estimate parameters in the proposed MTTD model. Empirical experiments on real datasets show that the MTTD model performs better than Popular Event Tracking (PET) and Dynamic Topic Model (DTM) in generalization performance, topic interpretability performance, topic content evolution and topic popularity evolution performance.

Suggested Citation

  • Li, Yuhua & Liu, Changzheng & Zhao, Ming & Li, Ruixuan & Xiao, Hailing & Wang, Kai & Zhang, Jun, 2016. "Multi-Topic Tracking Model for dynamic social network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 454(C), pages 51-65.
    • Handle: RePEc:eee:phsmap:v:454:y:2016:i:c:p:51-65
    DOI: 10.1016/j.physa.2016.02.038
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    References listed on IDEAS

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    1. Huang, Shihang & Liu, Ying & Dang, Depeng, 2014. "Burst topic discovery and trend tracing based on Storm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 331-339.
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    Cited by:

    1. Tawfik, M. & Tonnellier, X. & Sansom, C., 2018. "Light source selection for a solar simulator for thermal applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 802-813.
    2. Ma, Tinghuai & Li, Jing & Liang, Xinnian & Tian, Yuan & Al-Dhelaan, Abdullah & Al-Dhelaan, Mohammed, 2019. "A time-series based aggregation scheme for topic detection in Weibo short texts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).

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