A partition and interaction combined model for social event popularity prediction

Guandan Chen, Qingchao Kong, Wenji Mao, Dajun Zeng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Social media platforms make the spread of social event information quicker and more convenient. Some of these social events may become hot topics, which highlights the importance of event popularity prediction in public management, decision making and other security related applications. Due to the complexity of social event itself, it has two unique characteristics which most previous popularity prediction work has ignored: (1) the discussion of an event itself may consist of several components, e.g. different sub-events, different stances or different user communities; (2) the popularity of an event can be influenced by other related events. To address its unique characteristics, we propose an event popularity prediction model combining partition and interaction. We employ reinforcement learning to automatically partition an event into components and recognize related events. Then we predict event popularity by modeling component information and interactions between related events. Experimental results on a real world dataset show that our proposed model can outperform the competitive baseline methods.

Original languageEnglish (US)
Title of host publication2018 IEEE International Conference on Intelligence and Security Informatics, ISI 2018
EditorsDongwon Lee, Ghita Mezzour, Ponnurangam Kumaraguru, Nitesh Saxena
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages232-237
Number of pages6
ISBN (Electronic)9781538678480
DOIs
StatePublished - Dec 24 2018
Externally publishedYes
Event16th IEEE International Conference on Intelligence and Security Informatics, ISI 2018 - Miami, United States
Duration: Nov 9 2018Nov 11 2018

Other

Other16th IEEE International Conference on Intelligence and Security Informatics, ISI 2018
CountryUnited States
CityMiami
Period11/9/1811/11/18

Keywords

  • Information cascade
  • Popularity prediction
  • Reinforcement learning

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Communication

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  • Cite this

    Chen, G., Kong, Q., Mao, W., & Zeng, D. (2018). A partition and interaction combined model for social event popularity prediction. In D. Lee, G. Mezzour, P. Kumaraguru, & N. Saxena (Eds.), 2018 IEEE International Conference on Intelligence and Security Informatics, ISI 2018 (pp. 232-237). [8587366] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISI.2018.8587366