Detect rumors using time series of social context information on microblogging websites

Jing Ma, Wei Gao, Zhongyu Wei, Yueming Lu, Kam Fai Wong

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

120 Citations (Scopus)

Abstract

Automatically identifying rumors from online social media especially microblogging websites is an important research issue. Most of existing work for rumor detection focuses on modeling features related to microblog contents, users and propagation patterns, but ignore the importance of the variation of these social context features during the message propagation over time. In this study, we propose a novel approach to capture the temporal characteristics of these features based on the time series of rumor's lifecycle, for which time series modeling technique is applied to incorporate various social context information. Our experiments using the events in two microblog datasets confirm that the method outperforms state-of-the-art rumor detection approaches by large margins. Moreover, our model demonstrates strong performance on detecting rumors at early stage after their initial broadcast.

Original languageEnglish
Title of host publicationInternational Conference on Information and Knowledge Management, Proceedings
PublisherAssociation for Computing Machinery
Pages1751-1754
Number of pages4
Volume19-23-Oct-2015
ISBN (Print)9781450337946
DOIs
Publication statusPublished - 17 Oct 2015
Event24th ACM International Conference on Information and Knowledge Management, CIKM 2015 - Melbourne, Australia
Duration: 19 Oct 201523 Oct 2015

Other

Other24th ACM International Conference on Information and Knowledge Management, CIKM 2015
CountryAustralia
CityMelbourne
Period19/10/1523/10/15

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Keywords

  • Rumor detection
  • Social context
  • Temporal
  • Time series

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

Cite this

Ma, J., Gao, W., Wei, Z., Lu, Y., & Wong, K. F. (2015). Detect rumors using time series of social context information on microblogging websites. In International Conference on Information and Knowledge Management, Proceedings (Vol. 19-23-Oct-2015, pp. 1751-1754). Association for Computing Machinery. https://doi.org/10.1145/2806416.2806607