Modeling of collective synchronous behavior on social media

Victor C. Liang, Vincent To Yee Ng

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


Collective synchronous behavior is a pervasive phenomenon that has attracted many researchers' interests over past decades. It can be observed in many areas easily, including biology, chemistry, physics and social society. A series of interactive processes in-between individuals trigger the formation of collective behavior. Traditional data mining methods, however, mainly concentrate on the analysis of individual behavior but ignore the potential associations. Similarly, in sociology, many well-known models based on survey sampling are not suitable for the new emerging social media platform any more, where huge amounts of data are generated by users every day. It is necessary for researchers to develop effective approaches for sampling and modeling the collective behavior on social media. In this paper, we propose an innovative model that consists of multiple hidden Markov chains. By learning a group of time-series behavior data, our model can not only predict the synchronous state of a collective, but also measure the dependency property, namely reactive factor, of each individual. Preliminary experimental result shows that CoSync model has the power to distinguish behavior patterns of different persons.
Original languageEnglish
Title of host publicationProceedings - 12th IEEE International Conference on Data Mining Workshops, ICDMW 2012
Number of pages8
Publication statusPublished - 1 Dec 2012
Event12th IEEE International Conference on Data Mining Workshops, ICDMW 2012 - Brussels, Belgium
Duration: 10 Dec 201210 Dec 2012


Conference12th IEEE International Conference on Data Mining Workshops, ICDMW 2012


  • Collective synchronous behavior
  • Hidden Markov model
  • Reactive factor
  • Social media

ASJC Scopus subject areas

  • Software

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