A Collective synchronous behavior model 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

Abstract

Collective synchronous behavior is a pervasive phenomenon that we can discover in nature and virtual social media. Traditional data mining methods, however, mainly concentrate on analysis of individual behavior. In sociology, many well-known models are not suitable for the social media environment as well, in which huge amounts of data are generated everyday. In this paper, we proposed an innovative model that consists of multiple hidden Markov chains. By learning from the observations from a group of people, our model can not only predict the steady future state of a collective, but also measure the dependency property, reactive factor, of individuals. Experiment result shows that our model has ability to distinguish the behaviors of different persons.
Original languageEnglish
Title of host publicationDUBMMSM'12 - Proceedings of the 2012 ACM Workshop on Data-Driven User Behavioral Modeling and Mining from Social Media, Co-located with CIKM 2012
Pages3-6
Number of pages4
DOIs
Publication statusPublished - 10 Dec 2012
Event2012 ACM Workshop on Data-Driven User Behavioral Modeling and Mining from Social Media, DUBMMSM 2012, Co-located with 21st ACM Conference on Information and Knowledge Management, CIKM 2012 - Maui, HI, United States
Duration: 29 Oct 201229 Oct 2012

Conference

Conference2012 ACM Workshop on Data-Driven User Behavioral Modeling and Mining from Social Media, DUBMMSM 2012, Co-located with 21st ACM Conference on Information and Knowledge Management, CIKM 2012
Country/TerritoryUnited States
CityMaui, HI
Period29/10/1229/10/12

Keywords

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

ASJC Scopus subject areas

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

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