Brussels, Belgium Belgium
Dec. 10, 2012 to Dec. 10, 2012
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2012.71
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.
Hidden Markov models, Markov processes, Mathematical model, Media, Probability distribution, Data models, Synchronization, reactive factor, collective synchronous behavior, hidden Markov model, social media
Victor C. Liang, Vincent T.Y. Ng, "Modeling of Collective Synchronous Behavior on Social Media", ICDMW, 2012, 2013 IEEE 13th International Conference on Data Mining Workshops, 2013 IEEE 13th International Conference on Data Mining Workshops 2012, pp. 945-952, doi:10.1109/ICDMW.2012.71