Paper Info
Title
Modeling of Collective Synchronous Behavior on Social Media
Abstract
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.
Year
DOI
Venue
2012
10.1109/ICDMW.2012.71
Data Mining Workshops
Keywords
Field
DocType
individual behavior,time-series behavior data,collective behavior,cosync model,social media,behavior pattern,social media platform,social society,innovative model,collective synchronous behavior,hidden markov models,data mining
Data science,Data mining,Data modeling,Markov process,Computer science,Survey sampling,Artificial intelligence,Phenomenon,Collective behavior,Synchronization,Social media,Hidden Markov model,Machine learning
Conference
ISSN
ISBN
Citations 
2375-9232
978-1-4673-5164-5
0
PageRank 
References 
Authors
0.34
1
2
Name
Order
Citations
PageRank
Victor C. Liang101.01
Vincent T. Y. Ng2504122.85