Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/25464
Title: Modeling of collective synchronous behavior on social media
Authors: Liang, VC
Ng, VTY 
Keywords: Collective synchronous behavior
Hidden Markov model
Reactive factor
Social media
Issue Date: 2012
Publisher: IEEE
Source: 2012 IEEE 12th International Conference on Data Mining Workshops (ICDMW), 10-10 December 2012, Brussels, p. 945-952 How to cite?
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.
URI: http://hdl.handle.net/10397/25464
ISBN: 978-1-4673-5164-5
DOI: 10.1109/ICDMW.2012.71
Appears in Collections:Conference Paper

Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

27
Last Week
2
Last month
Checked on Oct 15, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.