Predicting the tendency of topic discussion on the online social networks using a dynamic probability model

Authors:
Yadong Zhou;Xiaohong Guan;Zhefei Zhang;Beibei Zhang
Affiliations:
Xi'an Jiaotong University, Xi'an, China;Xi'an Jiaotong University/ CFINS and Tsinghua University, Xi'an, China;Xi'an Jiaotong University, Xi'an, China;Xi'an Jiaotong University, Xi'an, China
Venue:
Proceedings of the hypertext 2008 workshop on Collaboration and collective intelligence
Year:
2008

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Abstract

Topic discussion is a significant phenomenon on the online social network, which would attract the rising attention in the near future. In this paper, we predict the tendency of topic discussion on the online social networks using a dynamic probability model. We analyze the process of topic discussion, and give the formulation of it. Three main factors (individual interest, group behavior, and time lapse) are analyzed and quantized, based on which, we propose a dynamic probability model to predict the user's behavior, i.e. attending the topic discussion or not, and then obtain the number of the attending users. Most of the parameters of the model can be calculated by ML estimate methods, and the rest 3 parameters are set by human experience. Experiment shows that our model could predict the tendency of topic discussion accurately. Also, we simulate different sets of the three experience parameters and study the selection of suitable experience parameters.