A time-varying propagation model of hot topic on BBS sites and Blog networks

B Zhang, X Guan, MJ Khan, Y Zhou - Information Sciences, 2012 - Elsevier
Information Sciences, 2012Elsevier
Modeling the propagation of hot online topic is a preliminary requirement of predicting the
trend of hot online topic. We propose a time-varying hot topic propagation model in online
discussion context based upon the collective behavior of users who are in different social
subgroups on blog networks and bulletin board system (BBS) sites. By analyzing the stability
of the equilibrium of our model, we search for the threshold to be watershed of the trend of
hot online topic and generalize about two theorems from the results of analysis, they exposit …
Modeling the propagation of hot online topic is a preliminary requirement of predicting the trend of hot online topic. We propose a time-varying hot topic propagation model in online discussion context based upon the collective behavior of users who are in different social subgroups on blog networks and bulletin board system (BBS) sites. By analyzing the stability of the equilibrium of our model, we search for the threshold to be watershed of the trend of hot online topic and generalize about two theorems from the results of analysis, they exposit two sufficient conditions under which the trend of hot online topic will die out or remain uniformly weakly persistent. Furthermore, we propose methods to predict the trend of hot online topic on the strength of our model and theorems. For different motivation, we design two methods: Method (I) is mainly served as a way of theoretical research for predicting long trend of single-peak hot online topic by the thresholds of theorems; and for application, we design method (II) to predict the number of users writing or commenting upon article posts with respect to multi-peak hot online topic and single-peak one in the following two days with the help of Method (I). Experiments of two methods are performed on widely-discussed topics on the Sina Blog and the famous Liang Quan Qi Mei (LQQM) BBS and Xi’an Jiaotong University (BMY) BBS in China. The experimental results show that our methods predict the trend of hot online topic efficiently not only for theoretical motivation but also for applicable motivation, and reduce the computational complexity. Hence, our model can serve as basis for predicting trends in hot online topic propagation.
Elsevier
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