Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Information diffusion through blogspace
Proceedings of the 13th international conference on World Wide Web
The predictive power of online chatter
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Information flow modeling based on diffusion rate for prediction and ranking
Proceedings of the 16th international conference on World Wide Web
Proceedings of the hypertext 2008 workshop on Collaboration and collective intelligence
Meme-tracking and the dynamics of the news cycle
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Taking Social Networks to the Next Level
International Journal of Distributed Systems and Technologies
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The groups discussing incidental popular topics over online social networks have recently become a research focus. By analyzing some fundamental relationships in these groups, we find that the traditional models are not suitable for describing the group dynamics of incidental topics. In this article we analyze the dynamics of online groups discussing incidental popular topics and present a new model for predicting the dynamic sizes of incidental topic groups. We discover that the dynamic sizes of incidental topic groups follow a heavy-tailed distribution. Based on this finding, we develop an adaptive parametric method for predicting the dynamics of this type of group. The model presented in the article is validated using actual data from LiveJournal and Sohu blogs. The empirical results show that the model can effectively predict the dynamic characteristics of incidental topic groups over both short and long timescales, and outperforms the SIR model. We conclude by offering two strategies for promoting the group popularity of incidental topics.