Predicting the social influence of upcoming contents in large social networks

Authors:
Yi Han;Lei Deng;Binying Xu;Lumin Zhang;Bin Zhou;Yan Jia
Affiliations:
Peking University, China and National University of Defense Technology, China;National University of Defense Technology, China;National University of Defense Technology, China;National University of Defense Technology, China;National University of Defense Technology, China;National University of Defense Technology, China
Venue:
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Year:
2013

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Abstract

Online social networks, such as twitter and facebook, are continuously generating the new contents and relationships. To fully understand the spread of topics, there are some essential but remaining open questions. Why do some seemingly ordinary topics actually received widespread attention? Is it due to the attractiveness of the content itself, or social network structure plays a larger role in the dissemination of information? Can we predict the trend of information dissemination? Analyzing and predicting the influence and spread of up-coming contents is an interesting and useful research direction, and has brilliant perspective on web advertising and spam detection. For solving the problems, in this paper, a novel time series model has been proposed. In this model, the existing user-generated contents are summarized with a set of valued sequences. An early predictor is adopted for analyzing the topical/structural properties of series, and the influence of newly coming contents are estimated with the predictor. The empirical study conducted on large real data sets indicates that our model is interesting and meaningful, and our methods are effective and efficient in practice.