Abstract
Large social networks (e.g. Twitter, Digg and LinkedIn), have successfully facilitated information diffusion related to various topics. Typically, each topic discussed in these networks is associated with a group of members who have generated content on it and these users form a topic community. Tracking topic community is of much importance to predict the trend of hot spots and public opinion. In this paper, we formally define the problem of topic community tracking as a two-step task, including topic interest modeling and topic evolution mining. We proposed a topic community tracking model to model user’s interest on a topic which is based on random walk algorithm and combines user’s personal affinity and social influence. And then, considering that user’s interest on a topic will vary with time when the discussion content changes and new topic community member joins in, we explore an Adaptive Topic Community Tracking Model. Comprehensive experimental studies on Digg and Sina Weibo corpus show that our approach outperforms existing ones and well matches the practice.
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Liang, Z., Jia, Y., Zhou, B. (2012). Adaptive Topic Community Tracking in Social Network. In: Sheng, Q.Z., Wang, G., Jensen, C.S., Xu, G. (eds) Web Technologies and Applications. APWeb 2012. Lecture Notes in Computer Science, vol 7235. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29253-8_41
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DOI: https://doi.org/10.1007/978-3-642-29253-8_41
Publisher Name: Springer, Berlin, Heidelberg
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