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Community-based topic modeling for social tagging
Conference proceeding

Community-based topic modeling for social tagging

Daifeng Li, Bing He, Ying Ding, Jie Tang, Cassidy Sugimoto, Zheng Qin, Erjia Yan, Juanzi Li and Tianxi Dong
Proceedings of the 19th ACM international conference on information and knowledge management, pp 1565-1568
26 Oct 2010
DOI: https://doi.org/10.1145/1871437.1871673

Abstract

topic mining social tagging system community detection ttr-lda-community
Exploring community is fundamental for uncovering the connections between structure and function of complex networks and for practical applications in many disciplines such as biology and sociology. In this paper, we propose a TTR-LDA-Community model which combines the Latent Dirichlet Allocation model (LDA) and the Girvan-Newman community detection algorithm with an inference mechanism. The model is then applied to data from Delicious, a popular social tagging system, over the time period of 2005-2008. Our results show that 1) users in the same community tend to be interested in similar set of topics in all time periods; and 2) topics may divide into several sub-topics and scatter into different communities over time. We evaluate the effectiveness of our model and show that the TTR-LDA-Community model is meaningful for understanding communities and outperforms TTR-LDA and LDA models in tag prediction.

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50 citations in Scopus

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