Simultaneously modeling semantics and structure of threaded discussions: a sparse coding approach and its applications

ABSTRACT

The huge amount of knowledge in web communities has motivated the research interests in threaded discussions. The dynamic nature of threaded discussions poses lots of challenging problems for computer scientists. Although techniques such as semantic models and structural models have been shown to be useful in a number of areas, they are inefficient in understanding threaded discussions due to three reasons: (I) as most of users read existing messages before posting, posts in a discussion thread are temporally dependent on the previous ones; It causes the semantics and structure to be coupled with each other in threaded discussions; (II) in online discussion threads, there are a lot of junk posts which are useless and may disturb content analysis; and (III) it is very hard to judge the quality of a post. In this paper, we propose a sparse coding-based model named SMSS to Simultaneously Model Semantics and Structure of threaded discussions. The model projects each post into a topic space, and approximates each post by a linear combination of previous posts in the same discussion thread. Meanwhile, the model also imposes two sparse constraints to force a sparse post reconstruction in the topic space and a sparse post approximation from previous posts. The sparse properties effectively take into account the characteristics of threaded discussions. Towards the above three problems, we demonstrate the competency of our model in three applications: reconstructing reply structure of threaded discussions, identifying junk posts, and finding experts in a given board/sub-board in web communities. Experimental results show encouraging performance of the proposed SMSS model in all these applications.

References

1. ]]K. Balog, L. Azzopardi, and M. de Rijke. A language modeling framework for expert finding. Information Processing and Management, 06(003):1--12, 2008.
2. ]]D.M. Blei and J.D. Lafferty. Dynamic topic models. In Proc. of ICML, pages 113--120, 2006.
3. ]]D.M. Blei, A.Y. Ng, and M.I. Jordan. Latent dirichlet allocation. Journal of Machine Learning Research, 3(6):993--1022, 2003.
4. ]]W. Buntine and A. Jakulin. Applying discrete PCA in data analysis. In Proc. of UAI, pages 59--66, 2004.
5. ]]C. Chemudugunta, P. Smyth, and M. Steyvers. Modeling general and specific aspects of documents with a probabilistic topic model. Advances in newral information processing systems, 41(6):391--407, 1990.
6. ]]G. Cong, L. Wang, C.-Y. Lin, Y.-I. Song, and Y. Sun. Finding question-answer pairs from online forums. In Proc. 31st SIGIR, pages 467--474, 2008.
7. ]]S. Ding, G. Cong, C.-Y. Lin, and X. Zhu. Using conditional random Éelds to extract contexts and answers of questions from online forums. In Proc. 11th ACL, pages 710--718, 2008.
8. ]]X. Gu and W.-Y. Ma. Building implicit links from content for forum search. In Proc. 29th SIGIR, pages 300--307, 2006.
9. ]]T. Hofmann. Probabilistic latent semantic indexing. In Proc. 29th SIGIR, pages 50--57, 1999.
10. ]]J. Huang, M. Zhou, and D. Yang. Extracting chatbot knowledge from online discussion forums. In Proc. 11th IJCAI, pages 423--428, 2006.
11. ]]J.Scott. Social Network Analysis: A Handbook. Sage Publications, London, 2000.
12. ]]J.W. Kim, K.S. Candan, and M.E. Donderler. Topic segmentation of message hierarchies for indexing and navigation support. In Proc. 16th WWW, pages 322--331, 2005.
13. ]]J. Kleinberg. Authoritative sources in a hyperlinked environment. J. ACM, 46(5):604--622, 1999.
14. ]]A. McCallum, A. Corrada-Emmanuel, and X. Wang. Topic and role discovery in social networks. In Proc. of IJCAI, pages 249--272, 2007.
15. ]]G. Mishne, D. Carmel, and R. Lempel. Blocking blog spam with language model disagreement. In Proc. of AIRWeb, 2005.
16. ]]L. Page, S. Brin, R. Motwani, and T. Winograd. The PageRank citation ranking: Bringing order to the web. Technical report. Stanford University, 1998.
17. ]]X.-H. Phan, L.-M. Nguyen, and S. Horiguchi. Learning to classify short and sparse text & web with hidden topics from large-scale data collections. In Proc. of WWW, pages 91--100, 2008.
18. ]]M. Rosen-Zvi, T. Gri±ths, M. Steyvers, and P. Smyth. The author-topic model for authors and documents. In Proc. of UAI, pages 487--494, 2004.
19. ]]D. Shen, Q. Yang, J.-T. Sun, and Z. Chen. Thread detection in dynamic text message streams. In Proc. 29th SIGIR, pages 35--42, 2006.
20. ]]X. Song, B.L. Tseng, C.-Y. Lin, and M.-T. Sun. Personalized recommendation driven by information flow. In Proc. 29th SIGIR, pages 509--516, 2006.
21. ]]C. Wang, D.M. Blei, and D. Heckerman. Continuous time dynamic topic models. In Proc. of UAI, 2008.
22. ]]J. Zhang, M.S. Ackerman, and L. Adamic. Expertise networks in online communities: structure and algorithms. In Proc. of WWW, pages 221--230, 2007.