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Modeling Dynamic Multi-topic Discussions in Online Forums

Modeling Dynamic Multi-topic Discussions in Online Forums. Hao Wu , Jiajun Bu, Chun Chen, Can Wang, Guang Qiu, Lijun Zhang and Jianfeng Shen * Zhejiang University, China *Zhejiang Health Information Center, China. July 13, AAAI’2010 Atlanta, GA, USA. Social Media.

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Modeling Dynamic Multi-topic Discussions in Online Forums

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  1. Modeling Dynamic Multi-topic Discussions in Online Forums Hao Wu, Jiajun Bu, Chun Chen, Can Wang, Guang Qiu, Lijun Zhang and Jianfeng Shen* Zhejiang University, China *Zhejiang Health Information Center, China July 13, AAAI’2010 Atlanta, GA, USA

  2. Social Media • Web 2.0 applications socialize users online • Online Forums • Distinct platform for knowledge sharing and information exchange Reveal how information propagates on Internet. Modeling the process of topic discussions and predicting user activity is an interesting problem!

  3. Benefits of Modeling • Understand online human interactions and group forming • Improve applications e.g., recommender • Track new ideas and technology • Mine opinions about products Social network analysis User review

  4. Environment of Online Forums • Great complexity • Randomness • Usually no well-defined friendships or co-authorships • Free to posting • Topic drifts in a single thread What are the mechanisms underlying user’s participation 433,839 threads 13,599,245 posts From which perspective to view the process of topic discussion ? How to make use of the property of topics and temporal feature for modeling Modeling Dynamic Multi-topic Discussions is challenging ! How to measure the importance of a user in discussions

  5. Outline • Motivation and Intuitions • Topic Flow Models • Experimental Results • Summary

  6. Topic Flow Model (TFM) The new comer reads some of the previous comments before posting. Reply Link Topic Flow Topic diffuses through the underlying social networks The information (topic) flows from early participant to late participant .

  7. Basic Topic Flow Model (B-TFM) Thread Document: Frequency of : Frequency of : Social Network Thread Documents Peer-influence Topic Flow ParticipationRank: measures the susceptibility of a user to a ‘infective’ topic Self-preference Normalization Random Walk With Restart

  8. Topic-specific TFM (T-TFM) • Different interaction patterns according to different topics iPhone FIFA World Cup Using Latent Dirichlet Allocation [Blei 2003]

  9. Time-sensitive T-TFM (TT-TFM) • Forgetting Mechanism past now Time lapses now Time Lapse Factor

  10. Evaluation: Prediction • ParticipationRank (indicator) • The willingness of a user in participation to discussion of a topic Train Predict ? Ranking Whether a user joins in discussion? (post at least once ) Synthesize For T-TFM and TT-TFM

  11. Outline • Motivation and Intuitions • Topic Flow Models • Experimental Results • Summary

  12. Experiments • Dataset (www.honda-tech.com) • Two communities: Drag Racing and Honda/Acura • Across one year, from 09/01/2008 to 08/31/2009. posted more than the average number of posts per user.

  13. Results • Evaluations • Divide the data into 12 continuous time windows • Generate ranking for each one month data, and predict user posting activity in the following one week

  14. Model Selection • = 0.3 and 0.1 • T = 30 and 40 • = 0.01

  15. Summary • An intuitive model of discussions in online forums • Topic Flow Models (TFM) • Consider both peer-influence and self-preference • Property of latent topics • Temporal feature: forgetting mechanism • Evaluation onprediction of user activity • Future work: • Utilize the web structure of online forum • More data sets e.g., • Build recommendation system

  16. Thanks! Any Question?

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