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User Joining Behavior in Online Forums

User Joining Behavior in Online Forums. Xiaolin Shi, Jun Zhu, Rui Cai, Lei Zhang Univ. of Michigan, Tsinghua Univ., Microsoft Research Asia. Motivations. A process of information diffusion and epidemics Building social computing systems: providing valuable insights to improve user experience

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User Joining Behavior in Online Forums

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  1. User Joining Behavior in Online Forums Xiaolin Shi, Jun Zhu, Rui Cai, Lei Zhang Univ. of Michigan, Tsinghua Univ., Microsoft Research Asia

  2. Motivations A process of information diffusion and epidemics Building social computing systems: providing valuable insights to improve user experience Difference between online forums and many other social media: relative randomness and lesser commitment of structural relationships.

  3. Community posts rating

  4. thread reply reply

  5. Definitions • Communities: explicitly pre-defined • Relationships: temporary, require little effort • User-community relationship or user joining community: posting • User-user relationship: reply

  6. Illustration of users joining communities Time t: post Community A Alice reply Community B Bob

  7. Illustration of users joining communities Time t+1: post Community A Alice reply Community B Bob

  8. Three central questions 8 Factors in online forums that influence people’s behavior in joining communities Relationships between these factors Differences of user grouping behavior in forums of different types (such as news forums versus technology forums)

  9. Description of datasets

  10. The first question What are the factors in online forums that influence people’s behavior in joining communities?

  11. Feature factors Features at t Join at t+1? Features at t • Examine the relationship between the features at time t and a user joining a community at time t+1 • Features associated with users: number of reply neighbors in the community • Features associated with communities • Community size: popularity of information • Average rating of top posts: authority or interestingness of information

  12. Diffusion curves 1: reply relationship (a) Digg (b) Apple • Observations: • Exhibiting law of diminishing returns – curves increase fast at the beginning, but more and more slowly towards the end. • “S-shaped” behavior at k = 0, 1, 2 (c) Google Earth (d) Honda The probability of a user joining a community at time t as a function of the number of reply neighbors who are active in that community at time t-1

  13. Reply relationship vs. stronger relationships LiveJournal Digg The weak relationship of reply exhibit similar patterns in its diffusion curves as those of strong relationships, such as real friendship and co-authorship in [Backstrom, 2006]

  14. Diffusion curves 2: community sizes (a) Digg (b) Apple The growth of the joining probability is sub-linearorlinear with respect to the normalized community size. (c) Google Earth (d) Honda The probability of a user joining a community at time t as a function of the normalized community size at time t-1.

  15. Diffusion curves 3: average ratings of top posts (a) Digg (b) Google Earth The probability of a user joining a community at time t as a function of the average rating of the top 10% high rating posts in the community at time t-1. The difference may be due to the different interfaces of the systems and people’s purposes in joining two types of forums.

  16. The second question What are the relationships between these factors: which ones are more effective in predicting the user joining behavior, and which ones carry supplementary information?

  17. Bipartite Markov Random Fields A bipartite MRF model with N communities and M users at time t. is an instance of the connections between users and communities at time t. The dashed edges are observed evidence.

  18. Evaluation results 0.5 1.0 • Evaluate the performance of prediction by measuring the areas under ROC curves poor  excellent

  19. Conclusions • Users’ joining behavior in online forums has strong regularities – in contrast to the little effort and commitment they have: • Reply relationship has similar diffusion curves as other strong social ties • Impact by features associated with communities • Relationships between the features • Different effects of feature associated with users versus features associated with communities • Carry supplementary information • Differences of user joining behavior in news forums and technology forums.

  20. Thank you! Questions?

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