2009 UserGroupingBehaviorinOnlineFor

• (Shi et al., 2009) ⇒ Xiaolin Shi, Jun Zhu, Rui Cai, and Lei Zhang. (2009). “User Grouping Behavior in Online Forums.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557105

Subject Headings:

Notes

• Categories and Subject Descriptors: J.4 Computer Applications: Social and Behaviorial Sciences; H.2.8 Information Systems: Database Management — Database Applications, Data mining.
• General Terms: Measurement, Experimentation

Cited By

• http://scholar.google.com/scholar?q=%22User+grouping+behavior+in+online+forums%22+2009
• http://portal.acm.org/citation.cfm?doid=1557019.1557105&preflayout=flat#citedby

Quotes

Author Keywords

Social Networks, Online Forums, Information Diffusion, Social Selection Model

Abstract

Online forums represent one type of social media that is particularly rich for studying human behavior in information seeking and diffusing. The way users join communities is a reflection of the changing and expanding of their interests toward information. In this paper, we study the patterns of user participation behavior, and the feature factors that influence such behavior on different forum datasets. We find that, despite the relative randomness and lesser commitment of structural relationships in online forums, users' community joining behaviors display some strong regularities. One particularly interesting observation is that the very weak relationships between users defined by online replies have similar diffusion curves as those of real friendships or co-authorships. We build social selection models, Bipartite Markov Random Field (BiMRF), to quantitatively evaluate the prediction performance of those feature factors and their relationships. Using these models, we show that some features carry supplementary information, and the effectiveness of different features vary in different types of forums. Moreover, the results of BiMRF with two-star configurations suggest that the feature of user similarity defined by frequency of communication or number of common friends is inadequate to predict grouping behavior, but adding node-level features can improve the fit of the model.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 UserGroupingBehaviorinOnlineFor	Jun Zhu Rui Cai Lei Zhang Xiaolin Shi			User Grouping Behavior in Online Forums		KDD-2009 Proceedings		10.1145/1557019.1557105		2009