This paper explores user participation behavior and the factors influencing it across various online forum datasets. It highlights how users' community joining behaviors, despite the seemingly random and less committed nature of online forums, exhibit strong regularities similar to real-life friendships or co-authorships. The study employs Bipartite Markov Random Field (BiMRF) models to evaluate the predictive performance of different feature factors, revealing that some features provide supplementary information and that their effectiveness varies across different forums. Additionally, the results suggest that user similarity based on communication frequency or number of common friends alone is insufficient for predicting grouping behavior, but incorporating node-level features can enhance the model's accuracy.
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Shi, Xiaolin, et al. "User grouping behavior in online forums." Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. 2009.
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