User Interest and Interaction Structure in Online Forums


  • Di Liu Carnegie Mellon University
  • Daniel Percival Carnegie Mellon University
  • Stephen Fienberg Carnegie Mellon University


Social Networks, Clustering, Text Analysis


We present a new similarity measure tailored to posts in an online forum. Our measure takes into account all the available information about user interest and interaction — the content of posts, the threads in the forum, and the author of the posts. We use this post similarity to build a similarity between users, based on principal coordinate analysis. This allows easy visualization of the user activity as well. Similarity between users has numerous applications, such as clustering or classification. We show that including the author of a post in the post similarity has a smoothing effect on principal coordinate projections. We demonstrate our method on real data drawn from an internal corporate forum, and compare our results to those given by a standard document classification method. We conclude our method gives a more detailed picture of both the local and global network structure.




How to Cite

Liu, D., Percival, D., & Fienberg, S. (2010). User Interest and Interaction Structure in Online Forums. Proceedings of the International AAAI Conference on Web and Social Media, 4(1), 283-286. Retrieved from