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We present a new similarity measure tailored to posts in an online forum. Ourmeasure takes into account all the available information about user interestand interaction --- the content of posts, the threads in the forum, and theauthor of the posts. We use this post similarity to build a similarity betweenusers, based on principal coordinate analysis. This allows easy visualizationof the user activity as well. Similarity between users has numerousapplications, such as clustering or classification. We show that including theauthor of a post in the post similarity has a smoothing effect on principalcoordinate projections. We demonstrate our method on real data drawn from aninternal corporate forum, and compare our results to those given by a standarddocument classification method. We conclude our method gives a more detailedpicture of both the local and global network structure.

User Interest and Interaction Structure in Online Forums