Finding Experts in Unstructured Communities through Relationships and Topics

The problem of expert finding has been explored for many structured question and answer-based social networks. Less explored is the problem of finding trusted individuals in an unstructured social setting. We explore the issue of finding experts in Reddit’s /r/programming, /r/CFB and /r/cars Subreddit – each of which is either unstructured or semi-structured – using a machine learning approach based on prior research in classifying user roles. To target the problem, we assumed that "experts" is a role similar to "answer-person" in previous research in that they have a common pattern of communication quantitatively recognizable through analysis of social network features. Using a combination of inferred topic distributions and social network structure, we trained a supervised classifer to find the topical-network "fingerprint" of experts. Among features examined for a given user include neighbor topical similarity, topical entropy, and depth 2-egocentric network properties. We compare our results with previously considered techniques for evaluating expertise: namely, ExpertiseRank (Social Network Page Rank), HITS and a simple z-score metric introduced by Zhang, Ackerman and Adamic (2007). Using our role-based algorithm, average f1 and recall scores during validation demonstrate comparable or better performance than prior social network analysis-based algorithms. We conclude by describing future metrics that can be explored in the problem of expert classifying in pure discussion communities.