Discovering Topical Experts in Twitter Social Network

Finding topic experts on microblogging sites with millions of users, such as Twitter, is a hard and challenging problem. In this thesis, we propose and investigate a new methodology for inferring topical experts in the popular Twitter social network. Our methodology relies on the wisdom of the Twitter crowds – it leverages Twitter Lists, which are often carefully created by individual users to include experts on topics that interest them and whose meta-data (List names and descriptions) provide valuable semantic cues to experts’ domain of expertise. We mined List information to build Cognos, an expert search system for Twitter. Detailed experimental evaluation based on a real-world deployment shows that: (a) Cognos infers a user’s expertise more accurately and comprehensively than state-of-the-art systems that rely on the user’s bio or tweet content, (b) Cognos scales well due to built-in mechanisms to efficiently update its experts’ database with new users, and (c) Despite relying only on a single feature, namely crowdsourced Lists, Cognos yields comparable, if not better, results in user tests, as compared to the official Twitter experts search engine for a wide range of queries. Our study highlights Lists as a potentially valuable source of information for future content or expert search systems in Twitter.