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- W1968133322 abstract "Finding topic experts on microblogging sites with millions of users, such as Twitter, is a hard and challenging problem. In this paper, we propose and investigate a new methodology for discovering topic 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 curated by individual users to include experts on topics that interest them and whose meta-data (List names and descriptions) provides valuable semantic cues to the experts' domain of expertise. We mined List information to build Cognos, a system for finding topic experts in 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 results comparable to, if not better than, those given by the official Twitter experts search engine for a wide range of queries in user tests. Our study highlights Lists as a potentially valuable source of information for future content or expert search systems in Twitter." @default.
- W1968133322 created "2016-06-24" @default.
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- W1968133322 date "2012-08-12" @default.
- W1968133322 modified "2023-10-16" @default.
- W1968133322 title "Cognos" @default.
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- W1968133322 doi "https://doi.org/10.1145/2348283.2348361" @default.
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