%0 Conference Paper %1 ghosh2012cognos %A Ghosh, Saptarshi %A Sharma, Naveen %A Benevenuto, Fabricio %A Ganguly, Niloy %A Gummadi, Krishna %B Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval %C New York, NY, USA %D 2012 %I ACM %K classification expert search twitter user %P 575--590 %R 10.1145/2348283.2348361 %T Cognos: crowdsourcing search for topic experts in microblogs %U http://doi.acm.org/10.1145/2348283.2348361 %X 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. %@ 978-1-4503-1472-5

@inproceedings{ghosh2012cognos, 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.}, acmid = {2348361}, added-at = {2013-10-19T09:48:44.000+0200}, address = {New York, NY, USA}, author = {Ghosh, Saptarshi and Sharma, Naveen and Benevenuto, Fabricio and Ganguly, Niloy and Gummadi, Krishna}, biburl = {https://www.bibsonomy.org/bibtex/2d78b2765fd3c58fc8726d5460654d65a/jaeschke}, booktitle = {Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval}, description = {Cognos}, doi = {10.1145/2348283.2348361}, interhash = {78edbb10b28fcf5bd779e94aee724116}, intrahash = {d78b2765fd3c58fc8726d5460654d65a}, isbn = {978-1-4503-1472-5}, keywords = {classification expert search twitter user}, location = {Portland, Oregon, USA}, numpages = {16}, pages = {575--590}, publisher = {ACM}, series = {SIGIR '12}, timestamp = {2014-07-28T15:57:31.000+0200}, title = {Cognos: crowdsourcing search for topic experts in microblogs}, url = {http://doi.acm.org/10.1145/2348283.2348361}, year = 2012 }