A statistical interpretation of term specificity and its application in retrieval
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Authoritative sources in a hyperlinked environment
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AuGEAS: authoritativeness grading, estimation, and sorting
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HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 03
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WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
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ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
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Tagging users based on Twitter lists
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Inferring who-is-who in the Twitter social network
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Topic-sensitive probabilistic model for expert finding in question answer communities
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Active microbloggers: identifying influencers, leaders and discussers in microblogging networks
SPIRE'12 Proceedings of the 19th international conference on String Processing and Information Retrieval
Bursty subgraphs in social networks
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Choosing the right crowd: expert finding in social networks
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New metric measure for the improvement of search results in microblogs
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Discovering social media experts by integrating social networks and contents
ADC '12 Proceedings of the Twenty-Third Australasian Database Conference - Volume 124
Estimating sharer reputation via social data calibration
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Topical authority propagation on microblogs
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Data Mining and Knowledge Discovery
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Content in microblogging systems such as Twitter is produced by tens to hundreds of millions of users. This diversity is a notable strength, but also presents the challenge of finding the most interesting and authoritative authors for any given topic. To address this, we first propose a set of features for characterizing social media authors, including both nodal and topical metrics. We then show how probabilistic clustering over this feature space, followed by a within-cluster ranking procedure, can yield a final list of top authors for a given topic. We present results across several topics, along with results from a user study confirming that our method finds authors who are significantly more interesting and authoritative than those resulting from several baseline conditions. Additionally our algorithm is computationally feasible in near real-time scenarios making it an attractive alternative for capturing the rapidly changing dynamics of microblogs.