We address the problem of identifying local experts on Twitter. Specifically, we propose a local expertise framework that integrates both users' topical expertise and their local authority by leveraging over 15 million geo-tagged Twitter lists. We evaluate the proposed approach across 16 queries coupled with over 2,000 individual judgments from Amazon Mechanical Turk. Our initial experiments find significant improvement over a naive local expert finding approach, suggesting the promise of exploiting geo-tagged Twitter lists for local expert finding.
This paper addresses the problem of identifying local experts in social media systems like Twitter. Local experts -in contrast to general topic experts -have specialized knowledge focused around a particular location, and are important for many applications including answering local information needs and interacting with community experts. And yet identifying these experts is difficult. Hence in this paper, we propose a geo-spatial-driven approach for identifying local experts that leverages the fine-grained GPS coordinates of millions of Twitter users. We propose a local expertise framework that integrates both users' topical expertise and their local authority. Concretely, we estimate a user's local authority via a novel spatial proximity expertise approach that leverages over 15 million geotagged Twitter lists. We estimate a user's topical expertise based on expertise propagation over 600 million geo-tagged social connections on Twitter. We evaluate the proposed approach across 56 queries coupled with over 11,000 individual judgments from Amazon Mechanical Turk. We find significant improvement over both general (non-local) expert approaches and comparable local expert finding approaches.
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