Abstract
Expertise location is a difficult task, with expertise often being implied and liable to change. In this paper we propose a heuristic-based approach for automated identification of expertis eon Twitter. We collect tweets from experts and non-experts in different domains and compute different types of features based on the heuristics regarding properties of the messages written and re-tweeted by the experts. We show that these heuristics provide us with interesting insights regarding how experts differ from other user groups which can help guide future studies in this areas and algorithms for expertise location.
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