University of Tehran at RepLab 2014.

In this paper, we present our approach to author ranking subtask; which is a part of author-profiling task in RepLab 2014. In this subtask, systems are expected to detect influential authors and opinion makers on Twitter web- site. The systems' output, for a given domain, must be a ranked list of authors according to their probability of being an influential author or opinion maker. Our system utilizes a Time-sensitive Voting algorithm, which is based on the hypothesis that influential authors tweet actively about topics of their interest. In this method, hot topics of each domain are extracted and a time-sensitive vot- ing algorithm ranks each authors on their respective topics.