Ranking of Event-focused English Tweets Based on Relevance and Informativeness

Abstract

A Twitter event contains enormous number of tweets including views or comments, real-time update of the event, conversation and often lots of irrelevant information too. Thus, ranking of the most important and relevant tweets for an event is a difficult research problem. With time, researchers proposed many state-of-the-art tweet ranking solutions. These solutions often return pointless tweets also which deluge the informative tweets. This paper proposes an approach to rank the most informative and relevant tweets for an event based on relevance and informativeness measure. To measure relevance and informativeness of tweets, new features are introduced and used to train ranking model. The performance of the approach is evaluated through experimental result and reports comparable performance in this domain.