From Chirps to Whistles: Discovering Event-specific Informative Content from Twitter

Abstract

Twitter has brought a paradigm shift in the way we produce and curate information about real-life events. Huge volumes of user-generated tweets are produced in Twitter, related to events. Not, all of them are useful and informative. A sizable amount of tweets are spams and colloquial personal status updates, which does not provide any useful information about an event. Thus, it is necessary to identify, rank and segregate event-specific informative content from the tweet streams. In this paper, we develop a novel generic framework based on the principle of mutual reinforcement, for identifying event-specific informative content from Twitter. Mutually reinforcing relationships between tweets, hashtags, text units, URLs and users are defined and represented using TwitterEventInfoGraph. An algorithm - TwitterEventInfoRank is proposed, that simultaneously ranks tweets, hashtags, text units, URLs and users producing them, in terms of event-specific informativeness by leveraging the semantics of relationships between each of them as represented by TwitterEventInfoGraph. Experiments and observations are reported on four million (approx) tweets collected for five real-life events, and evaluated against popular baseline techniques showing significant improvement in performance.