Continuous Semantic Crawling Events

Abstract

The need to tap into the wisdom of the crowd" via social networks in real-time has already been demonstrated during critical events such as the Arab Spring and the recently concluded US Elections. As Twitter becomes a platform of choice for streaming event related information in real-time, we face several challenges in the related to �filtering, realtime monitoring and tracking of the dynamic evolution of an event. We present a novel approach to continuously track an evolving event on Twitter by leveraging hashtags that are �filtered using an evolving background knowledge (Wikipedia). Our approach (1) collects evolving hashtags by adapting tag co-occurrence information; (2) exploits the semantics of events for selecting hashtags by monitoring and leveraging the corresponding Wikipedia event pages; and (3) fi�lters tweets using hashtags that are determined to be semantically relevant to the event. We evaluated our approach on two recent events: United States Presidential Elections 2012 and Hurricane Sandy. The results demonstrate that Wikipedia can be leveraged to determine, rank, and evolve small, high quality event-related hashtags in real-time to �lter event-relevant tweets stream.