Difference between revisions of "Continuous Semantic Crawling Events"
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| + | 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. | ||
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=Hashtag Analysis= | =Hashtag Analysis= | ||
=Approach= | =Approach= | ||
=Evaluation= | =Evaluation= | ||
Revision as of 03:52, 25 December 2012
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.
