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 | + | 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) filters 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 filter event-relevant tweets stream. |
| − | to determine, rank, and evolve small, high quality event-related hashtags in real-time to | + | |
=Hashtag Analysis= | =Hashtag Analysis= | ||
=Approach= | =Approach= | ||
=Evaluation= | =Evaluation= | ||
Revision as of 03:53, 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) filters 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 filter event-relevant tweets stream.
