Exploiting Geolocation, User and Temporal Information for Natural Hazards Monitoring in Twitter

Víctor Fresno, Arkaitz Zubiaga , Heng Ji , Raquel Martínez


During emergency situation events it is important to acquire as much information about the event as possible, and social media sites like Twitter offer important real-time user contributed data. Typical Information Filtering techniques are keyword-based approaches or focused on co-occurrence with keywords. However, this approximations can lose relevant local information if messages do not contain an initially considered event-related keyword. Considering geolocation, user and temporal information within a pseudo-relevance feedback approach we can find event-related terminology but not co-occurring with initially considered keywords. Thus, taking into account the temporal aspect we can modify a query expansion function like Kullback-Leibler divergence in order to improve the Information Filtering process. The proposals has been evaluated in two datasets of real-world events obtaning encouraging results.

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