Abstract
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, these approaches can miss 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 eventrelated 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. Our proposed approaches have been evaluated in two Twitter datasets associated with real-world events, obtaining encouraging results.
Original language | English (US) |
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Pages (from-to) | 85-92 |
Number of pages | 8 |
Journal | Procesamiento del Lenguaje Natural |
Volume | 54 |
State | Published - Mar 1 2015 |
Externally published | Yes |
Keywords
- Information retrieval
- Natural hazards monitoring
- Pseudo-relevance feedback
- Real-time social media analysis
ASJC Scopus subject areas
- Language and Linguistics
- Linguistics and Language
- Computer Science Applications