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

  1. Víctor Fresno
  2. Arkaitz Zubiaga
  3. Heng Ji
  4. Raquel Martínez
Journal:
Procesamiento del lenguaje natural

ISSN: 1135-5948

Year of publication: 2015

Issue: 54

Pages: 85-92

Type: Article

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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 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. Our proposed approaches have been evaluated in two Twitter datasets associated with real-world events, obtaining encouraging results.

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Funding information

Funders

  • U.S. Army Research Laboratory
    • W911NF-09-2-0053
  • Norfolk Southern

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