Exploiting geolocation, user and temporal information for monitoring natural hazards on twitter

Victor Fresno, Arkaitz Zubiaga, Heng Ji, Raquel Martínez

Research output: Contribution to journalArticlepeer-review

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 languageEnglish (US)
Pages (from-to)85-92
Number of pages8
JournalProcesamiento de Lenguaje Natural
Volume54
StatePublished - Mar 1 2015
Externally publishedYes

Keywords

  • Information retrieval
  • Natural hazards monitoring
  • Pseudo-relevance feedback
  • Real-time social media analysis
  • Twitter

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language
  • Computer Science Applications

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