Harnessing linked knowledge sources for topic classification in social media

  • Authors:

  • Amparo E. Cano;Andrea Varga;Matthew Rowe;Fabio Ciravegna;Yulan He

  • Affiliations:

  • The Open University, UK;The University of Sheffield, UK;Lancaster University, UK;The University of Sheffield;Aston University, UK

  • Venue:
  • Proceedings of the 24th ACM Conference on Hypertext and Social Media
  • Year:
  • 2013

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Abstract

Topic classification (TC) of short text messages offers an effective and fast way to reveal events happening around the world ranging from those related to Disaster (e.g. Sandy hurricane) to those related to Violence (e.g. Egypt revolution). Previous approaches to TC have mostly focused on exploiting individual knowledge sources (KS) (e.g. DBpedia or Freebase) without considering the graph structures that surround concepts present in KSs when detecting the topics of Tweets. In this paper we introduce a novel approach for harnessing such graph structures from multiple linked KSs, by: (i) building a conceptual representation of the KSs, (ii) leveraging contextual information about concepts by exploiting semantic concept graphs, and (iii) providing a principled way for the combination of KSs. Experiments evaluating our TC classifier in the context of Violence detection (VD) and Emergency Responses (ER) show promising results that significantly outperform various baseline models including an approach using a single KS without linked data and an approach using only Tweets.