Characterising emergent semantics in twitter lists

  • Authors:

  • Andrés García-Silva;Jeon-Hyung Kang;Kristina Lerman;Oscar Corcho

  • Affiliations:

  • Ontology Engineering Group, Facultad de Informática, Universidad Politécnica de Madrid, Spain;Information Sciences Institute, University of Southern California;Information Sciences Institute, University of Southern California;Ontology Engineering Group, Facultad de Informática, Universidad Politécnica de Madrid, Spain

  • Venue:
  • ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
  • Year:
  • 2012

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Abstract

Twitter lists organise Twitter users into multiple, often overlapping, sets. We believe that these lists capture some form of emergent semantics, which may be useful to characterise. In this paper we describe an approach for such characterisation, which consists of deriving semantic relations between lists and users by analyzing the co-occurrence of keywords in list names. We use the vector space model and Latent Dirichlet Allocation to obtain similar keywords according to co-occurrence patterns. These results are then compared to similarity measures relying on WordNet and to existing Linked Data sets. Results show that co-occurrence of keywords based on members of the lists produce more synonyms and more correlated results to that of WordNet similarity measures.