Characterising Emergent Semantics in Twitter Lists

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7295)


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.


Path Length Semantic Relation Latent Dirichlet Allocation Vector Space Model SPARQL Query 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Ontology Engineering Group, Facultad de InformáticaUniversidad Politécnica de MadridSpain
  2. 2.Information Sciences InstituteUniversity of Southern CaliforniaUSA

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