Topic classification in social media using metadata from hyperlinked objects

  • Authors:

  • Sheila Kinsella;Alexandre Passant;John G. Breslin

  • Affiliations:

  • Digital Enterprise Research Institute, National University of Ireland, Galway;Digital Enterprise Research Institute, National University of Ireland, Galway;Digital Enterprise Research Institute and School of Engineering and Informatics, National University of Ireland, Galway

  • Venue:
  • ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
  • Year:
  • 2011

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Abstract

Social media presents unique challenges for topic classification, including the brevity of posts, the informal nature of conversations, and the frequent reliance on external hyperlinks to give context to a conversation. In this paper we investigate the usefulness of these external hyperlinks for determining the topic of an individual post. We focus specifically on hyperlinks to objects which have related metadata available on the Web, including Amazon products and YouTube videos. Our experiments show that the inclusion of metadata from hyperlinked objects in addition to the original post content improved classifier performance measured with the F-score from 84% to 90%. Further, even classification based on object metadata alone outperforms classification based on the original post content.