Terms of a Feather: Content-Based News Recommendation and Discovery Using Twitter

  • Owen Phelan
  • Kevin McCarthy
  • Mike Bennett
  • Barry Smyth
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6611)


User-generated content has dominated the web’s recent growth and today the so-called real-time web provides us with unprecedented access to the real-time opinions, views, and ratings of millions of users. For example, Twitter’s 200m+ users are generating in the region of 1000+ tweets per second. In this work, we propose that this data can be harnessed as a useful source of recommendation knowledge. We describe a social news service called Buzzer that is capable of adapting to the conversations that are taking place on Twitter to ranking personal RSS subscriptions. This is achieved by a content-based approach of mining trending terms from both the public Twitter timeline and from the timeline of tweets published by a user’s own Twitter friend subscriptions. We also present results of a live-user evaluation which demonstrates how these ranking strategies can add better item filtering and discovery value to conventional recency-based RSS ranking techniques.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Owen Phelan
    • 1
  • Kevin McCarthy
    • 1
  • Mike Bennett
    • 1
  • Barry Smyth
    • 1
  1. 1.CLARITY: Centre for Sensor Web Technologies, School of Computer Science & InformaticsUniversity College DublinIreland

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