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.


Recommender System News Story Twitter User Ranking Strategy Social Graph 
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 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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