Clickbait Detection

  • Martin Potthast
  • Sebastian Köpsel
  • Benno Stein
  • Matthias Hagen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9626)

Abstract

This paper proposes a new model for the detection of clickbait, i.e., short messages that lure readers to click a link. Clickbait is primarily used by online content publishers to increase their readership, whereas its automatic detection will give readers a way of filtering their news stream. We contribute by compiling the first clickbait corpus of 2992 Twitter tweets, 767 of which are clickbait, and, by developing a clickbait model based on 215 features that enables a random forest classifier to achieve 0.79 ROC-AUC at 0.76 precision and 0.76 recall.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Martin Potthast
    • 1
  • Sebastian Köpsel
    • 1
  • Benno Stein
    • 1
  • Matthias Hagen
    • 1
  1. 1.Bauhaus-Universität WeimarWeimarGermany

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