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ECIR
2016
Springer

Clickbait Detection

4 years 3 months ago
Clickbait Detection
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.
Martin Potthast, Sebastian Köpsel, Benno Stei
Added 02 Apr 2016
Updated 02 Apr 2016
Type Journal
Year 2016
Where ECIR
Authors Martin Potthast, Sebastian Köpsel, Benno Stein, Matthias Hagen
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