Clickbait Detection

@inproceedings{Potthast2016ClickbaitD,
  title={Clickbait Detection},
  author={Martin Potthast and Sebastian K{\"o}psel and Benno Stein and Matthias Hagen},
  booktitle={ECIR},
  year={2016}
}
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… CONTINUE READING

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Crowdsourcing a Large Corpus of Clickbait on Twitter

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CITES BACKGROUND, RESULTS & METHODS

A Community Driven Approach for click Bait Reporting

  • 2018 26th International Conference on Software, Telecommunications and Computer Networks (SoftCOM)
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CITES BACKGROUND
HIGHLY INFLUENCED

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