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2017

Machine Learning Based Detection of Clickbait Posts in Social Media

Cao, Xinyue, Le, Thai, Jason et al.

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Understand

Clickbait (headlines) make use of misleading titles that hide critical information from or exaggerate the content on the landing target pages to entice clicks.

As clickbaits often use eye-catching wording to attract viewers, target contents are often of low quality.
Clickbaits are especially widespread on social media such as Twitter, adversely impacting user experience by causing immense dissatisfaction.
Hence, it has become increasingly important to put forward a widely applicable approach to identify and detect clickbaits.

Machine Learning Based Detection of Clickbait Posts in Social Media · Around

Built on

Improving the Reproducibility of PAN’s Shared Tasks: Plagiarism Detection, Author Identification, and Author Profiling
M. Potthast, T. Gollub, F. Rangel, P. Rosso, E. Stamatatos, and B. Stein · 2014
Earlier work this paper cites.

Similar

Clickbait Detection
M. Potthast, S. Köpsel, B. Stein, and M. Hagen · 2016
Cited alongside, same era.
Crowdsourcing a Large Corpus of Clickbait on Twitter
M. Potthast, T. Gollub, K. Komlossy, S. Schuster, M. Wiegmann, E. Garces, M. Hagen, and B. Stein
Cited in the paper.

Then

The Clickbait Challenge 2017: Towards a Regression Model for Clickbait Strength
M. Potthast, T. Gollub, M. Hagen, and B. Stein · 2017
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