The Webis Clickbait Corpus 2016 (Webis-Clickbait-16) comprises 2992 Twitter tweets sampled from top 20 news publishers as per retweets in 2014. The tweets have been manually annotated by three independent annotators with regard to whether they can be considered clickbait. A total of 767 tweets are considered clickbait by the majority of annotators. The majority vote of reviewers can be used as a ground truth to build clickbait detection technology. This corpus is the first of its kind and gives rise to the development of technology to tackle clickbait.

{"references": ["Martin Potthast, Sebastian K\u00f6psel, Benno Stein, and Matthias Hagen. Clickbait Detection. In Nicola Ferro et al, editors, Advances in Information Retrieval. 38th European Conference on IR Research (ECIR 2016) volume 9626 of Lecture Notes in Computer Science, pages 810-817, Berlin Heidelberg New York, March 2016. Springer"]}