  In this paper, we propose an approach for the detection of clickbait posts in
online social media (OSM). Clickbait posts are short catchy phrases that
attract a user's attention to click to an article. The approach is based on a
machine learning (ML) classifier capable of distinguishing between clickbait
and legitimate posts published in OSM. The suggested classifier is based on a
variety of features, including image related features, linguistic analysis, and
methods for abuser detection. In order to evaluate our method, we used two
datasets provided by Clickbait Challenge 2017. The best performance obtained by
the ML classifier was an AUC of 0.8, an accuracy of 0.812, precision of 0.819,
and recall of 0.966. In addition, as opposed to previous studies, we found that
clickbait post titles are statistically significant shorter than legitimate
post titles. Finally, we found that counting the number of formal English words
in the given content is useful for clickbait detection.
