# Detecting Clickbait in Online Social Media: You Won't Believe How We Did It

arXiv: https://arxiv.org/abs/1710.06699 · HTML: https://mlsanity.com/paper/1710.06699/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2017.

## Built on by (most cited first, 4 of 4)

- [Is it a click bait? Let's predict using Machine Learning](https://mlsanity.com/paper/2106.07348/) (2021)
- [Clickbait Detection in YouTube Videos](https://mlsanity.com/paper/2107.12791/) (2021)
- [ClickGuard: A Trustworthy Adaptive Fusion Framework for Clickbait Detection](https://mlsanity.com/paper/2604.07272/) (2026)
- [YTClickbait21K: Human-Annotated Multimodal Dataset for YouTube Clickbait Detection Across Diverse Channels and Content Categories](https://mlsanity.com/paper/2606.14780/) (2026)

## Abstract (arXiv)

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.