# Diving Deep into Clickbaits: Who Use Them to What Extents in Which Topics with What Effects?

arXiv: https://arxiv.org/abs/1703.09400 · HTML: https://mlsanity.com/paper/1703.09400/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2017.

## Built on by (most cited first, 11 of 11)

- [Clickbait Spoiling via Question Answering and Passage Retrieval](https://mlsanity.com/paper/2203.10282/) (2022)
- [SciClops: Detecting and Contextualizing Scientific Claims for Assisting Manual Fact-Checking](https://mlsanity.com/paper/2110.13090/) (2021)
- [Multimodal Clickbait Detection by De-confounding Biases Using Causal Representation Inference](https://mlsanity.com/paper/2410.07673/) (2024)
- [Not all Fake News is Written: A Dataset and Analysis of Misleading Video Headlines](https://mlsanity.com/paper/2310.13859/) (2023)
- [Clickbait Detection via Large Language Models](https://mlsanity.com/paper/2306.09597/) (2023)
- [Prompt-tuning for Clickbait Detection via Text Summarization](https://mlsanity.com/paper/2404.11206/) (2024)
- [What Drives Online Popularity: Author, Content or Sharers? Estimating Spread Dynamics with Bayesian Mixture Hawkes](https://mlsanity.com/paper/2406.03390/) (2024)
- [BanglaBait: Semi-Supervised Adversarial Approach for Clickbait Detection on Bangla Clickbait Dataset](https://mlsanity.com/paper/2311.06204/) (2023)
- [Is it a click bait? Let's predict using Machine Learning](https://mlsanity.com/paper/2106.07348/) (2021)
- [Te Ahorr'e Un Click: A Revised Definition of Clickbait and Detection in Spanish News](https://mlsanity.com/paper/2507.09777/) (2025)
- [Emotion-Aware Clickbait Attack in Social Media](https://mlsanity.com/paper/2604.27369/) (2026)

## Abstract (arXiv)

The use of alluring headlines (clickbait) to tempt the readers has become a growing practice nowadays. For the sake of existence in the highly competitive media industry, most of the on-line media including the mainstream ones, have started following this practice. Although the wide-spread practice of clickbait makes the reader's reliability on media vulnerable, a large scale analysis to reveal this fact is still absent. In this paper, we analyze 1.67 million Facebook posts created by 153 media organizations to understand the extent of clickbait practice, its impact and user engagement by using our own developed clickbait detection model. The model uses distributed sub-word embeddings learned from a large corpus. The accuracy of the model is 98.3%. Powered with this model, we further study the distribution of topics in clickbait and non-clickbait contents.