# Using Neural Network for Identifying Clickbaits in Online News Media

arXiv: https://arxiv.org/abs/1806.07713 · HTML: https://mlsanity.com/paper/1806.07713/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2018.

## Built on by (most cited first, 5 of 5)

- [On Unifying Misinformation Detection](https://mlsanity.com/paper/2104.05243/) (2021)
- [SOK: Fake News Outbreak 2021: Can We Stop the Viral Spread?](https://mlsanity.com/paper/2105.10671/) (2021)
- [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)
- [SmartMixed: A Two-Phase Training Strategy for Adaptive Activation Function Learning in Neural Networks](https://mlsanity.com/paper/2510.22450/) (2025)

## Abstract (arXiv)

Online news media sometimes use misleading headlines to lure users to open the news article. These catchy headlines that attract users but disappointed them at the end, are called Clickbaits. Because of the importance of automatic clickbait detection in online medias, lots of machine learning methods were proposed and employed to find the clickbait headlines. In this research, a model using deep learning methods is proposed to find the clickbaits in Clickbait Challenge 2017's dataset. The proposed model gained the first rank in the Clickbait Challenge 2017 in terms of Mean Squared Error. Also, data analytics and visualization techniques are employed to explore and discover the provided dataset to get more insight from the data.