# We used Neural Networks to Detect Clickbaits: You won't believe what happened Next!

arXiv: https://arxiv.org/abs/1612.01340 · HTML: https://mlsanity.com/paper/1612.01340/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2016.

## Built on by (most cited first, 9 of 9)

- [LightMove: A Lightweight Next-POI Recommendation for Taxicab Rooftop Advertising](https://mlsanity.com/paper/2108.04993/) (2021)
- [Multimodal Clickbait Detection by De-confounding Biases Using Causal Representation Inference](https://mlsanity.com/paper/2410.07673/) (2024)
- [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)
- [Baitradar: A Multi-Model Clickbait Detection Algorithm Using Deep Learning](https://mlsanity.com/paper/2505.17448/) (2025)
- [Clickbait Headline Detection in Indonesian News Sites using Multilingual Bidirectional Encoder Representations from Transformers (M-BERT)](https://mlsanity.com/paper/2102.01497/) (2021)
- [Web-based Application for Detecting Indonesian Clickbait Headlines using IndoBERT](https://mlsanity.com/paper/2102.10601/) (2021)
- [Is it a click bait? Let's predict using Machine Learning](https://mlsanity.com/paper/2106.07348/) (2021)
- [POSHAN: Cardinal POS Pattern Guided Attention for News Headline Incongruence](https://mlsanity.com/paper/2111.03547/) (2021)

## Abstract (arXiv)

Online content publishers often use catchy headlines for their articles in order to attract users to their websites. These headlines, popularly known as clickbaits, exploit a user's curiosity gap and lure them to click on links that often disappoint them. Existing methods for automatically detecting clickbaits rely on heavy feature engineering and domain knowledge. Here, we introduce a neural network architecture based on Recurrent Neural Networks for detecting clickbaits. Our model relies on distributed word representations learned from a large unannotated corpora, and character embeddings learned via Convolutional Neural Networks. Experimental results on a dataset of news headlines show that our model outperforms existing techniques for clickbait detection with an accuracy of 0.98 with F1-score of 0.98 and ROC-AUC of 0.99.