# Baitradar: A Multi-Model Clickbait Detection Algorithm Using Deep Learning

arXiv: https://arxiv.org/abs/2505.17448 · HTML: https://mlsanity.com/paper/2505.17448/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 23 May 2025.

## Builds on (most cited first, 4 of 4)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Skip-Thought Vectors](https://mlsanity.com/paper/1506.06726/) (2015)
- [Stop Clickbait: Detecting and Preventing Clickbaits in Online News Media](https://mlsanity.com/paper/1610.09786/) (2016)
- [We used Neural Networks to Detect Clickbaits: You won't believe what happened Next!](https://mlsanity.com/paper/1612.01340/) (2016)

## Built on by (most cited first, 3 of 3)

- [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)
- [BanClickThumb: A Multimodal Dataset and Transformer Fusion Benchmarks for Clickbait Detection in Bengali YouTube Videos](https://mlsanity.com/paper/2607.17182/) (2026)

## Abstract (arXiv)

Following the rising popularity of YouTube, there is an emerging problem on this platform called clickbait, which provokes users to click on videos using attractive titles and thumbnails. As a result, users ended up watching a video that does not have the content as publicized in the title. This issue is addressed in this study by proposing an algorithm called BaitRadar, which uses a deep learning technique where six inference models are jointly consulted to make the final classification decision. These models focus on different attributes of the video, including title, comments, thumbnail, tags, video statistics and audio transcript. The final classification is attained by computing the average of multiple models to provide a robust and accurate output even in situation where there is missing data. The proposed method is tested on 1,400 YouTube videos. On average, a test accuracy of 98% is achieved with an inference time of less than 2s.