# BanglaBait: Semi-Supervised Adversarial Approach for Clickbait Detection on Bangla Clickbait Dataset

arXiv: https://arxiv.org/abs/2311.06204 · HTML: https://mlsanity.com/paper/2311.06204/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 44 references that are in the map.
- First posted on arXiv 10 Nov 2023.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation](https://mlsanity.com/paper/2102.00287/) (2021)
- [Not Low-Resource Anymore: Aligner Ensembling, Batch Filtering, and New Datasets for Bengali-English Machine Translation](https://mlsanity.com/paper/2009.09359/) (2020)
- [BanFakeNews: A Dataset for Detecting Fake News in Bangla](https://mlsanity.com/paper/2004.08789/) (2020)
- [Stop Clickbait: Detecting and Preventing Clickbaits in Online News Media](https://mlsanity.com/paper/1610.09786/) (2016)
- [BNLP: Natural language processing toolkit for Bengali language](https://mlsanity.com/paper/2102.00405/) (2021)
- [A Large Multi-Target Dataset of Common Bengali Handwritten Graphemes](https://mlsanity.com/paper/2010.00170/) (2020)
- [Diving Deep into Clickbaits: Who Use Them to What Extents in Which Topics with What Effects?](https://mlsanity.com/paper/1703.09400/) (2017)
- [On Unifying Misinformation Detection](https://mlsanity.com/paper/2104.05243/) (2021)
- [The Clickbait Challenge 2017: Towards a Regression Model for Clickbait Strength](https://mlsanity.com/paper/1812.10847/) (2018)
- [Clickbait Identification using Neural Networks](https://mlsanity.com/paper/1710.08721/) (2017)

## Built on by (most cited first, 1 of 1)

- [An Interpretable Benchmark for Clickbait Detection and Tactic Attribution](https://mlsanity.com/paper/2509.10937/) (2025)

## Abstract (arXiv)

Intentionally luring readers to click on a particular content by exploiting their curiosity defines a title as clickbait. Although several studies focused on detecting clickbait titles in English articles, low resource language like Bangla has not been given adequate attention. To tackle clickbait titles in Bangla, we have constructed the first Bangla clickbait detection dataset containing 15,056 labeled news articles and 65,406 unlabelled news articles extracted from clickbait dense news sites. Each article has been labeled by three expert linguists and includes an article's title, body, and other metadata. By incorporating labeled and unlabelled data, we finetune a pretrained Bangla transformer model in an adversarial fashion using Semi Supervised Generative Adversarial Networks (SS GANs). The proposed model acts as a good baseline for this dataset, outperforming traditional neural network models (LSTM, GRU, CNN) and linguistic feature based models. We expect that this dataset and the detailed analysis and comparison of these clickbait detection models will provide a fundamental basis for future research into detecting clickbait titles in Bengali articles. We have released the corresponding code and dataset.