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.

通过构建达到 15,056 个标记的新闻文章和 65,406 个未标记的新闻文章的第一个孟加拉 clickbait 检测数据集，应用 SS GANs 对 Semi Supervised Generative Adversarial Networks 进行微调，我们提出的新模型作为该数据集的一个良好基准，优于传统神经网络模型（LSTM、GRU、CNN）和基于语言特征的模型，这一研究将为未来检测孟加拉文 clickbait 标题提供一个基础。