BanglaBait: Semi-Supervised Adversarial Approach for Clickbait Detection
  on Bangla Clickbait Dataset
Md. Motahar Mahtab Monirul Haque Mehedi Hasan Farig Sadeque
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.