A Novel Contrastive Learning Method for Clickbait Detection on RoCliCo:
  A Romanian Clickbait Corpus of News Articles
Daria-Mihaela Broscoteanu Radu Tudor Ionescu
To increase revenue, news websites often resort to using deceptive news
titles, luring users into clicking on the title and reading the full news.
Clickbait detection is the task that aims to automatically detect this form of
false advertisement and avoid wasting the precious time of online users.
Despite the importance of the task, to the best of our knowledge, there is no
publicly available clickbait corpus for the Romanian language. To this end, we
introduce a novel Romanian Clickbait Corpus (RoCliCo) comprising 8,313 news
samples which are manually annotated with clickbait and non-clickbait labels.
Furthermore, we conduct experiments with four machine learning methods, ranging
from handcrafted models to recurrent and transformer-based neural networks, to
establish a line-up of competitive baselines. We also carry out experiments
with a weighted voting ensemble. Among the considered baselines, we propose a
novel BERT-based contrastive learning model that learns to encode news titles
and contents into a deep metric space such that titles and contents of
non-clickbait news have high cosine similarity, while titles and contents of
clickbait news have low cosine similarity. Our data set and code to reproduce
the baselines are publicly available for download at
https://github.com/dariabroscoteanu/RoCliCo.