# A Novel Contrastive Learning Method for Clickbait Detection on RoCliCo: A Romanian Clickbait Corpus of News Articles

arXiv: https://arxiv.org/abs/2310.06540 · HTML: https://mlsanity.com/paper/2310.06540/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 39 references that are in the map.
- First posted on arXiv 10 Oct 2023.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [The birth of Romanian BERT](https://mlsanity.com/paper/2009.08712/) (2020)
- [SaRoCo: Detecting Satire in a Novel Romanian Corpus of News Articles](https://mlsanity.com/paper/2105.06456/) (2021)
- [Clickbait Detection using Multiple Categorization Techniques](https://mlsanity.com/paper/2003.12961/) (2020)
- [Identifying Clickbait: A Multi-Strategy Approach Using Neural Networks](https://mlsanity.com/paper/1710.01507/) (2017)
- [Clickbait Headline Detection in Indonesian News Sites using Multilingual Bidirectional Encoder Representations from Transformers (M-BERT)](https://mlsanity.com/paper/2102.01497/) (2021)
- [Clickbait in Hindi News Media : A Preliminary Study](https://mlsanity.com/paper/2012.07609/) (2020)

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

- [Click it or Leave it: Detecting and Spoiling Clickbait with Informativeness Measures and Large Language Models](https://mlsanity.com/paper/2602.18171/) (2026)
- [SaRoHead: Detecting Satire in a Multi-Domain Romanian News Headline Dataset](https://mlsanity.com/paper/2504.07612/) (2025)
- [Te Ahorr'e Un Click: A Revised Definition of Clickbait and Detection in Spanish News](https://mlsanity.com/paper/2507.09777/) (2025)

## Abstract (arXiv)

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.