# What Makes You CLIC: Detection of Croatian Clickbait Headlines

arXiv: https://arxiv.org/abs/2507.14314 · HTML: https://mlsanity.com/paper/2507.14314/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 18 Jul 2025.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics](https://mlsanity.com/paper/2009.10795/) (2020)
- [Stop Clickbait: Detecting and Preventing Clickbaits in Online News Media](https://mlsanity.com/paper/1610.09786/) (2016)
- [Language Ranker: A Metric for Quantifying LLM Performance Across High and Low-Resource Languages](https://mlsanity.com/paper/2404.11553/) (2024)
- [BERTi'c -- The Transformer Language Model for Bosnian, Croatian, Montenegrin and Serbian](https://mlsanity.com/paper/2104.09243/) (2021)
- [We Built a Fake News & Click-bait Filter: What Happened Next Will Blow Your Mind!](https://mlsanity.com/paper/1803.03786/) (2018)
- [Tabloids in the Era of Social Media? Understanding the Production and Consumption of Clickbaits in Twitter](https://mlsanity.com/paper/1709.02957/) (2017)
- [TakeLab Retriever: AI-Driven Search Engine for Articles from Croatian News Outlets](https://mlsanity.com/paper/2411.19718/) (2024)

## Abstract (arXiv)

Online news outlets operate predominantly on an advertising-based revenue model, compelling journalists to create headlines that are often scandalous, intriguing, and provocative -- commonly referred to as clickbait. Automatic detection of clickbait headlines is essential for preserving information quality and reader trust in digital media and requires both contextual understanding and world knowledge. For this task, particularly in less-resourced languages, it remains unclear whether fine-tuned methods or in-context learning (ICL) yield better results. In this paper, we compile CLIC, a novel dataset for clickbait detection of Croatian news headlines spanning a 20-year period and encompassing mainstream and fringe outlets. We fine-tune the BERTi'c model on this task and compare its performance to LLM-based ICL methods with prompts both in Croatian and English. Finally, we analyze the linguistic properties of clickbait. We find that nearly half of the analyzed headlines contain clickbait, and that finetuned models deliver better results than general LLMs.