# Click it or Leave it: Detecting and Spoiling Clickbait with Informativeness Measures and Large Language Models

arXiv: https://arxiv.org/abs/2602.18171 · HTML: https://mlsanity.com/paper/2602.18171/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 20 Feb 2026.

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

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models](https://mlsanity.com/paper/2304.01852/) (2023)
- [Stop Clickbait: Detecting and Preventing Clickbaits in Online News Media](https://mlsanity.com/paper/1610.09786/) (2016)
- [Clickbait Spoiling via Question Answering and Passage Retrieval](https://mlsanity.com/paper/2203.10282/) (2022)
- [The Clickbait Challenge 2017: Towards a Regression Model for Clickbait Strength](https://mlsanity.com/paper/1812.10847/) (2018)
- [Multimodal Clickbait Detection by De-confounding Biases Using Causal Representation Inference](https://mlsanity.com/paper/2410.07673/) (2024)
- [Clickbait Detection using Multiple Categorization Techniques](https://mlsanity.com/paper/2003.12961/) (2020)
- [A Novel Contrastive Learning Method for Clickbait Detection on RoCliCo: A Romanian Clickbait Corpus of News Articles](https://mlsanity.com/paper/2310.06540/) (2023)

## Built on by (most cited first, 2 of 2)

- [LLM-guided headline rewriting for clickability enhancement without clickbait](https://mlsanity.com/paper/2603.22459/) (2026)
- [Clickbait detection: quick inference with maximum impact](https://mlsanity.com/paper/2604.08148/) (2026)

## Abstract (arXiv)

Clickbait headlines degrade the quality of online information and undermine user trust. We present a hybrid approach to clickbait detection that combines transformer-based text embeddings with linguistically motivated informativeness features. Using natural language processing techniques, we evaluate classical vectorizers, word embedding baselines, and large language model embeddings paired with tree-based classifiers. Our best-performing model, XGBoost over embeddings augmented with 15 explicit features, achieves an F1-score of 91%, outperforming TF-IDF, Word2Vec, GloVe, LLM prompt based classification, and feature-only baselines. The proposed feature set enhances interpretability by highlighting salient linguistic cues such as second-person pronouns, superlatives, numerals, and attention-oriented punctuation, enabling transparent and well-calibrated clickbait predictions. We release code and trained models to support reproducible research.