Researchers propose ClickGuard, an AI tool that detects clickbait and spoils articles in real time
Browser extension uses a hybrid ML model to flag clickbait with 91% F1-score and provides one- to two-sentence summaries to preempt misleading content.
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- Researchers developed ClickGuard, an AI-driven browser extension that identifies clickbait news using a hybrid ML architecture.
- The tool combines transformer embeddings, linguistically motivated features, and a custom 'baitness' score to achieve 91% F1-score on an open dataset.
- ClickGuard warns users before and after accessing a link and provides a one- to two-sentence spoiler summarizing the article.
- The extension explains predictions using analyzed metrics and is demonstrated in a public video.
Researchers from Poland propose ClickGuard, an AI-driven browser extension designed to identify and mitigate clickbait news articles. The system moves beyond traditional detection by combining transformer-based embeddings, linguistically motivated features, and a custom 'baitness' score within a hybrid machine learning architecture.
The team evaluated multiple natural language processing techniques, including classic vectorizers and large language model embeddings, before selecting an XGBoost-based model. On an open combined dataset, this model achieves an F1-score of 91%, indicating strong performance in distinguishing clickbait from non-clickbait content.
ClickGuard operates both before and after users access a link. If a user navigates to a page, the extension provides a percentage score reflecting the likelihood that the article is clickbait. Predictions are explained using the analyzed metrics, including those developed specifically for this system.
In addition to detection, the tool offers a 'clickbait spoiler'—a concise one- to two-sentence summary of the article’s content. This feature aims to preempt misleading headlines by giving users immediate context about what the article actually contains.
The researchers provide a demonstration of the tool in a public video, illustrating its real-time functionality and user interface. The work is documented in a paper submitted to arXiv on May 18, 2026.
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