ClickGuard: Detecting and Spoiling Clickbait News with Informativeness Measures and Large Language Models

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[Submitted on 18 May 2026]

Title:ClickGuard: Detecting and Spoiling Clickbait News with Informativeness Measures and Large Language Models

View a PDF of the paper titled ClickGuard: Detecting and Spoiling Clickbait News with Informativeness Measures and Large Language Models, by Wojciech Michaluk and 4 other authors

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Abstract:This paper presents an AI-driven browser extension that identifies clickbait to help users avoid misleading Internet articles. Moving beyond traditional detection, the application employs a hybrid machine learning architecture that combines transformer-based embeddings with linguistically motivated features and a custom "baitness" score. After evaluating various natural language processing techniques -- from classic vectorizers to large language model (LLM) embeddings -- an XGBoost-based model was developed that achieves an F1-score of 91% on the open combined dataset. Most importantly, the tool can warn users before and after they access a clickbait article. After opening an article, the user receives a percentage score indicating the likelihood that it is clickbait. The prediction is explained based on the analyzed metrics, including those specifically developed within the proposed system. The browser extension also provides a clickbait spoiler -- a one- to two-sentence summary of the entire article. Demo video:this https URL}{this https URL

Subjects:

Artificial Intelligence (cs.AI)

Cite as: arXiv:2607.20463 [cs.AI]

(or arXiv:2607.20463v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.20463

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Soveatin Kuntur [view email] [v1] Mon, 18 May 2026 10:12:47 UTC (511 KB)

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