This paper presents ClickGuard, 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. Additionally, the browser extension provides a clickbait spoiler—a one- to two-sentence summary of the entire article.
Demo video: ClickGuard Demo
Blogger's Review: The innovation of ClickGuard lies in its combination of traditional NLP methods with modern deep learning techniques, significantly improving clickbait detection accuracy. By providing a quantitative score and summary functionality, users can not only avoid misleading information but also quickly grasp the core content of articles, enhancing practicality. Looking forward to its broader application as the model continues to optimize.