ClickGuard: Detecting and Spoiling Clickbait News with Informativeness Measures and Large Language Models
For Internet users, this tool offers practical clickbait detection and explanation, but the approach is incremental, combining existing techniques.
The paper presents ClickGuard, a browser extension that detects clickbait using a hybrid ML model combining transformer embeddings and linguistic features, achieving 91% F1-score on a combined dataset, and provides users with a clickbait likelihood score and a spoiler summary.
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:https://www.youtube.com/watch?v=IJ1gkQV82C4}{https://www.youtube.com/watch?v=IJ1gkQV82C4