用AI识别骗点击文章，打开前预警、打开后揭秘内容
ClickGuard: Detecting and Spoiling Clickbait News with Informativeness Measures and Large Language Models
- 结合语言特征与大模型嵌入，计算文章‘诱饵度’得分
- 在公开数据集上达91%的点击诱饵检测准确率
- 提供点击诱饵预警和一句话内容剧透，适合信息焦虑者
本文提出一款基于AI的浏览器插件，用于识别网络上的点击诱饵类新闻，帮助用户避免误导性文章。该系统采用混合机器学习架构，融合基于Transformer的文本嵌入与语言学特征，并设计了自定义的“诱饵度”评分机制。在对比多种自然语言处理技术（从传统向量化器到大语言模型嵌入）后，最终构建的基于XGBoost的模型在公开合并数据集上实现了91%的F1分数。该工具可在用户访问文章前和访问后双重预警：打开后显示文章为点击诱饵的可能性百分比，并基于分析指标解释预测结果，包括系统自研的多个评估维度。此外，插件还提供“点击诱饵剧透”功能——一段1至2句话的全文摘要。演示视频：https://www.youtube.com/watch?v=IJ1gkQV82C4
原文摘要 · Abstract (English)
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
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