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

ClickGuard：利用信息量度量与大语言模型检测并剧透点击诱饵新闻

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.

摘要： 本文介绍了一款人工智能驱动的浏览器扩展程序，旨在识别点击诱饵（Clickbait），帮助用户避开具有误导性的互联网文章。该应用超越了传统的检测方法，采用了一种混合机器学习架构，将基于 Transformer 的嵌入（Embeddings）与语言学特征及自定义的“诱饵度”（baitness）评分相结合。

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.

在评估了从经典向量化器到大语言模型（LLM）嵌入等多种自然语言处理技术后，研究人员开发出一种基于 XGBoost 的模型，在公开的综合数据集上实现了 91% 的 F1 分数。最重要的是，该工具可以在用户访问点击诱饵文章之前和之后发出警告。

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.

在打开文章后，用户会收到一个百分比分数，显示其为点击诱饵的可能性。系统会根据分析指标（包括该系统专门开发的指标）对预测结果进行解释。此外，该浏览器扩展还提供“点击诱饵剧透”功能，即对整篇文章进行一到两句话的总结。