提出可解释的假新闻标题检测与手法归因框架，提升AI判断透明度。
An Interpretable Benchmark for Clickbait Detection and Tactic Attribution
- 构建基于真实新闻生成的合成数据集，系统化注入点击诱饵策略。
- 双阶段模型：先检测是否为点击诱饵，再识别具体语言操纵手法。
- 对比BERT与大模型在零样本/少样本下的表现，支持可解释分析。
点击诱饵标题泛滥严重威胁数字媒体信息可信度与用户信任。尽管机器学习在识别操纵性内容方面取得进展，但缺乏可解释性限制了实际应用。本文提出一种可解释的点击诱饵检测模型，不仅能识别点击诱饵标题，还能归因其使用的具体语言操纵策略。我们构建了一个合成数据集，通过预定义的点击诱饵策略系统性地增强真实新闻标题，支持可控实验与模型行为的深入分析。提出两阶段自动分析框架：第一阶段比较微调后的BERT分类器与大语言模型（GPT-4.0和Gemini 2.4 Flash）在零样本提示与少样本提示下的表现，后者包含示例标题及其关联的说服性策略；第二阶段采用专用BERT分类器预测每个标题中出现的具体点击诱饵策略。该研究推动了对抗操纵性媒体内容的透明、可信AI系统发展。数据集已开源，地址为https://github.com/LLM-HITCS25S/ClickbaitTacticsDetection。
原文摘要 · Abstract (English)
The proliferation of clickbait headlines poses significant challenges to the credibility of information and user trust in digital media. While recent advances in machine learning have improved the detection of manipulative content, the lack of explainability limits their practical adoption. This paper presents a model for explainable clickbait detection that not only identifies clickbait titles but also attributes them to specific linguistic manipulation strategies. We introduce a synthetic dataset generated by systematically augmenting real news headlines using a predefined catalogue of clickbait strategies. This dataset enables controlled experimentation and detailed analysis of model behaviour. We present a two-stage framework for automatic clickbait analysis comprising detection and tactic attribution. In the first stage, we compare a fine-tuned BERT classifier with large language models (LLMs), specifically GPT-4.0 and Gemini 2.4 Flash, under both zero-shot prompting and few-shot prompting enriched with illustrative clickbait headlines and their associated persuasive tactics. In the second stage, a dedicated BERT-based classifier predicts the specific clickbait strategies present in each headline. This work advances the development of transparent and trustworthy AI systems for combating manipulative media content. We share the dataset with the research community at https://github.com/LLM-HITCS25S/ClickbaitTacticsDetection
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。