用语言特征+大模型检测标题党，准确率达91%
Click it or Leave it: Detecting and Spoiling Clickbait with Informativeness Measures and Large Language Models
- 结合语言学特征与大模型嵌入，提升点击诱饵识别能力
- 最佳模型F1达91%，显著优于传统方法
- 突出第二人称、夸张词等提示词，结果可解释
点击诱饵类标题损害在线信息质量并削弱用户信任。本文提出一种混合检测方法，将基于Transformer的文本嵌入与语言学启发的资讯性特征相结合。通过自然语言处理技术，评估了经典向量化方法、词嵌入基线以及大语言模型嵌入，并搭配树模型分类器进行比较。最优模型为基于XGBoost的嵌入增强模型，融合15个显式特征，取得91%的F1分数，优于TF-IDF、Word2Vec、GloVe、基于LLM提示的分类及仅使用特征的基线。所提特征集通过突出第二人称代词、最高级形容词、数字和引导注意力的标点符号，提升了模型可解释性，实现透明且校准良好的点击诱饵预测。代码与训练模型已开源，支持可复现研究。
原文摘要 · Abstract (English)
Clickbait headlines degrade the quality of online information and undermine user trust. We present a hybrid approach to clickbait detection that combines transformer-based text embeddings with linguistically motivated informativeness features. Using natural language processing techniques, we evaluate classical vectorizers, word embedding baselines, and large language model embeddings paired with tree-based classifiers. Our best-performing model, XGBoost over embeddings augmented with 15 explicit features, achieves an F1-score of 91\%, outperforming TF-IDF, Word2Vec, GloVe, LLM prompt based classification, and feature-only baselines. The proposed feature set enhances interpretability by highlighting salient linguistic cues such as second-person pronouns, superlatives, numerals, and attention-oriented punctuation, enabling transparent and well-calibrated clickbait predictions. We release code and trained models to support reproducible research.
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