Click it or Leave it: Detecting and Spoiling Clickbait with Informativeness Measures and Large Language Models

• URL: http://arxiv.org/abs/2602.18171v1
• Date: Fri, 20 Feb 2026 12:16:08 GMT
• Title: Click it or Leave it: Detecting and Spoiling Clickbait with Informativeness Measures and Large Language Models
• Abstract summary: We present a hybrid approach to clickbait detection that combines transformer-based text embeddings with linguistically motivated informativeness features.<n>Our best-performing model, XGBoost over embeddings augmented with 15 explicit features, achieves an F1-score of 91%.<n>The proposed feature set enhances interpretability by highlighting salient linguistic cues such as second-person pronouns, superlatives, numerals, and attention-oriented punctuation.
• Score: 1.5658704610960574
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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