We propose a lightweight hybrid approach to clickbait detection that combines OpenAI semantic embeddings with six compact heuristic features capturing stylistic and informational cues. To improve efficiency, embeddings are reduced using PCA and evaluated with XGBoost, GraphSAGE, and GCN classifiers. While the simplified feature design yields slightly lower F1-scores, graph-based models achieve competitive performance with substantially reduced inference time. High ROC--AUC values further indicate strong discrimination capability, supporting reliable detection of clickbait headlines under varying decision thresholds.
@article{arxiv.2604.08148,
title = {Clickbait detection: quick inference with maximum impact},
author = {Soveatin Kuntur and Panggih Kusuma Ningrum and Anna Wróblewska and Maria Ganzha and Marcin Paprzycki},
journal= {arXiv preprint arXiv:2604.08148},
year = {2026}
}