Research
Clickbait detection: quick inference with maximum impact
arXiv:2604.08148v1 Announce Type: new Abstract: We propose a lightweight hybrid approach to clickbait detection that combines OpenAI semantic embeddings with six compact heuristic features capturing s
arXiv:2604.08148v1 Announce Type: new Abstract: 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.
Related
- A GAN and LLM-Driven Data Augmentation Framework for Dynamic Linguistic Pattern Modeling in Chinese Sarcasm Detection
- An Empirical Analysis of Static Analysis Methods for Detection and Mitigation of Code Library Hallucinations
- Differentially Private Language Generation and Identification in the Limit
- LLM Prompt Duel Optimizer: Efficient Label-Free Prompt Optimization
Source: arXiv cs.CL | 2026-04-10