Clickbait detection: quick inference with maximum impact
This work addresses clickbait detection for online content platforms, but it is incremental as it builds on existing methods with efficiency improvements.
The paper tackled clickbait detection by proposing a lightweight hybrid approach combining semantic embeddings and heuristic features, achieving competitive performance with substantially reduced inference time, as indicated by high ROC-AUC values.
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.