用轻量模型快速检测标题党，兼顾速度与准确率。
Clickbait detection: quick inference with maximum impact
- 结合语义嵌入与六种简洁特征，设计轻量化检测方法
- 图神经网络使推理速度显著提升，F1得分仍具竞争力
- 适合需要实时响应的新闻推荐与内容审核场景
我们提出一种轻量级混合点击诱饵检测方法，结合OpenAI语义嵌入与六种紧凑启发式特征，捕捉风格和信息线索。为提升效率，使用PCA降低嵌入维度，并通过XGBoost、GraphSAGE和GCN分类器进行评估。尽管简化特征设计导致F1分数略有下降，但图模型在显著降低推理时间的同时保持了良好性能。高ROC-AUC值表明模型在不同决策阈值下均具备强区分能力，支持对点击诱饵标题的可靠检测。
原文摘要 · Abstract (English)
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.
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