用自适应融合提升点击诱骗内容识别准确率
ClickGuard: A Trustworthy Adaptive Fusion Framework for Clickbait Detection
- 融合语义与语法特征，动态调整信息权重
- 测试准确率达96.93%，优于现有方法
- 可解释性强，适合需要可信AI的平台
点击诱骗标题广泛存在，通过煽动性语言和误导性表述吸引点击，严重威胁网络内容可信度。本文提出ClickGuard：一种可信的自适应融合框架，结合BERT嵌入与结构特征，通过句法-语义自适应融合模块（SSAFB）实现动态信息整合，并采用混合CNN-BiLSTM捕获文本模式与依赖关系。模型在测试中达到96.93%准确率，超越现有先进方法。通过LIME与置换特征重要性（PFI）评估模型可解释性，结合扰动分析衡量预测对特征变化的敏感度。消融实验证明SSAFB在优化特征融合中的有效性。该模型在多个数据集上表现稳健，为解决句法-语义建模挑战提供了可扩展、可靠的在线内容可信度增强方案。代码已开源。
原文摘要 · Abstract (English)
The widespread use of clickbait headlines, crafted to mislead and maximize engagement, poses a significant challenge to online credibility. These headlines employ sensationalism, misleading claims, and vague language, underscoring the need for effective detection to ensure trustworthy digital content. The paper introduces, ClickGuard: a trustworthy adaptive fusion framework for clickbait detection. It combines BERT embeddings and structural features using a Syntactic-Semantic Adaptive Fusion Block (SSAFB) for dynamic integration. The framework incorporates a hybrid CNN-BiLSTM to capture patterns and dependencies. The model achieved 96.93% testing accuracy, outperforming state-of-the-art approaches. The model's trustworthiness is evaluated using LIME and Permutation Feature Importance (PFI) for interpretability and perturbation analysis. These methods assess the model's robustness and sensitivity to feature changes by measuring the average prediction variation. Ablation studies validated the SSAFB's effectiveness in optimizing feature fusion. The model demonstrated robust performance across diverse datasets, providing a scalable, reliable solution for enhancing online content credibility by addressing syntactic-semantic modelling challenges. Code of the work is available at: https://github.com/palindromeRice/ClickBait_Detection_Architecture
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