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

翻译：[translated abstract in Chinese] 标题党新闻标题的广泛使用，旨在误导用户并最大化吸引力，对网络可信度构成了重大挑战。这些标题采用煽情性、误导性声明及模糊语言，凸显了有效检测对确保数字内容可信度的必要性。本文提出了ClickGuard：一种用于标题党检测的可信赖自适应融合框架。该框架利用句法-语义自适应融合模块（SSAFB），将BERT嵌入和结构特征进行动态整合。框架采用混合CNN-BiLSTM来捕获模式与依赖关系。模型在测试集上达到了96.93%的准确率，优于现有最先进方法。模型的可信度通过LIME和置换特征重要性（PFI）进行可解释性评估及扰动分析。这些方法通过测量平均预测变化量，评估模型对特征变化的鲁棒性和敏感性。消融实验验证了SSAFB在优化特征融合方面的有效性。该模型在多个数据集上展示了稳健性能，通过解决句法-语义建模难题，为提升网络内容可信度提供了可扩展、可靠的解决方案。相关代码已在GitHub开源：https://github.com/palindromeRice/ClickBait_Detection_Architecture