Online fake news profoundly distorts public judgment and erodes trust in social platforms. While existing detectors achieve competitive performance on benchmark datasets, they remain notably vulnerable to malicious comments designed specifically to induce misclassification. This evolving threat landscape necessitates detection systems that simultaneously prioritize predictive accuracy and structural robustness. However, current detectors often fail to generalize across diverse and novel comment attack patterns. To bridge this gap, we propose AdComment, an adaptive adversarial training framework for robustness enhancement against diverse malicious comments. Based on cognitive psychology, we categorize adversarial comments into Fact Distortion, Logical Confusion, and Emotional Manipulation, and leverage LLMs to synthesize diverse, category-specific perturbations. Central to our framework is an InfoDirichlet Resampling (IDR) mechanism that dynamically adjusts malicious comment proportions during training, thereby steering optimization toward the model's most susceptible regions. Experimental results demonstrate that our approach achieves state-of-the-art performance on three benchmark datasets, improving the F1 scores by 17.9%, 14.5% and 9.0%, respectively.

翻译：网络虚假新闻严重扭曲公众判断，侵蚀对社交平台的信任。尽管现有检测器在基准数据集上取得了具有竞争力的性能，但其在面对专门设计用于诱导错误分类的恶意评论时仍表现出明显的脆弱性。这一不断演变的威胁态势要求检测系统必须同时兼顾预测准确性与结构鲁棒性。然而，当前检测器往往难以泛化至多样且新颖的评论攻击模式。为弥补这一差距，我们提出了AdComment，一种用于增强模型对多样化恶意评论鲁棒性的自适应对抗训练框架。基于认知心理学，我们将对抗性评论分为事实扭曲、逻辑混淆和情感操纵三类，并利用大语言模型合成多样化、类别特定的扰动。我们框架的核心是信息狄利克雷重采样机制，该机制在训练过程中动态调整恶意评论的比例，从而将优化过程导向模型最脆弱的区域。实验结果表明，我们的方法在三个基准数据集上取得了最先进的性能，分别将F1分数提高了17.9%、14.5%和9.0%。