用可解释性分析去除虚假信息检测中的干扰因素，提升模型泛化能力。
Enhancing Disinformation Detection with Explainable AI and Named Entity Replacement
- 通过SHAP分析识别并移除影响判断的无关特征（如链接、表情符号）
- 对命名实体进行伪匿名替换后，外部测试准确率平均提升65.78%
- 适合关注虚假信息检测可解释性与鲁棒性的研究者使用
自动识别虚假信息是自然语言处理领域的重要挑战。传统文本分类方法常依赖冗余特征，难以捕捉虚假信息的细微差别。本研究采用后处理可解释性方法SHAP，识别对分类结果有高影响力的虚假元素。实验表明，移除非信息性内容（如URL、表情符号），并对命名实体（如卢旺达）进行伪匿名化处理，可有效减少模型偏差，提升泛化能力。在内部与外部数据集上对比分析发现，经扩展预处理和实体替换后，虚假信息分类模型在外部测试集上的性能平均提升65.78%，而内部测试性能未显著下降。
原文摘要 · Abstract (English)
The automatic detection of disinformation presents a significant challenge in the field of natural language processing. This task addresses a multifaceted societal and communication issue, which needs approaches that extend beyond the identification of general linguistic patterns through data-driven algorithms. In this research work, we hypothesise that text classification methods are not able to capture the nuances of disinformation and they often ground their decision in superfluous features. Hence, we apply a post-hoc explainability method (SHAP, SHapley Additive exPlanations) to identify spurious elements with high impact on the classification models. Our findings show that non-informative elements (e.g., URLs and emoticons) should be removed and named entities (e.g., Rwanda) should be pseudo-anonymized before training to avoid models' bias and increase their generalization capabilities. We evaluate this methodology with internal dataset and external dataset before and after applying extended data preprocessing and named entity replacement. The results show that our proposal enhances on average the performance of a disinformation classification method with external test data in 65.78% without a significant decrease of the internal test performance.
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