利用可解释人工智能与命名实体替换增强虚假信息检测
Enhancing Disinformation Detection with Explainable AI and Named Entity Replacement
AI总结：
本文提出利用SHAP识别并移除URL、表情符号等虚假特征，同时对命名实体进行伪匿名化，以增强虚假信息检测模型的泛化能力，在外部测试数据上平均性能提升65.78%。
摘要：
虚假信息的自动检测在自然语言处理领域构成一项重大挑战。该任务涉及多层面的社会与传播问题，需要超越仅通过数据驱动算法识别一般语言模式的方法。在本研究中，我们假设文本分类方法无法捕捉虚假信息的细微差别，并且其决策往往基于冗余特征。因此，我们应用事后可解释性方法（SHAP，SHapley Additive exPlanations）来识别对分类模型具有高影响的虚假元素。我们的研究结果表明，非信息性元素（例如URL和表情符号）应在训练前移除，命名实体（例如卢旺达）应进行伪匿名化处理，以避免模型偏差并提高其泛化能力。我们使用内部数据集和外部数据集，在应用扩展数据预处理和命名实体替换前后对该方法进行了评估。结果表明，我们的方案在外部测试数据上将虚假信息分类方法的平均性能提高了65.78%，且内部测试性能没有显著下降。
英文摘要：
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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