基于图的谣言检测的对比式词元级解释
Contrastive Token-level Explanations for Graph-based Rumour Detection
AI总结：
本文提出 CT-LRP 框架，通过词元级对比解释增强基于 GNN 的谣言检测可解释性，并在多个公开数据集上验证其能生成高保真解释。
摘要：
社交媒体的广泛使用加速了信息传播，但也助长了有害谣言的扩散，这些谣言可能扰乱经济、影响政治结果并加剧公共卫生危机，例如 COVID-19 疫情。虽然基于图神经网络（GNN）的方法在自动化谣言检测中展现出显著潜力，但它们往往缺乏透明度，使得其预测难以解释。现有的图可解释性技术在应对基于 GNN 的模型所使用的高维文本嵌入中特征维度之间的依赖关系所带来的独特挑战方面存在不足。在本文中，我们提出了对比式词元层级相关传播（CT-LRP），这是一个旨在增强基于 GNN 的谣言检测可解释性的新型框架。CT-LRP 通过提供词元级解释来扩展现有的图可解释性方法，从而提供更细粒度和更强的可解释性。我们在三个公开可用的谣言检测数据集上训练的多个 GNN 模型上评估了 CT-LRP 的有效性，结果表明它能够持续生成高保真、有意义的解释，为构建更稳健、更可信的谣言检测系统铺平了道路。
英文摘要：
The widespread use of social media has accelerated the dissemination of information, but it has also facilitated the spread of harmful rumours, which can disrupt economies, influence political outcomes, and exacerbate public health crises, such as the COVID-19 pandemic. While Graph Neural Network (GNN)-based approaches have shown significant promise in automated rumour detection, they often lack transparency, making their predictions difficult to interpret. Existing graph explainability techniques fall short in addressing the unique challenges posed by the dependencies among feature dimensions in high-dimensional text embeddings used in GNN-based models. In this paper, we introduce Contrastive Token Layerwise Relevance Propagation (CT-LRP), a novel framework designed to enhance the explainability of GNN-based rumour detection. CT-LRP extends current graph explainability methods by providing token-level explanations that offer greater granularity and interpretability. We evaluate the effectiveness of CT-LRP across multiple GNN models trained on three publicly available rumour detection datasets, demonstrating that it consistently produces high-fidelity, meaningful explanations, paving the way for more robust and trustworthy rumour detection systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
