Disinformation on social media poses both societal and technical challenges, requiring robust detection systems. While previous studies have integrated textual information into propagation networks, they have yet to fully leverage the advancements in Transformer-based language models for high-quality contextual text representations. This work addresses this gap by incorporating Transformer-based textual features into Graph Neural Networks (GNNs) for fake news detection. We demonstrate that contextual text representations enhance GNN performance, achieving 33.8% relative improvement in Macro F1 over models without textual features and 9.3% over static text representations. We further investigate the impact of different feature sources and the effects of noisy data augmentation. We expect our methodology to open avenues for further research, and we made code publicly available.

翻译：社交媒体上的虚假信息带来了社会和技术层面的双重挑战，亟需构建鲁棒的检测系统。尽管先前的研究已将文本信息整合至传播网络中，但尚未充分利用基于Transformer的语言模型在高质量上下文文本表征方面的进展。本研究通过将基于Transformer的文本特征融入图神经网络（GNNs）以进行虚假新闻检测，从而弥补这一不足。我们证明，上下文文本表征能够提升GNN的性能，相较于无文本特征的模型在宏平均F1分数上实现了33.8%的相对提升，相较于静态文本表征则提升了9.3%。我们进一步探究了不同特征源的影响以及噪声数据增强的效果。我们期望本方法能为后续研究开辟新路径，相关代码已公开提供。