Disinformation on social media poses both societal and technical challenges. 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 investigates the impact of incorporating textual features into Graph Neural Networks (GNNs) for fake news detection. Our experiments demonstrate that contextual representations improve performance by 9.3% in Macro F1 over static ones and 33.8% over GNNs without textual features. However, noisy data augmentation degrades performance and increases instability. We expect our methodology to open avenues for further research, and all code is made publicly available.

翻译：社交媒体上的虚假信息既带来了社会挑战，也带来了技术挑战。尽管先前的研究已将文本信息整合到传播网络中，但尚未充分利用基于Transformer的语言模型的最新进展来获取高质量的上下文文本表征。本研究探讨了将文本特征融入图神经网络（GNNs）以进行虚假新闻检测的影响。我们的实验表明，与静态表征相比，上下文表征将宏平均F1分数提升了9.3%；与不含文本特征的GNNs相比，则提升了33.8%。然而，噪声数据增强会降低性能并增加不稳定性。我们期望本方法能为后续研究开辟新的途径，所有代码均已公开提供。