  With the rise of social media, the spread of fake news has become a
significant concern, potentially misleading public perceptions and impacting
social stability. Although deep learning methods like CNNs, RNNs, and
Transformer-based models like BERT have enhanced fake news detection, they
primarily focus on content, overlooking social context during news propagation.
Graph-based techniques have incorporated this social context but are limited by
the need for large labeled datasets. Addressing these challenges, this paper
introduces GAMC, an unsupervised fake news detection technique using the Graph
Autoencoder with Masking and Contrastive learning. By leveraging both the
context and content of news propagation as self-supervised signals, our method
negates the requirement for labeled datasets. We augment the original news
propagation graph, encode these with a graph encoder, and employ a graph
decoder for reconstruction. A unique composite loss function, including
reconstruction error and contrast loss, is designed. The method's contributions
are: introducing self-supervised learning to fake news detection, proposing a
graph autoencoder integrating two distinct losses, and validating our
approach's efficacy through real-world dataset experiments.
