In the era of widespread social networks, the rapid dissemination of fake
news has emerged as a significant threat, inflicting detrimental consequences
across various dimensions of people's lives. Machine learning and deep learning
approaches have been extensively employed for identifying fake news. However, a
significant challenge in identifying fake news is the limited availability of
labeled news datasets. Therefore, the One-Class Learning (OCL) approach,
utilizing only a small set of labeled data from the interest class, can be a
suitable approach to address this challenge. On the other hand, representing
data as a graph enables access to diverse content and structural information,
and label propagation methods on graphs can be effective in predicting node
labels. In this paper, we adopt a graph-based model for data representation and
introduce a semi-supervised and one-class approach for fake news detection,
called LOSS-GAT. Initially, we employ a two-step label propagation algorithm,
utilizing Graph Neural Networks (GNNs) as an initial classifier to categorize
news into two groups: interest (fake) and non-interest (real). Subsequently, we
enhance the graph structure using structural augmentation techniques.
Ultimately, we predict the final labels for all unlabeled data using a GNN that
induces randomness within the local neighborhood of nodes through the
aggregation function. We evaluate our proposed method on five common datasets
and compare the results against a set of baseline models, including both OCL
and binary labeled models. The results demonstrate that LOSS-GAT achieves a
notable improvement, surpassing 10%, with the advantage of utilizing only a
limited set of labeled fake news. Noteworthy, LOSS-GAT even outperforms binary
labeled models.

本研究采用基于图的模型的半监督和单类别方法 LOSS-GAT，利用标签传播算法检测假新闻，并在实验证明其优于传统的二分类模型。