LOSS-GAT: Label Propagation and One-Class Semi-Supervised Graph
  Attention Network for Fake News Detection
Batool Lakzaei Mostafa Haghir Chehreghani Alireza Bagheri
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.