LOSS-GAT: Label Propagation and One-Class Semi-Supervised Graph Attention Network for Fake News Detection

TL;DR

LOSS-GAT is a semi-supervised graph neural network approach that effectively detects fake news using limited labeled data, outperforming traditional models by leveraging label propagation and structural augmentation.

Contribution

The paper introduces LOSS-GAT, a novel graph-based, one-class semi-supervised model that improves fake news detection with minimal labeled data, surpassing existing methods.

Findings

Achieves over 10% improvement in detection accuracy.

Outperforms binary labeled models on multiple datasets.

Effectively utilizes limited labeled fake news data.

Abstract

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.