Semi-Supervised Learning and Graph Neural Networks for Fake News Detection

International audienceSocial networks have become the main platforms for information dissemination. Nevertheless, due to the increasing number of users, social media platforms tend to be highly vulnerable to the propagation of disinformation-making the detection of fake news a challenging task. In this work, we focus on content-based methods for detecting fake news-casting the problem to a binary text classification one (an article corresponds to either fake news or not). The main challenge here stems from the fact that the number of labeled data is limited; very few articles can be examined and annotated as fake. To this extend, we opted for semi-supervised learning approaches. In particular, our work proposes a graph-based semi-supervised fake news detection method, based on graph neural networks. The experimental results indicate that the proposed methodology achieves better performance compared to traditional classification techniques, especially when trained on limited number of labeled articles