Detection of fake news campaigns using graph convolutional networks

ABSTRACT

The detection of organized disinformation campaigns that spread fake news, by first camouflaging them as real ones, is crucial in the battle against misinformation and disinformation in social media. This article presents a method for classifying the diffusion graphs of news formed in social media, by taking into account the profiles of the users that participate in the graph, the profiles of their social relations and the way the news spread, ignoring the actual text content of the news or the messages that spread it. This increases the robustness of the method and widens its applicability in different contexts. The results of this study show that the proposed method outperforms methods that rely on textual information only and provide a model that can be employed for detecting similar disinformation campaigns on different context in the same social medium.