Journal of Engineering Research

With the rapid development of social media platforms, the spreading speed and scope of false information have significantly increased, exerting a profound impact on society. This study focuses on utilizing Graph Neural Networks (GNNs) technology to identify false information in social media. A method based on the modeling of graph-structured data is proposed. By capturing the complex relationships between user interaction patterns and content features, the detection accuracy is improved. This method demonstrates superior performance when dealing with large-scale data and can effectively address issues that are difficult to solve with traditional methods. The combination of deep learning and graph analysis techniques provides a new perspective and solution for combating false information.

False Information Detection; Graph Neural Networks; Social Media Analysis; Machine Learning