GNN-Based Social Media Forensics
Abstract
Social media websites have not only become the centre of human communication in the digital era, but also include such complicated issues as misinformation distribution, organised manipulation, and fraud. Proper analysis and comprehension of the inner mechanics and framework of such sites is a key to digital security and justice. In this chapter, we will examine how Graph Neural Networks (GNNs) are transforming the area of social media forensics. By modeling social networks as massive, interconnected structures—where users represent nodes and interactions form edges—GNNs learn deep, advanced representations at both the individual profile and global network levels. They are very good at detecting non-evident patterns, whether they are bad bots organised through coordinated campaigns, or the paths of distribution of harmful content, or red flags that show the usage of an account has been compromised, which is often not detected by feature-engineering-heavy methods. The methodology places less emphasis on content analysis per se, and in lieu more on a holistic, relational perspective, which includes more powerful means of provenance, authenticity, and wider methods to find traces of adversarial activities. Ultimately, GNN-based systems will welcome stronger, scalable, and granular intelligence that will allow investigators and platforms to react more to the continuously changing vigour in our online social environments.
