Nowadays, with technology having become an integral factor of our lives, new challenges are catered to by the vast science of big data, which holds great promise in discovering heterogeneities and patterns. With advancements in digitalization, communication has become instant and highly accessible. However, social networks are turning vulnerable as personal and sensitive information is accessible online due to a lack of security measures—communication systems can easily be breached by cybercriminals which is tailored by social engineering. This extensive scalability introduces complexities in statistical and computational analysis; thus, there is a need to study these trends in high dimensionality and real time. This paper proposes a significant method of tracking and extracting valuable information and eliminating noise by facilitating interpretation through graphical neural networks (GNN). We relate the proposed framework to provide an in-depth survey to mitigate the risks associated with the critical challenge of social engineering.