Graph neural networks based framework to analyze social media platforms for malicious user detection

Abstract

Online social media (OSM) has emerged as the most pertinent and readily available platform for individuals to effectively express their perspectives. Users connect seamlessly in an unstructured network, allowing information to flow within seconds. This interconnectedness, while enabling rapid information dissemination, also opens the door to significant challenges such as misinformation, disinformation, cyberbullying, privacy concerns, polarized opinions, and digital footprints. Users on social media are active with different intentions, which could include information sharing, social connections, shaping public opinion, or launching campaigns either for or against certain organizations with specific objectives. Depending on the users’ intentions, the content can be either malicious or non-malicious. Malicious content can induce fear, uncertainty, or financial damage, leading to societal polarization or reduced revenue for commercial organizations. Therefore, the detection of users with malicious intentions is crucial to curb the spread of harmful content in society. This paper proposes a deep learning-based framework that explores social media in three different domains: users’ profiles, the content being shared, and the analysis of users’ unstructured ego-networks. The framework is established on an inductive learning-based graph neural network for a 3D analysis of social media platforms. The proposed model can serve as a benchmark and provide a baseline for researchers. The performance of the proposed model is compared with available approaches, such as SVM and LSTM. A series of experiments demonstrates the out-performance of the proposed framework on real-world PHEME dataset. Additionally, the proposed framework may also be used as an OSINT (Open-Source Intelligence) tool, depending on the availability of customized data.