Challenges in Detection of Misinformation in Social Networks using Machine Learning

Social networking has become a popular platform for sharing information, collaboration, and communication. However, a number of difficulties emerge when social networks expand, such as concerns about scalability, privacy, data security, misinformation, and content moderation. By using enormous volumes of data to produce insights and automate decision-making processes, machine learning (ML) has become a potent tool to address these issues. Although ML attempts to provide answers to these problems, it also presents issues, including algorithmic biases, real-time data processing, large-scale implementation, interpretability of models, and adversarial manipulation of algorithms. This survey investigates the primary concerns surrounding social networking and offers a study of various challenges and a literature evaluation of relevant research. We present a novel taxonomy of misinformation detection concerns and propose solutions for improving model robustness and interpretability.