Social eRisk Prediction Using Natural Language Processing and Machine Learning

This research analyzes textual content shared on social media platforms. The freedom of social networking usage invites many social electronic risks (SeR) in current times, including cyberbullying, online harassment, misinformation spread, privacy breaches, and depression. This study mainly focused on detecting depression signs using social media data. In today’s world, depression is not a small problem. It’s not just an issue, as it seriously impacts a person’s emotions and thinking abilities. With the increasing prevalence of mental health concerns, particularly depression, online platforms serve as valuable sources of user-generated data. Depression, a prevalent mental health disorder, poses significant challenges for timely identification. Leveraging diverse datasets, this study employs many machine learning and natural language processing (NLP) techniques to develop predictive models. The research evaluates the efficacy of each model in accurately classifying individuals with depression based on various features. Comparative analyses highlight the strengths and limitations of each algorithm, shedding light on their respective performances. The results underscore the potential of machine learning in advancing depression detection methods, with implications for early intervention and personalized mental health care. This investigation contributes to the growing field of digital mental health by providing insights into the optimal use of diverse machine learning models and NLP techniques for depression detection.