Mental Illness Classification on Social Media Texts Using Deep Learning and Transfer Learning

Abstract

Given the current social distance restrictions across the world, most individuals now use social media as their major medium of communication. Due to this, millions of people suffering from mental diseases have been isolated, and they are unable to get help in person. They have become more reliant on online venues to express themselves and seek advice on dealing with their mental disorders. According to the World Health Organization (WHO), approximately 450 million people are affected. Mental illnesses, such as depression, anxiety, etc., are immensely common and have affected an individual’s physical health. Recently, Artificial Intelligence (AI) methods have been presented to help mental health providers, including psychiatrists and psychologists, in decision-making based on patients’ authentic information (e.g., medical records, behavioral data, social media utilization, etc.). AI innovations have demonstrated predominant execution in numerous real-world applications, broadening from computer vision to healthcare. This study analyzed unstructured user data on the Reddit platform and classified five common mental illnesses: depression, anxiety, bipolar disorder, ADHD, and PTSD. In this paper, we proposed a Transformer model with late fusion methods to combine the two texts (title and post) of the dataset into the model to detect the mental disorders of individuals. We compared the proposed models with traditional machine learning, deep learning, and transfer learning multi-class models. Our proposed Transformer model with the late fusion method outperformed (F1 score = $89.65$) the state-of-the-art performance (F1 score = $89.00$~\cite{murarka2021classification}). This effort will benefit the public health system by automating the detection process and informing the appropriate authorities about people who need emergency assistance.