Fine-Tuning Large Language Models For Depression And Anxiety Detection On Twitter

Abstract

User mental states are represented via social networking sites like Twitter, which also record the users thoughts and emotions. This transformation extends to fields like public health epidemiology, where analyzing social media has become a valuable tool for understanding mental health trends. Anxiety and depression remain the most widespread mental health issues globally, with their prevalence growing over the last decade.(Organization, ) Social media,especially Twitter,offers a unique glimpse into individuals mental states,and researchers have found that analyzing tweets can provide real-time insights into shifts in mental well-being.(Choudhury, Counts, & Horvitz, )The integration of artificial intelligence, especially natural language processing (NLP),has opened doors to processing massive amounts of unstructured text. These advancements make it feasible to create systems that monitor mental health at scale, offering a practical approach to identifying and addressing mental health concerns through digital footprints.