Due to the rapid growth of user interaction on different social media platforms, publicly
available social media data has increased substantially. The sheer amount of data and level …

Natural language processing researchers have proven the ability of machine learning
approaches to detect depression-related cues from language; however, to date, these efforts …

Depression harms. However, due to a lack of mental health awareness and fear of stigma,
many patients do not actively seek diagnosis and treatment, leading to detrimental …

In our work, we present the contribution of the BLUE team in the eRisk Lab task focused on
identifying symptoms of depression in Reddit social media posts. The task consists of …

For numerous years, researchers have employed social media data to gain insights into
users' mental health. Nevertheless, the majority of investigations concentrate on …

Users of social platforms often perceive these sites as supportive spaces to post about their
mental health issues. Those conversations contain important traces about individuals' health …

Depression is a prevalent and debilitating mental health condition that requires accurate
and efficient detection for timely and effective treatment. In this study, we utilized the E-DAIC …

This paper proposes a new depression detection system based on LLMs that is both
interpretable and interactive. It not only provides a diagnosis, but also diagnostic evidence …

Sentiment and lexical analyses are widely used to detect depression or anxiety disorders. It
has been documented that there are significant differences in the language used by a …

This study investigates the use of Large Language Models (LLMs) for improved depression
detection from users social media data. Through the use of fine-tuned GPT 3.5 Turbo 1106 …