Detecting mental disorder on social media: a ChatGPT-augmented
  explainable approach
Loris Belcastro Riccardo Cantini Fabrizio Marozzo Domenico Talia Paolo Trunfio
In the digital era, the prevalence of depressive symptoms expressed on social
media has raised serious concerns, necessitating advanced methodologies for
timely detection. This paper addresses the challenge of interpretable
depression detection by proposing a novel methodology that effectively combines
Large Language Models (LLMs) with eXplainable Artificial Intelligence (XAI) and
conversational agents like ChatGPT. In our methodology, explanations are
achieved by integrating BERTweet, a Twitter-specific variant of BERT, into a
novel self-explanatory model, namely BERT-XDD, capable of providing both
classification and explanations via masked attention. The interpretability is
further enhanced using ChatGPT to transform technical explanations into
human-readable commentaries. By introducing an effective and modular approach
for interpretable depression detection, our methodology can contribute to the
development of socially responsible digital platforms, fostering early
intervention and support for mental health challenges under the guidance of
qualified healthcare professionals.