Explainable AI for Mental Disorder Detection via Social Media: A survey and outlook

Mental health constitutes a complex and pervasive global challenge, affecting millions of lives and often leading to severe consequences. In this paper, we conduct a thorough survey to explore the intersection of data science, artificial intelligence, and mental healthcare, focusing on the recent developments of mental disorder detection through online social media (OSM). A significant portion of the population actively engages in OSM platforms, creating a vast repository of personal data that holds immense potential for mental health analytics. The paper navigates through traditional diagnostic methods, state-of-the-art data- and AI-driven research studies, and the emergence of explainable AI (XAI) models for mental healthcare. We review state-of-the-art machine learning methods, particularly those based on modern deep learning, while emphasising the need for explainability in healthcare AI models. The experimental design section provides insights into prevalent practices, including available datasets and evaluation approaches. We also identify key issues and challenges in the field and propose promising future research directions. As mental health decisions demand transparency, interpretability, and ethical considerations, this paper contributes to the ongoing discourse on advancing XAI in mental healthcare through social media. The comprehensive overview presented here aims to guide researchers, practitioners, and policymakers in developing the area of mental disorder detection.

I Wish to Have an Argument: Argumentative Reasoning in Large Language Models

We evaluate the ability of contemporary large language models (LLMs) to perform argumentative reasoning. We frame our experiments in terms of the argument mining (AM) and argument pair extraction (APE) tasks, and evaluate their ability to perform reasoning at increasing levels of abstraction in the input and output representations (e.g., arbitrary label sets, semantic graphs). We find that, although LLMs are able to match or surpass the state-of-the-art in AM and APE, their argumentative reasoning performance is very dependent on the input and output representation. We also find an "exemplar effect", where too many exemplars increasingly become detrimental for task performance, and about 4-5 being the optimal amount. Neither result extends to chain-of-thought (CoT) prompting: we find the exemplar effect to be nullified, and our results suggest that CoT allows for better performance under ill-conditioned problems. We hope that the work reported contributes to the improvement of argumentative reasoning in LLMs.

Visualising Argumentation Graphs with Graph Embeddings and t-SNE

This paper applies t-SNE, a visualisation technique familiar from Deep Neural Network research to argumentation graphs by applying it to the output of graph embeddings generated using several different methods. It shows that such a visualisation approach can work for argumentation and show interesting structural properties of argumentation graphs, opening up paths for further research in the area.