Tommy Yuan

Yusif Ibrahimov, Tarique Anwar, Tommy Yuan

In today's interconnected society, social media platforms have become an important part of our lives, where individuals virtually express their thoughts, emotions, and moods. These expressions offer valuable insights into their mental health. This paper explores the use of platforms like Facebook, $\mathbb{X}$ (formerly Twitter), and Reddit for mental health assessments. We propose a domain knowledge-infused residual attention model called DepressionX for explainable depression severity detection. Existing deep learning models on this problem have shown considerable performance, but they often lack transparency in their decision-making processes. In healthcare, where decisions are critical, the need for explainability is crucial. In our model, we address the critical gap by focusing on the explainability of depression severity detection while aiming for a high performance accuracy. In addition to being explainable, our model consistently outperforms the state-of-the-art models by over 7% in terms of $\text{F}_1$ score on balanced as well as imbalanced datasets. Our ultimate goal is to establish a foundation for trustworthy and comprehensible analysis of mental disorders via social media.

Frederic Higham, Tommy Yuan

This study explores the effectiveness of applying AI and gamification into a presentation platform aimed at University students wanting to improve their public speaking skills in their native tongue. Specifically, a platform based on the radio show, Just a Minute (JAM), is explored. In this game, players are challenged to speak fluently on a topic for 60 seconds without repeating themselves, hesitating or deviating from the topic. JAM has proposed benefits such as allowing students to improve their spontaneous speaking skills and reduce their use of speech disfluencies ("um", "uh", etc.). Previous research has highlighted the difficulties students face when speaking publicly, the main one being anxiety. AI Powered Presentation Platforms (AI-PPPs), where students can speak with an immersive AI audience and receive real-time feedback, have been explored as a method to improve student's speaking skills and confidence. So far they have shown promising results which this study aims to build upon. A group of students from the University of York are enlisted to evaluate the effectiveness of the JAM platform. They are asked to fill in a questionnaire, play through the game twice and then complete a final questionnaire to discuss their experiences playing the game. Various statistics are gathered during their gameplay such as the number of points they gained and the number of rules they broke. The results showed that students found the game promising and believed that their speaking skills could improve if they played the game for longer. More work will need to be carried out to prove the effectiveness of the game beyond the short term.

Yusif Ibrahimov, Tarique Anwar, Tommy Yuan et al.

In today's interconnected society, social media platforms provide a window into individuals' thoughts, emotions, and mental states. This paper explores the use of platforms like Facebook, X (formerly Twitter), and Reddit for depression severity detection. We propose AttentionDep, a domain-aware attention model that drives explainable depression severity estimation by fusing contextual and domain knowledge. Posts are encoded hierarchically using unigrams and bigrams, with attention mechanisms highlighting clinically relevant tokens. Domain knowledge from a curated mental health knowledge graph is incorporated through a cross-attention mechanism, enriching the contextual features. Finally, depression severity is predicted using an ordinal regression framework that respects the clinical-relevance and natural ordering of severity levels. Our experiments demonstrate that AttentionDep outperforms state-of-the-art baselines by over 5% in graded F1 score across datasets, while providing interpretable insights into its predictions. This work advances the development of trustworthy and transparent AI systems for mental health assessment from social media.

Yusif Ibrahimov, Tarique Anwar, Tommy Yuan

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.

Lars Malmqvist, Tommy Yuan, Suresh Manandhar

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.