Explainable AI for Stress and Depression Detection in the Cyberspace and Beyond
Cambria, Erik, Gulyás, Balázs, Pang, Joyce S., Marsh, Nigel V., and Subramaniam, Mythily (2024) Explainable AI for Stress and Depression Detection in the Cyberspace and Beyond. In: Lecture Notes in Computer Science (14658) pp. 108-120. From: PAKDD 2024: 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, 7-10 May 2024, Taipei, Taiwan.
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Abstract
Stress and depression have emerged as prevalent challenges in contemporary society, deeply intertwined with the complexities of modern life. This paper delves into the multifaceted nature of these phenomena, exploring their intricate relationship with various socio-cultural, technological, and environmental factors through the application of neurosymbolic AI to social media content. Through a quantitative and qualitative analysis of results, we elucidate the profound impact of technological advancements on information processing, work culture, and social dynamics, highlighting the role of digital connectivity in exacerbating stressors. Economic pressures and social isolation further compound these challenges, contributing to a pervasive sense of unease and disconnection. Environmental stressors, including climate change, add another layer of complexity, fostering existential concerns about the future. Moreover, the persistent stigma surrounding mental health perpetuates a cycle of silence and suffering, hindering access to support and resources. Addressing these issues necessitates a holistic approach, encompassing societal changes, policy interventions, and individual coping strategies.