research-article

A Taxonomy of Ethical Tensions in Inferring Mental Health States from Social Media

Authors:

Stevie Chancellor,

Michael L. Birnbaum,

Vincent M. B. Silenzio,

Munmun De ChoudhuryAuthors Info & Claims

FAT* '19: Proceedings of the Conference on Fairness, Accountability, and Transparency

Pages 79 - 88

https://doi.org/10.1145/3287560.3287587

Published: 29 January 2019 Publication History

Abstract

Powered by machine learning techniques, social media provides an unobtrusive lens into individual behaviors, emotions, and psychological states. Recent research has successfully employed social media data to predict mental health states of individuals, ranging from the presence and severity of mental disorders like depression to the risk of suicide. These algorithmic inferences hold great potential in supporting early detection and treatment of mental disorders and in the design of interventions. At the same time, the outcomes of this research can pose great risks to individuals, such as issues of incorrect, opaque algorithmic predictions, involvement of bad or unaccountable actors, and potential biases from intentional or inadvertent misuse of insights. Amplifying these tensions, there are also divergent and sometimes inconsistent methodological gaps and under-explored ethics and privacy dimensions. This paper presents a taxonomy of these concerns and ethical challenges, drawing from existing literature, and poses questions to be resolved as this research gains traction. We identify three areas of tension: ethics committees and the gap of social media research; questions of validity, data, and machine learning; and implications of this research for key stakeholders. We conclude with calls to action to begin resolving these interdisciplinary dilemmas.

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Cited By

Kumar ASharma ADhanka SMaini S(2025)Machine Learning for Combating Mental Health StigmaTransforming Neuropsychology and Cognitive Psychology With AI and Machine Learning10.4018/979-8-3693-9341-3.ch014(333-366)Online publication date: 17-Jan-2025
https://doi.org/10.4018/979-8-3693-9341-3.ch014
Alvarado Garcia AYang TMiceli M(2025)What Knowledge Do We Produce from Social Media Data and How?Proceedings of the ACM on Human-Computer Interaction10.1145/37012169:1(1-45)Online publication date: 10-Jan-2025
https://dl.acm.org/doi/10.1145/3701216
Qorich MEl Ouazzani R(2025)Lightweight advanced deep-learning models for stress detection on social mediaEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.109720140:COnline publication date: 15-Jan-2025
https://dl.acm.org/doi/10.1016/j.engappai.2024.109720
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Index Terms

A Taxonomy of Ethical Tensions in Inferring Mental Health States from Social Media
1. Applied computing
  1. Law, social and behavioral sciences
    1. Psychology
2. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Social media

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FAT* '19: Proceedings of the Conference on Fairness, Accountability, and Transparency

January 2019

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DOI:10.1145/3287560

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