Artificial Intelligence and Social Media for the Detection of Eating Disorders

Artificial intelligence (AI) has been increasingly recognized for its potential in mental health management, including detecting, preventing, and treating eating disorders (EDs). Linardon et al. investigated current practices and perspectives on AI in ED treatment from professionals and community participants. While their work provides valuable insights into AI's role in ED management in the treatment phase, the applications of AI at earlier stages, particularly for case detection, and perspectives of key groups involved in this early-stage implementation (e.g., health professionals and individuals with or at risk of EDs) remain underexplored. Given the large volume of multimodal data available on social media platforms, together with their widespread use and accessibility, the integration of AI and social media provides an ideal opportunity for conducting large-scale, population-based detection for EDs. Thus, in this commentary, we discuss AI's potential to leverage social media data for case detection, highlight related ethical considerations (e.g., bias and data privacy), and propose future research directions.

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Publication Details

Journal
International Journal of Eating Disorders
Published
2025-04-08
DOI
https://doi.org/10.1002/eat.24438
Citations
2
Primary Topic
Eating Disorders and Behaviors
Type
article
Field-Weighted Citation Impact
4.61

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article

Artificial Intelligence and Social Media for the Detection of Eating Disorders

Jinbo He, Feng Ji
2 citations
International Journal of Eating Disorders
Eating Disorders and Behaviors
4.61
article

Artificial Intelligence and Social Media for the Detection of Eating Disorders

Jinbo He, Feng Ji
article en
2 citations

Abstract

Artificial intelligence (AI) has been increasingly recognized for its potential in mental health management, including detecting, preventing, and treating eating disorders (EDs). Linardon et al. investigated current practices and perspectives on AI in ED treatment from professionals and community participants. While their work provides valuable insights into AI's role in ED management in the treatment phase, the applications of AI at earlier stages, particularly for case detection, and perspectives of key groups involved in this early-stage implementation (e.g., health professionals and individuals with or at risk of EDs) remain underexplored. Given the large volume of multimodal data available on social media platforms, together with their widespread use and accessibility, the integration of AI and social media provides an ideal opportunity for conducting large-scale, population-based detection for EDs. Thus, in this commentary, we discuss AI's potential to leverage social media data for case detection, highlight related ethical considerations (e.g., bias and data privacy), and propose future research directions.

International Journal of Eating DisordersVol. 58(7)
University of Toronto (CA), Chinese University of Hong Kong, Shenzhen (CN)
National Natural Science Foundation of China, Connaught Fund, Science, Technology and Innovation Commission of Shenzhen Municipality, Social Sciences and Humanities Research Council of Canada
Openalex Percentile: Top 7%
Eating Disorders and Behaviors
4.61
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Artificial Intelligence and Social Media for the Detection of Eating Disorders — Jinbo He, Feng Ji · International Journal of Eating Disorders (2025) | TGRS Research Map | TGRS