Algorithmic Recommendation Feedback and Mental Health Self-Diagnosis: A Theoretical Model Inspired by the Concept of the Looping Effect
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Keywords

recommendation feedback
looping effect
self-diagnosis

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How to Cite

Podolak, A., Zarotyńska, A. and Sito, R. (2026) “Algorithmic Recommendation Feedback and Mental Health Self-Diagnosis: A Theoretical Model Inspired by the Concept of the Looping Effect”, Scientific Journal of Bielsko-Biala School of Finance and Law. Bielsko-Biała, PL, 30(2). doi: 10.19192/wsfip.sj2.2026.15.

Abstract

This article presents a model describing the role of recommendation systems in shaping diagnostic self-identification among young adults. Its starting point is Ian Hacking’s concept of the looping effect: classifications of people influence self-understanding, ways of reporting experience and action, while the responses of classified persons feed back into expert knowledge, institutions and the social meanings of categories. The study is based on a targeted narrative review of psychological, sociological, clinical and media studies literature completed on 8 August 2026. Research documents feed personalisation, uneven quality of mental health content and associations between exposure to such material and users’ beliefs. The available empirical evidence does not yet establish a single causal chain leading from exposure to self-diagnosis, identity change or symptom intensification. The model therefore distinguishes a well-observable short recommendation feedback loop from a hypothesised long loop involving institutional knowledge and practice. The first mechanism follows from the architecture of personalisation; the second remains a research question. Depending on the context, a diagnostic label may organise experience and facilitate help-seeking, or it may become excessively central to self-concept. The article discusses implications for clinical practice and proposes longitudinal studies linking exposure history, changes in self-description and institutional responses.

https://doi.org/10.19192/wsfip.sj2.2026.15
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References

Alexander, E.D., Chung, V.-H.-A., Yacovelli, A., Sarmiento, I. and Andersson, N. (2024). Social media and postsecondary student adoption of mental health labels: Protocol for a scoping review. BMJ Open, 14(2), e078193.

Altmann, B., Fleischer, K., Tse, J. and Haslam, N. (2024). Effects of diagnostic labels on perceptions of marginal cases of mental ill-health. PLOS Mental Health, 1(3), e0000096.

American Psychiatric Association (2022). Diagnostic and statistical manual of mental disorderpp. 5th ed., text rev. Washington, DC: American Psychiatric Association Publishing.

Bhandari, A. and Bimo, S. (2022). Why's everyone on TikTok now? The algorithmized self and the future of self-making on social media. Social Media + Society, 8(1), 20563051221086241.

Boeker, M. and Urman, A. (2022). An empirical investigation of personalization factors on TikTok. In: Proceedings of the ACM Web Conference 2022. New York: Association for Computing Machinery, pp. 2298–2309.

Carter, A., Gracey, F., Moody, J., Ovens, A. and Chatburn, E. (2026). Quality, reliability and misinformation in mental health and neurodivergence content on social media: A systematic review. Journal of Social Media Research, 3(1), pp. 30–47.

Cruwys, T. and Gunaseelan, S. (2016). ‘Depression is who I am’: Mental illness identity, stigma and wellbeing. Journal of Affective Disorders, 189, pp. 36–42.

Eddington, K.M. and Badillo-Winard, E. (2025). Mental illness identity: A scoping review. Identity, 25(3), pp. 412–427.

Foster, A. and Ellis, N. (2024). TikTok-inspired self-diagnosis and its implications for educational psychology practice. Educational Psychology in Practice, 40(4), pp. 491–508.

Foulkes, L. and Andrews, J.L. (2023). Are mental health awareness efforts contributing to the rise in reported mental health problems? A call to test the prevalence inflation hypothesipp. New Ideas in Psychology, 69, 101010.

Hacking, I. (1996). The looping effects of human kindpp. In: D. Sperber, D. Premack and A.J. Premack, ed. Causal cognition: A multidisciplinary debate. Oxford: Clarendon Press, pp. 351–394.

Hacking, I. (2007). Kinds of people: Moving targetpp. In: P.J. Marshall, ed. Proceedings of the British Academy, Volume 151, 2006 Lecturepp. London: British Academy, pp. 285–318.

Haslam, N. (2016). Concept creep: Psychology's expanding concepts of harm and pathology. Psychological Inquiry, 27(1), pp. 1–17.

Jutel, A. (2009). Sociology of diagnosis: A preliminary review. Sociology of Health & Illness, 31(2), pp. 278–299.

Kapp, S.K., Gillespie-Lynch, K., Sherman, L.E. and Hutman, T. (2013). Deficit, difference, or both? Autism and neurodiversity. Developmental Psychology, 49(1), pp. 59–71.

Karasavva, V., Miller, C., Groves, N., Montiel, A., Canu, W. and Mikami, A. (2025). A double-edged hashtag: Evaluation of #ADHD-related TikTok content and its associations with perceptions of ADHD. PLOS ONE, 20(3), e0319335.

Klug, D., Qin, Y., Evans, M. and Kaufman, G. (2021). Trick and please: A mixed-method study on user assumptions about the TikTok algorithm. In: Proceedings of the 13th ACM Web Science Conference 2021. New York: Association for Computing Machinery, pp. 84–92.

Kvaale, E.P., Haslam, N. and Gottdiener, W.H. (2013). The ‘side effects’ of medicalization: A meta-analytic review of how biogenetic explanations affect stigma. Clinical Psychology Review, 33(6), pp. 782–794.

Milton, A., Ajmani, L., DeVito, M.A. and Chancellor, S. (2023). ‘I see me here’: Mental health content, community, and algorithmic curation on TikTok. In: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systempp. New York: Association for Computing Machinery, pp. 1–17.

O'Connor, C., Kadianaki, I., Maunder, K. and McNicholas, F. (2018). How does psychiatric diagnosis affect young people's self-concept and social identity? A systematic review and synthesis of the qualitative literature. Social Science & Medicine, 212, pp. 94–119.

Rutter, L.A., Howard, J., Lakhan, P., Valdez, D., Bollen, J. and Lorenzo-Luaces, L. (2023). ‘I haven't been diagnosed, but I should be’: Insight into self-diagnoses of common mental health disorders: Cross-sectional study. JMIR Formative Research, 7, e39206.

Sims, R., Michaleff, Z.A., Glasziou, P. and Thomas, R. (2021). Consequences of a diagnostic label: A systematic scoping review and thematic framework. Frontiers in Public Health, 9, 725877.

Starcevic, V. and Berle, D. (2013). Cyberchondria: Towards a better understanding of excessive health-related Internet use. Expert Review of Neurotherapeutics, 13(2), pp. 205–213.

Stubbe, D.E. (2025). Patient self-diagnosis: Physician engagement tools to compete with TikTok. Focus, 23(2), pp. 212–216.

Tomczak, K.K., Worhach, J., Rich, M., Swearingen Ludolph, O., Eppling, S., Sideridis, G. and Katz, T.C. (2024). Time is ticking for TikTok tics: A retrospective follow-up study in the post-COVID-19 isolation era. Brain and Behavior, 14(3), e3451.

Tsou, J.Y. (2007). Hacking on the looping effects of psychiatric classifications: What is an interactive and indifferent kind? International Studies in the Philosophy of Science, 21(3), pp. 329–344.

Underhill, R. and Foulkes, L. (2025). Self-diagnosis of mental disorders: A qualitative study of attitudes on Reddit. Qualitative Health Research, 35(7), pp. 779–792.

Van den Bergh, O., Witthöft, M., Petersen, S. and Brown, R.J. (2017). Symptoms and the body: Taking the inferential leap. Neuroscience & Biobehavioral Reviews, 74, pp. 185–203.

White, R.W. and Horvitz, E. (2009). Cyberchondria: Studies of the escalation of medical concerns in web search. ACM Transactions on Information Systems, 27(4), article 23, pp. 1–37.

Witthöft, M. and Rubin, G.J. (2013). Are media warnings about the adverse health effects of modern life self-fulfilling? An experimental study on idiopathic environmental intolerance attributed to electromagnetic fields (IEI-EMF). Journal of Psychosomatic Research, 74(3), pp. 206–212.

World Health Organization (2024). ICD-11 for mortality and morbidity statistics, release 2024-01 [online]. Available at: https://icd.who.int/browse/2024-01/mms/en [accessed 31 May 2026].https://icd.who.int/browse/2024-01/mms/en

Yanos, P.T., Roe, D. and Lysaker, P.H. (2010). The impact of illness identity on recovery from severe mental illnespp. American Journal of Psychiatric Rehabilitation, 13(2), pp. 73–93.

Yeung, A., Ng, E. and Abi-Jaoude, E. (2022). TikTok and attention-deficit/hyperactivity disorder: A cross-sectional study of social media content quality. The Canadian Journal of Psychiatry, 67(12), pp. 899–906.

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Copyright (c) 2026 Anna Podolak, Agnieszka Zarotyńska, Robert Sito

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