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This content will become publicly available on May 2, 2026

Title: Algorithmic Self-Diagnosis from Targeted Ads
People go online for information and support about sensitive topics like depression, infertility, death, or divorce. However, what happens when such topics are algorithmically recommended to them even if they are not looking for it? This article examines people's self-diagnostic behaviors based on algorithmically-recommended content, for example, wondering if they might have depression because an algorithm pushed that topic into their view. Specifically, it examines what happens when the sensitive content is not generated by users, but by companies in the form of targeted advertisements. This paper explores these questions in three parts. The first part reviews literature on self-diagnosis and targeted advertising. The second part presents a mixed-methods study of how targeted ads can enable self-diagnostic reactions. The third part reflects on the mechanisms that influence self-diagnosis and examines potential regulatory implications.  more » « less
Award ID(s):
2311102
PAR ID:
10617735
Author(s) / Creator(s):
; ;
Publisher / Repository:
Proceedings of the ACM on Human-Computer Interaction
Date Published:
Journal Name:
Proceedings of the ACM on Human-Computer Interaction
Volume:
9
Issue:
2
ISSN:
2573-0142
Page Range / eLocation ID:
1 to 19
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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