Year: 2025
Type: article
Abstract: 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.
DOI: 10.1145/3710926
Authors: Sarita Schoenebeck, Cami Goray, Yasemin Gunal
Institution: University of Michigan
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
Funder: National Science Foundation
OpenAlex ID: W4410538902