Affiliation:
1. University of Michigan, State St., Ann Arbor, MI
Abstract
Disclosures of distress and stigma on identified social media can be beneficial. Yet, many who may benefit from such disclosures do not engage in them. I examine factors that inform decisions to not disclose stigmatized experiences on identified social media. I conducted in-depth interviews with women in the US who used social media, had experienced pregnancy loss, and had not disclosed about their loss on identified social media. I detail six types of factors related to the self, audience, network, society, platform, and temporality that contribute to non-disclosure decisions. I show that the Disclosure Decision-Making (DDM) framework introduced in prior work explaining disclosures when they do occur, also explains non-disclosure decisions on social media. I show how DDM builds from and bridges prior privacy theories, namely, Communication Privacy Management and Contextual Integrity. I discuss design implications around removing barriers to disclosure to facilitate beneficial disclosures and reduce stigma.
Funder
National Science Foundation
Publisher
Association for Computing Machinery (ACM)
Subject
Human-Computer Interaction
Cited by
42 articles.
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