Abstract
HIV status disclosure fields in online sex-social applications ("apps") are designed to help increase awareness, reduce stigma, and promote sexual health. Public disclosure could also help those diagnosed relate to others with similar statuses to feel less isolated. However, in our interview study (n=28) with HIV positive and negative men who have sex with men (MSM), we found some users preferred to keep their status private, especially when disclosure could stigmatise and disadvantage them, or risk revealing their status to someone they knew offline in a different context. How do users manage these tensions between health, stigma, and privacy? We analysed our interview data using signalling theory as a conceptual framework and identify participants developing 'signal appropriation' strategies, helping them manage the disclosure of their HIV status. Additionally, we propose a set of design considerations that explore the use of signals in the design of sensitive disclosure fields.
Original language | English |
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Title of host publication | Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems - CHI '19 |
Subtitle of host publication | May 4–9, 2019, Glasgow, Scotland, UK |
Place of Publication | New York |
Publisher | ACM |
Number of pages | 15 |
ISBN (Print) | 9781450359702 |
DOIs | |
Publication status | Published - 2 May 2019 |
Event | ACM CHI Conference on Human Factors in Computing Systems 2019: CHI’19 Workshop: HCI in China: Research Agenda, Education Curriculum, Industry Partnership, and Communities Building - Scottish Event Campus, Glasgow, United Kingdom Duration: 4 May 2019 → 9 May 2019 https://chi2019.acm.org/ http://chi2019.acm.org |
Conference
Conference | ACM CHI Conference on Human Factors in Computing Systems 2019 |
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Abbreviated title | CHI 2019 |
Country/Territory | United Kingdom |
City | Glasgow |
Period | 4/05/19 → 9/05/19 |
Internet address |
Keywords
- signal appropriation
- signalling theory
- online dating
- privacy unraveling
- HIV disclosure
- stigma
- stigmatized populations