Safety and Harm in Social Computing Systems that support Matching Markets
Abstract
Social computing systems that support matching markets, like online dating and gig work platforms, provide numerous benefits to users. However, these systems also present safety-related risks. Prior research has considered individual aspects of safety in these systems (e.g. scams, physical violence) across specific user groups. However, there is a gap in understanding how platform affordances (or lack thereof), impact how users experience harm and the protective safety behaviors they engage in to try to mitigate harm.
In this dissertation I investigate how platforms influence safety in gig work and online dating, focusing on three characteristics shared by one or both of these platforms: a financial motive for the interaction, uneven power dynamics between interacting parties, and a non-trivial offline component of the interaction. I begin by studying how these characteristics impact safety in four types of gig work. Then, I broaden my work to systematize the harms and protective behaviors across online daters and gig workers, bringing together two seemingly disparate groups that actually share many safety-related vulnerabilities and protective strategies. My work suggests that in addition to causing and facilitating harm, matching market platforms also limit the protective safety behaviors users can engage in.