Qualitative interview study reveals disclosure decision-making mechanisms in muted trauma survivors, highlighting the protective role of tailored algorithmic filters in online spaces.
Trauma is a common experience affecting over 70 percent of adults globally, with many survivors seeking support from online communities. Yet few studies explore the online experiences of muted groups who lack the words to name or describe their trauma. We pull from 29 in-depth interviews with muted trauma survivors who belong to online communities where trauma narratives are commonplace. Using a spinning top metaphor, we model the sociotechnical nature of the disclosure decision-making process, uncovering new affordances, such as indirect feedback and transportability in online platforms. Findings challenge prior notions of community engagement and algorithmic filter bubbles, highlighting the potential for algorithmic filters to counteract societal filters for muted groups. We conclude with design recommendations to make online spaces safer for trauma survivors.
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Randazzo et al. (2023) studied this question.
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