Affiliation:
1. University of Michigan, Ann Arbor, Michigan
2. University of California, Irvine, CA
Abstract
When studying identity transitions, interview participants can find it difficult to reflect on their transitions and recall specific details related to past experiences. We present a new approach to enable participant reflection on past identity transitions, and a means to fill in blanks by eliciting data that may not otherwise come up: showing participants sentiment visualizations of their social media data. After detailing our methods of constructing sentiment visualizations, we discuss our experiences using them in a study on gender transition. For most participants, the visualizations elicited substantial reflection, and enabled recalling forgotten data and new interpretations of transition experiences. We guide researchers on how to use this method when studying other identity transitions; this may be especially powerful for marginalized people who undergo substantial identity changes. This article proposes a way to uncover participants’ personal histories, which can help HCI researchers to better understand and support marginalized people’s experiences.
Publisher
Association for Computing Machinery (ACM)
Subject
Human-Computer Interaction
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