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Fat liberation is a social movement advocating for equal treatment of fat people, who are currently subjected to harmful stereotypes, harassment, discrimination at school and work, and medical mistreatment, and is an understudied movement in HCI research. Due to the social and legal acceptability of anti-fatness, many physical spaces, such as businesses and healthcare providers, are unsafe or inaccessible for fat people. We conducted three in-person and virtual participatory design workshops with fat liberationist organizers and community members (N = 15) to imagine fat positive technologies. Participants designed a system to help them find size-inclusive resources, services, and healthcare providers in the offline world with design features centered around intersectionality, and participants' desire for technologies that recognized their diverse identities and characteristics. We present features and values for a fat-positive information-seeking system and synthesize present and historical HCI theories into a design framework for intersectional fat-positive HCI.
Online communities are susceptible to racist attacks, even when community policies explicitly prohibit racism. Drawing on the concept of symbolic boundary, we explored how community members sustained their communities against the perpetuation of racist logics and practices on Reddit. We drew on trace ethnography to analyze conversations about crime in two city subreddits (i.e., r/baltimore and r/chicago). The findings illustrate that the fragility of community boundaries was contributed by race baiting posts, covert racism, and racist brigading. At the same time, our research highlights that moderation efforts maintained and established institutional, cultural, and geographical boundaries to combat racist attacks. We discuss boundary as a design technique for building safe spaces for community members. Content warning: This work contains racist quotes that can upset or harm some readers.
We collected and analyzed Instagram direct messages (DMs) from 173 youth aged 13–21 (including 86 LGBTQ+ youth). We examined youth's risk-flagged social media trace data with their self-reported mental health outcomes to examine how the differing online experiences of LGBTQ+ youth compare with their heterosexual counterparts. We found that LGBTQ+ youth experienced significantly more high-risk online interactions compared to heterosexual youth. LGBTQ+ youth reported overall poorer mental health, with online harassment specifically amplifying Self-Harm and Injury. LGBTQ+ youth's mental well-being linked positively to sexual messages, unlike heterosexual youth. Qualitatively, we found that most of the risk-flagged messages of LGBTQ+ youth were sexually motivated; however, a silver lining was that they sought support for their sexual identity from peers on the platform. The study highlights the importance of tailored online safety and inclusive design for LGBTQ+ youth, with implications for CHI community advancements in fostering a supportive online environments.
Online dating technology mediates various social interactions for LGBTQ+ communities, yet how such technology shapes queerness remains understudied, particularly within queer women's communities in non-Western settings. To address this gap, we conducted a qualitative study with 17 queer women, aiming to uncover their experiences and challenges in online dating within the conservative context of South Korea. Contrary to their initial expectations of exploring open-ended forms of interaction, we found that dating applications tended to systematically normalize queerness in sexuality presentation, relationship building, and shared identities in the community. These mechanisms forced them to conform to the "normalized queerness," thereby impeding non-normative and flexible aspects of queer interactions. Building upon these findings, we discuss how the technological affordances of online dating platforms facilitate the normalization of queerness under the influence of sociocultural contexts of South Korea.
The proliferation of AI-powered search and recommendation systems has accelerated the formation of "filter bubbles" that reinforce people's biases and narrow their perspectives. Previous research has attempted to address this issue by increasing the diversity of information exposure, which is often hindered by a lack of user motivation to engage with. In this study, we took a human-centered approach to explore how Large Language Models (LLMs) could assist users in embracing more diverse perspectives. We developed a prototype featuring LLM-powered multi-agent characters that users could interact with while reading social media content. We conducted a participatory design study with 18 participants and found that multi-agent dialogues with gamification incentives could motivate users to engage with opposing viewpoints. Additionally, progressive interactions with assessment tasks could promote thoughtful consideration. Based on these findings, we provided design implications with future work outlooks for leveraging LLMs to help users burst their filter bubbles.