Local information is essential for civic engagement, community belonging and well-being, and collective action. As more U.S. communities become "news deserts" without local newspapers or broadcast media, neighborhood- and municipality-level groups on platforms like Facebook, Nextdoor, and Reddit have become key nodes in local information infrastructure. This paper examines how volunteer moderators of these local online groups contribute to sustaining local information infrastructure, focusing on how they understand their groups’ informational function, the roles they assume to realize this function, and the skills they mobilize to fulfill perceived roles. Drawing on an Asynchronous Remote Community study and in-depth interviews with U.S.-based moderators, we conceptualize local volunteer moderation as situated civic labor, emphasizing the interpretive, relational, and context-contingent nature of their work. We offer design implications for platforms to support local knowledge and discretion and sustain democratic practices to strengthen the civic potential of online spaces to serve their local communities.

Through interviews with 16 social justice activists, we explore their challenges of adapting to Instagram, particularly in light of the platform's evolving algorithm. Our findings reveal that the frequent changes in these algorithms significantly impact their ability to engage effectively- and disproportionately impact visibility, especially for those with fewer resources and less algorithmic expertise. Our contributions encompass discussions on activists' challenges in adapting to platform changes, and the strategic shifts towards gaining broader visibility. We also address the expectations of being a '' good digital activist '' amidst algorithmic mediation on Instagram, emphasizing participants' need for navigating platform mediated complexities and maintaining authenticity. Finally, we suggest design implications, advocating features- for both existing platforms and alternative systems exclusively for activism that reduce activists' concerns about quantitative metrics, promote selective privacy, tie amplification to thoughtful engagement, and foster community building through contextual moderation and communication.

Recent studies show that users often interpret social media algorithms as mystical or spiritual because of their unpredictability. This invites new questions about how such perceptions affect the content that creators create and the communities they form online. In this study, 14 creators of algorithmic conspirituality content on TikTok were interviewed to explore their interpretations and creation processes influenced by the platform's For You Page algorithm. We illustrate how creators' beliefs interact with TikTok's algorithmic mediation to reinforce and shape their spiritual or relational themes. Furthermore, we show how algorithmic conspirituality content impacts viewers, highlighting its role in generating significant emotional and affective labor for creators, stemming from complex relational dynamics inherent in this content creation. We discuss implications for design to support creators aimed at recognizing the unexpected spiritual and religious experiences algorithms prompt, as well as supporting creators in effectively managing these challenges.

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.

The Human-Computer Interaction (HCI) community has consistently focused on the experiences of users moderated by social media platforms. Recently, scholars have noticed that moderation practices could perpetuate biases, resulting in the marginalization of user groups undergoing moderation. However, most studies have primarily addressed marginalization related to issues such as racism or sexism, with little attention given to the experiences of people with disabilities. In this paper, we present a study on the moderation experiences of blind users on TikTok, also known as "BlindToker," to address this gap. We conducted semi-structured interviews with 20 BlindTokers and used thematic analysis to analyze the data. Two main themes emerged: BlindTokers’ situated content moderation experiences and their reactions to content moderation. We reported on the lack of accessibility on TikTok’s platform, contributing to the moderation and marginalization of BlindTokers. Additionally, we discovered instances of harassment from trolls that prompted BlindTokers to respond with harsh language, triggering further moderation. We discussed these findings in the context of the literature on moderation, marginalization, and transformative justice, seeking solutions to address such issues.

Transparency can empower users to make informed choices about how they use an algorithmic decision-making system and judge its potential consequences. However, transparency is often conceptualized by the outcomes it is intended to bring about, not the specifics of mechanisms to achieve those outcomes. We conducted an online experiment focusing on how different ways of explaining Facebook's News Feed algorithm might affect participants' beliefs and judgments about the News Feed. We found that all explanations caused participants to become more aware of how the system works, and helped them to determine whether the system is biased and if they can control what they see. The explanations were less effective for helping participants evaluate the correctness of the system's output, and form opinions about how sensible and consistent its behavior is. We present implications for the design of transparency mechanisms in algorithmic decision-making systems based on these results.