Nearly 30 million people work in the foodservice and retail industries in the U.S., representing approximately 18% of the total U.S. workforce. These service-industry workers contend with pressures from algorithmic management and other workplace technologies, yet they typically do not benefit from technologies that might help foster mutual support in the way that white-collar workers do. Recently, Starbucks, a major service-industry employer, has garnered media attention for issues with understaffing, labor law violations, and algorithm-based operations. We conducted interviews with 16 Starbucks employees about their workplace issues, interactions with technology, and communication practices. These interviews illustrate how workplace technologies worsen existing issues for service-industry workers and how challenges to worker-to-worker communication reduce their capacity to rectify these issues, especially at the cross-store level. Our participants want better communication with other workers, such as through labor unions or new information and communication technologies (ICTs), to help improve their working conditions. We discuss how HCI scholars can use action research to help design localized, worker-driven ICTs to facilitate more connectivity and collaborative practices outside of the workplace. We conclude by outlining our ongoing work studying and designing ICTs for service-industry workers.

Social media feeds are deeply personal spaces that reflect individual values and preferences. However, top-down, platform-wide content algorithms can reduce users’ sense of agency and fail to account for nuanced experiences and values. Drawing on the paradigm of interactive machine teaching (IMT), an interaction framework for non-expert algorithmic adaptation, we map out a design space for teachable social media feed experiences to empower agential, personalized feed curation. To do so, we conducted a think-aloud study (N = 24) featuring four social media platforms—Instagram, Mastodon, TikTok, and Twitter—to understand key signals users leveraged to determine the value of a post in their feed. We synthesized users’ signals into taxonomies that, when combined with user interviews, inform five design principles that extend IMT into the social media setting. We finally embodied our principles into three feed designs that we present as sensitizing concepts for teachable feed experiences moving forward.

Data work is often completed by crowdworkers, who are routinely dehumanized, disempowered, and sidelined. We turn to citizen science to reimagine data work, highlighting collaborative relationships between citizen science project managers and volunteers. Though citizen science and traditional crowd work entail similar forms of data work, such as classifying or transcribing large data sets, citizen science relies on volunteer contributions rather than paid data work. We detail the work citizen science project managers did to shape volunteer experiences: aligning science goals, minimizing barriers to participation, engaging communities, communicating with volunteers, providing training and education, rewarding contributions, and reflecting on volunteer work. These management strategies created opportunities for meaningful work by cultivating intrinsic motivation and fostering collaborative work relationships but ultimately limited participation to specific data-related tasks. We recommend management tactics and task design strategies for creating meaningful work for "invisible collar" workers, an understudied class of labor in CSCW.

When discussing difficult topics online, is it common to meaningfully engage with people from diverse perspectives? Why or why not? Could features of the online environment be redesigned to encourage civil conversation across difference? To investigate these questions, we need to explore them in a particular context. In this paper, we study discussions of gun policy on Reddit, with the overarching goal of developing insights into the potential of the internet to support understanding across difference. We use two methods: a clustering analysis of Reddit posts to contribute insights about what people discuss, and an interview study of twenty Reddit users to help us understand why certain kinds of conversation take place and others don't. We find that the discussion of gun politics falls into three groups: conservative pro-gun, liberal pro-gun, and liberal anti-gun. Each type of group has its own characteristic topics. While our subjects state that they would be willing to engage with others across the ideological divide, in practice they rarely do. Subjects are siloed into like-minded subreddits through a two-pronged effect, where they are simultaneously pushed away from opposing-view communities while actively seeking belonging in like-minded ones. Another contributing factor is Reddit's "karma" mechanism: fear of being downvoted and losing karma points and social approval of peers causes our subjects to hesitate to say anything in conflict with group norms. The pseudonymous nature of discussion on Reddit plays a complex role, with some subjects finding it freeing and others fearing reprisal from others not bound by face-to-face norms of politeness. Our subjects believe that content moderation can help ameliorate these issues; however, our findings suggest that moderators need different tools to do so effectively. We conclude by suggesting platform design changes that might increase discussion across difference.