content platform designers

Abstract: YouTube has many features, such as homepage recommendations, that encourage users to explore its vast library of videos. However, when users visit YouTube with a specific intention, e.g., learning how to program in Python, these features to encourage exploration are often distracting. Prior work has innovated 'commitment interfaces' that restrict social media but finds that they often indiscriminately block needed content. In this paper, we describe the design, development, and evaluation of an 'adaptable commitment interface,' the SwitchTube mobile app, in which users can toggle between two interfaces when watching YouTube videos: Focus Mode (search-first) and Explore Mode (recommendations-first). In a three-week field deployment with 46 US participants, we evaluate how the ability to switch between interfaces affects user experience, finding that it provides users with a greater sense of agency, satisfaction, and goal alignment. We conclude with design implications for how adaptable commitment interfaces can support digital wellbeing.

Abstract: AI slop has flooded social media feeds with overprocessed and derivative content--Jesus fusing with a shrimp, a humanoid orange chubby cat shoplifting, and a "hero" dog rescuing a human child after a plane crash. This might be no laughing matter, as waves of media coverage have taken a distinctly negative bent, emphasizing its low quality, ethical concerns, and threats to human creativity. Despite extensive attention from politicians and technologists, the perspectives of everyday users--the very audience most exposed to this phenomenon--remain largely unexplored. This descriptive work explored how Reddit users experience, conceptualize and contest AI slop. Inductive thematic analysis of posts and comments across six slop-related subreddits identified four themes covering constitutional defects, algorithmic overreach, user resistance, and entertainment outcomes--ranging from anxieties about eroding agency, moral norms, creativity, and existential questions to defensive distinctions between quality AI content and slop. We derive a user-driven definition: AI slop is audience-contested AI-generated content characterized by constitutional defects and enabled by platform logics. We operationalize slop through a facet-theoretical framework to inform future research into this fuzzy category.

Abstract: Digital platforms provide increasingly detailed traces of collaborative activity, yet these traces do not directly reveal whether contributions remain largely independent or whether more structured forms of collective work emerge through interaction. This paper addresses the methodological problem of measuring such organization when the collective being studied is not defined as a team in advance. We propose a trace-based method for measuring teamness, understood as the degree to which collaborative activity expresses properties associated with teamwork. The method translates established dimensions of teamwork into behavioral indicators that can be recovered from digital traces. These indicators can be examined separately and summarized through a composite measure of their joint expression. It does not require predefined team membership and is designed to be adapted across collaborative settings. We illustrate the method using revision histories from 52,437 articles in the English Wikipedia's Medicine WikiProject, comprising more than 10 million human-authored edits. Using article-months as observation units, the application shows that different dimensions of teamlike organization display distinct patterns and that their joint expression can vary within the same articles over time. The contribution is a general measurement framework for examining graded and dynamic forms of collective organization directly from behavioral traces.