Social media platforms are increasingly adopting features that display crowdsourced context alongside posts, a technique pioneered by X’s Community Notes. These systems—which we term Crowdsourced Context Systems (CCS)—have the potential to reshape the information ecosystem as major platforms embrace them as alternatives to professional fact-checking. To understand the features and implications of these systems, we conduct a systematic literature review of existing CCS research (n=56) and analyze real-world CCS implementations. Based on our analysis, we develop a framework with two components. First, we present a theoretical model to conceptualize and define CCS. Second, we identify a design space encompassing six aspects: participation, inputs, curation, presentation, platform treatment, and transparency. We also surface normative implications of different CCS design and implementation choices. Our work integrates theoretical, design, and ethical perspectives to establish a foundation for future human-centered research on Crowdsourced Context Systems.

How are Reddit communities responding to AI-generated content?We explored this question through a large-scale analysis of subreddit community rules and their change over time.We collected the metadata and community rules for over 300, 000 public subreddits and measured the prevalence of rules governing AI.We labeled subreddits and AI rules according to existing taxonomies from the HCI literature and a new taxonomy we developed specifc to AI rules.While rules about AI are still relatively uncommon, the number of subreddits with these rules more than doubled over the course of a year.AI rules are more common in larger subreddits and communities focused on art or celebrity topics, and less common in those focused on social support.These rules often focus on AI images and evoke, as justifcation, concerns about quality and authenticity.Overall, our fndings illustrate the emergence of varied concerns about AI, in diferent community contexts.Platform designers and HCI researchers should heed these concerns if they hope to encourage community self-determination in the age of generative AI.We make our datasets public to enable future large-scale studies of community self-governance.

Generative AI is altering how we work, learn, communicate, and participate in online communities. How might online communities be changed by generative AI? To start addressing this question, we focused on online community moderators' experiences with AI-generated content (AIGC). We performed fifteen in-depth, semi-structured interviews with moderators of Reddit communities that restrict the use of AIGC. Our study finds that rules about AIGC are motivated by concerns about content quality, social dynamics, and governance challenges. Moderators fear that, without such rules, AIGC threatens to reduce their communities' utility and social value. We find that, despite the absence of robust tools for detecting AIGC, moderators were able to somewhat limit the disruption it caused by working with their communities to clarify norms. However, moderators found enforcing AIGC restrictions challenging, as they rely on time-intensive and inaccurate detection heuristics. Our results highlight the importance of supporting community autonomy and self-determination in the face of this sudden technological change, and suggest potential design solutions that may help.

This paper examines the tensions between neighborhood gentrification and community surveillance posts on Nextdoor, a hyperlocal social media platform for neighborhoods. We created a privacy-preserving pipeline to gather research data from public Nextdoor posts in Atlanta, Georgia and filtered these to a dataset of 1,537 community surveillance posts. We developed a qualitative codebook to label observed patterns of community surveillance, and deploy a large language model to tag these posts at scale. Ultimately, we present an extensible and empirically-tested typology of the modes of community surveillance that occur on hyperlocal platforms. We find a complex relationship between community surveillance posts and neighborhood gentrification, which indicates that publicly disclosing information about perceived outsiders, especially for petty crimes, is most prevalent in gentrifying neighborhoods. Our empirical evidence inform critical perspectives which posit that community surveillance on platforms like Nextdoor can exclude and marginalize minoritized populations, particularly in gentrifying neighborhoods. Our findings carry broader implications for hyperlocal social platforms and their potential to amplify and exacerbate social tensions and exclusion.