Voice-based social media platforms that enable attendees to have real-time, ephemeral interactions with each other—such as X-Spaces, Discord, and Clubhouse—have seen considerable growth in recent years. While prior research on these spaces has predominantly focused on moderating harms, our work seeks to understand emergent practices employed by hosts to proactively shape their discussion space— focusing on the facilitation aspect of moderation duties. Drawing on facilitation strategies, we study these practices through three comprehensive studies using mixed-methods: survey of social-audio users, co-design interviews, and analyzing training sessions for hosts. Our findings reveal insights into the issues faced by hosts and attendees, current facilitation practices, opinions on technological solutions, and factors that could be responsible for some of the identified issues such as the available training for hosts. We found that hosts themselves are often significant sources of issues due to practices such as focusing more on self-promotion than facilitating discussions. In addition, host training sessions seem to encourage behaviors that contribute to the negative perception of hosts. We draw on outcomes from co-design interviews to guide the design of future tools to support hosts in facilitating social-audio spaces. Our findings provide insights that could help create a more positive experience for both hosts and attendees.

Recent years have seen a rise in social virtual reality (VR) platforms that allow people to interact in real-time through voice and gestures. The ephemeral nature of communication on these platforms can enable new forms of harmful behavior and new challenges for moderators. We performed virtual field research on three VR environments (AltspaceVR, Horizon Worlds, Rec Room). Based on observing 100 scheduled events, our analysis uncovered 13 distinct types of potentially harmful behaviors enabled by real-time voice, embodied interactions, and platform affordances. We witnessed potential harm at 45% of our observed events; only 24% of these incidents were addressed by moderators. To understand moderation practices, we conducted interviews with 11 moderators to investigate how they assess real-time interactions and how they operate within the current state of moderation tools. Our work sheds light on how moderation tools and practices must evolve to meet the new challenges of social VR.

The internet is often thought of as a democratizer, enabling equality in aspects such as pay, as well as a tool introducing novel communication and monetization opportunities. In this study we examine athletes on Cameo, a website that enables bi-directional fan-celebrity interactions, questioning whether the well-documented gender pay gaps in sports persist in this digital setting. Traditional studies into gender pay gaps in sports are mostly in a centralized setting where an organization decides the pay for the players, while Cameo facilitates grass-roots fan engagement where fans pay for video messages from their preferred athletes. The results showed that even on such a platform gender pay gaps persist, both in terms of cost-per-message, and in the number of requests, proxied by number of ratings. For instance, we find that female athletes have a median pay of 30$ per-video, while the same statistic is 40$ for men. The results also contribute to the study of parasocial relationships and personalized fan engagements over a distance. Something that has become more relevant during the ongoing COVID-19 pandemic, where in-person fan engagement has often been limited.

This paper proposes a novel framework for certifying the fairness of predictive models trained on biased data. It draws from query answering for incomplete and inconsistent databases to formulate the problem of consistent range approximation (CRA) of fairness queries for a predictive model on a target population. The framework employs background knowledge of the data collection process and biased data, working with or without limited statistics about the target population, to compute a range of answers for fairness queries. Using CRA, the framework builds predictive models that are certifiably fair on the target population, regardless of the availability of external data during training. The framework's efficacy is demonstrated through evaluations on real data, showing substantial improvement over existing state-of-the-art methods.