People with visual impairments (PVI) increasingly adopt conversational AI (CAI) in their daily practices. While much existing HCI research has focused on the technical capabilities of CAI, less has examined the societal contexts in which PVI use CAI, particularly from non-Western perspectives. We conducted a study with 14 participants with visual impairments in South Korea using an audio-based probe featuring imagined dialogues between a blind user and a future CAI. Our findings situate CAI use alongside persistent social barriers such as prejudice and restricted employment opportunities that contribute to a lack of social visibility for PVI. These societal conditions shape not only how CAI is used, but also how the potential benefits and limitations of CAI are experienced. We discuss the need for CAI design within the socio-technical realities of PVI, and conclude by discussing the importance of emphasizing social awareness and empowerment in the development of future CAI systems.

Current news commenting systems are designed based on implicitly individualistic assumptions, where discussion is the result of a series of disconnected opinions. This often results in fragmented and polarized conversations that fail to represent the spectrum of public discourse. In this work, we develop a news commenting system where users take on distributed roles to collaboratively structure the comments to encourage a connected, balanced discussion space. Through a within-subject, mixed-methods evaluation (N=38), we find that the system supported three stages of participation: understanding issues, collaboratively structuring comments, and building a discussion. With our system, users’ comments displayed more balanced perspectives and a more emotionally neutral argumentation. Simultaneously, we observed reduced argument strength compared to a traditional commenting system, indicating a trade-off between inclusivity and depth. We conclude with design considerations and trade-offs for introducing distributed roles in news commenting system design.

AI agents, or bots, serve important roles in online communities. However, they are often designed by outsiders or a few tech-savvy members, leading to bots that may not align with the broader community’s needs. How might communities collectively shape the behavior of community bots? We present Botender, a system that enables communities to collaboratively design LLM-powered bots without coding. With Botender, community members can directly propose, iterate on, and deploy custom bot behaviors tailored to community needs. Botender facilitates testing and iteration on bot behavior through case-based provocations: interaction scenarios generated to spark user reflection and discussion around desirable bot behavior. A validation study found these provocations more useful than standard test cases for revealing improvement opportunities and surfacing disagreements. During a five-day deployment across six Discord servers, Botender supported communities in tailoring bot behavior to their specific needs, showcasing the usefulness of case-based provocations in facilitating collaborative bot design.

With generative AI enabling easier production of sexually abusive content, deepfake sexual abuse has intensified, making anyone with visual data be a potential victim or perpetrator. Current moderation systems for non-consensual intimate imagery (NCII) are platform-centric, reactive, and poorly aligned with the workflows of real-time monitors and survivor supporters. To address this gap, we held participatory design workshops with 10 activists affiliated with victim advocacy and survivors experienced in combating deepfake sexual abuse in South Korea. Their insights revealed distinctive challenges, including ambiguity in content classification, barriers to evidence collection, and increased workloads and safety risks during monitoring. Participants suggested features for proactive protection, long-term case tracking, and cross-platform coordination, while emphasizing the need for conversations about data ownership and platform accountability. Based on these findings, we discuss design implications for system and policy that foster multi-stakeholder collaboration to prevent harm, strengthen cross-platform response, and reduce secondary trauma for activists.

Creating games together is both a playful and effective way to develop skills in computational thinking, collaboration, and more.However, game development can be challenging for younger developers who lack formal training.While teenage developers frequently turn to online communities for peer support, their experiences may vary.To better understand the benefits and challenges teens face within online developer communities, we conducted interviews with 18 teenagers who created games or elements in Roblox and received peer support from one or more online Roblox developer communities.Our findings show that developer communities provide teens with valuable resources for technical, social, and career growth.However, teenagers also struggle with inter-user conflicts and a lack of community structure, leading to difficulties in handling complex issues that may arise, such as financial scams.Based on these insights, we propose takeaways for creating positive and safe online spaces for teenage game creators.

Volunteer moderators use various strategies to address online harms within their communities. Although punitive measures like content removal or account bans are common, recent research has explored the potential for restorative justice as an alternative framework to address the distinct needs of victims, offenders, and community members. In this study, we take steps toward identifying a more concrete design space for restorative justice-oriented tools by developing ApoloBot, a Discord bot designed to facilitate apologies when harm occurs in online communities. We present results from two rounds of interviews: first, with moderators giving feedback about the design of ApoloBot, and second, after a subset of these moderators have deployed ApoloBot in their communities. This study builds on prior work to yield more detailed insights regarding the potential of adopting online restorative justice tools, including opportunities, challenges, and implications for future designs.

In-game team communication in online multiplayer games has shown the potential to foster efficient collaboration and positive social interactions. Yet players often associate communication within ad hoc teams with frustration and wariness. Though previous works have quantitatively analyzed communication patterns at scale, few have identified the motivations of how a player makes in-the-moment communication decisions. In this paper, we conducted an observation study with 22 League of Legends players by interviewing them during Solo Ranked games on their use of four in-game communication media (chat, pings, emotes, votes). We performed thematic analysis to understand players' in-context assessment and perception of communication attempts. We demonstrate that players evaluate communication opportunities on proximate game states bound by player expectations and norms. Our findings illustrate players' tendency to view communication, regardless of its content, as a precursor to team breakdowns. We build upon these findings to motivate effective player-oriented communication design in online games.

Platforms are increasingly adopting industrial models of moderation that prioritize scalability and consistency, frequently at the expense of context-sensitive and user-centered values. Building on the multi-level governance framework that examines the interdependent relationship between platforms and middle-level communities, we investigate community appeals systems on Discord as a model for successful community-led governance. We investigate how Discord servers operationalize appeal systems through a qualitative interview study with focus groups and individual interviews with 17 community moderators. Our findings reveal a structured appeals process that balances scalability, fairness, and accountability while upholding community-centered values of growth and rehabilitation. Communities design these processes to empower users, ensuring their voices are heard in moderation decisions and fostering a sense of belonging. This research provides insights into the practical implementation of community-led governance in a multi-level governance framework, illustrating how communities can maintain their core principles while integrating procedural fairness and tool-based design. We discuss how platforms can gain insights from community-led moderation work to motivate governance structures that effectively balance and align the interests of multiple stakeholders.

Hate speech remains a persistent and unresolved challenge in online platforms. Content moderators, working on the front lines to review user-generated content and shield viewers from hate speech, often find themselves unprotected from the mental burden as they continuously engage with offensive language. To safeguard moderators' mental well-being, we designed HateBuffer, which anonymizes targets of hate speech, paraphrases offensive expressions into less offensive forms, and shows the original expressions when moderators opt to see them. Our user study with 80 participants consisted of a simulated hate speech moderation task set on a fictional news platform, followed by semi-structured interviews. Although participants rated the hate severity of comments lower while using HateBuffer, contrary to our expectations, they did not experience improved emotion or reduced fatigue compared with the control group. In interviews, however, participants described HateBuffer as an effective buffer against emotional contagion and the normalization of biased opinions in hate speech. Notably, HateBuffer did not compromise moderation accuracy and even contributed to a slight increase in recall. We explore possible explanations for the discrepancy between the perceived benefits of HateBuffer and its measured impact on mental well-being. We also underscore the promise of text-based content modification techniques as tools for a healthier content moderation environment.

Online communities can offer many benefits for youth including peer learning, cultural expression, and skill development. However, most HCI research on youth-focused online communities has centered communities developed by adults for youth rather than by the youth themselves. In this work, we interviewed 11 teenagers (ages 13-17) who moderate online Discord communities created by youth, for youth. Participants were identified by Discord platform staff as leaders of well-moderated servers through an intensive exam and application-based process. We also interviewed 2 young adults who volunteered as mentors of some of our teen participants. We present our findings about the benefits, motivations, and risks of teen-led online communities, as well as the role of external stakeholders of these youth spaces. We contextualize our work within the broader teen online safety landscape to provide recommendations to better support, encourage, and protect teen moderators and their online communities. This empirical work contributes one of the first studies to date with teen Discord moderators and aims to empower safe youth-led online communities.

The widespread adoption of video conferencing platforms has raised privacy concerns. Recent studies have shown that users express various concerns, such as reluctance toward mandatory camera-on policies, but these findings remain coarse-grained, lacking details on specific features and social relationships. This paper investigates how users perceive privacy with respect to various features in video conferencing platforms. Using the framework of contextual integrity, we analyze information flows across diverse scenarios, such as business meetings and online classes. Our findings reveal nuanced privacy perceptions regarding features that have been discontinued (e.g., attention tracking) or adjusted (e.g., meeting recording), suggesting that the handling of these features could have aligned better with users’ privacy expectations. Additionally, we identify emerging privacy concerns about the pinning and spotlighting features, as users often feel great discomfort when their video is pinned or spotlighted by others in specific contexts. These insights provide a deeper understanding of privacy in video conferencing, highlighting the need for more refined privacy controls and a proactive approach to feature development.

Moderating online spaces effectively is not a matter of simply taking down content: moderators also provide private feedback and defuse situations before they cross the line into harm. However, moderators have little tool support for these activities, which often occur in the backchannel rather than in front of the entire community. In this paper, we introduce Chillbot, a moderation tool for Discord designed to facilitate backchanneling from moderators to users. With Chillbot, moderators gain the ability to send rapid anonymous feedback responses to situations where removal or formal punishment is too heavy-handed to be appropriate, helping educate users about how to improve their behavior while avoiding direct confrontations that can put moderators at risk. We evaluated Chillbot through a two week field deployment on eleven Discord servers ranging in size from 25 to over 240,000 members. Moderators in these communities used Chillbot more than four hundred times during the study, and moderators from six of the eleven servers continued using the tool past the end of the formal study period. Based on this deployment, we describe implications for the design of a broader variety of means by which moderation tools can help shape communities' norms and behavior.

In the summer of 2021, users on the livestreaming platform Twitch were targeted by a wave of "hate raids," a form of attack that overwhelms a streamer's chatroom with hateful messages, often through the use of bots and automation. Using a mixed-methods approach, we combine a quantitative measurement of attacks across the platform with interviews of streamers and third-party bot developers. We present evidence that confirms that some hate raids were highly-targeted, hate-driven attacks, but we also observe another mode of hate raid similar to networked harassment and specific forms of subcultural trolling. We show that the streamers who self-identify as LGBTQ+ and/or Black were disproportionately targeted and that hate raid messages were most commonly rooted in anti-Black racism and antisemitism. We also document how these attacks elicited rapid community responses in both bolstering reactive moderation and developing proactive mitigations for future attacks. We conclude by discussing how platforms can better prepare for attacks and protect at-risk communities while considering the division of labor between community moderators, tool-builders, and platforms.

Volunteer moderators are an increasingly essential component of effective community management across a range of services, such as Facebook, Reddit, Discord, YouTube, and Twitch. Prior work has investigated how users of these services become moderators, their attitudes towards community moderation, and the work that they perform, largely through interviews with community moderators and managers. In this paper, we analyze survey data from a large, representative sample of 1,053 adults in the United States who are active Twitch moderators. Our findings -- examining moderator recruitment, motivations, tasks, and roles -- validate observations from prior qualitative work on Twitch moderation, showing not only how they generalize across a wider population of livestreaming contexts, but also how they vary. For example, while moderators in larger channels are more likely to have been chosen because they were regular, active participants, mods in smaller channels are more likely to have had a pre-existing connection with the streamer. We similarly find that channel size predicts differences in how new moderators are onboarded and their motivations for becoming moderators. Finally, we find that moderators' self-perceived roles map to differences in the patterns of conversation, socialization, enforcement, and other tasks that they perform. We discuss these results, how they relate to prior work on community moderation across services, and applications to research and design in volunteer moderation.

With increasing attention to online anti-social behaviors such as personal attacks and bigotry, it is critical to have an accurate accounting of how widespread anti-social behaviors are. In this paper, we empirically measure the prevalence of anti-social behavior in one of the world's most popular online community platforms. We operationalize this goal as measuring the proportion of unmoderated comments in the 97 most popular communities on Reddit that violate eight widely accepted platform norms. To achieve this goal, we contribute a human-AI pipeline for identifying these violations and a bootstrap sampling method to quantify measurement uncertainty. We find that 6.25% (95% Confidence Interval [5.36%, 7.13%]) of all comments in 2016, and 4.28% (95% CI [2.50%, 6.26%]) in 2020, are violations of these norms. Most anti-social behaviors remain unmoderated: moderators only removed one in twenty violating comments in 2016, and one in ten violating comments in 2020. Personal attacks were the most prevalent category of norm violation; pornography and bigotry were the most likely to be moderated, while politically inflammatory comments and misogyny/vulgarity were the least likely to be moderated. This paper offers a method and set of empirical results for tracking these phenomena as both the social practices (e.g., moderation) and technical practices (e.g., design) evolve.

Online chat functions as a discussion channel for diverse social issues. However, deliberative discussion and consensus-reaching can be difficult in online chats in part because of the lack of structure. To explore the feasibility of a conversational agent that enables deliberative discussion, we designed and developed DebateBot, a chatbot that structures discussion and encourages reticent participants to contribute. We conducted a 2 (discussion structure: unstructured vs. structured) × 2 (discussant facilitation: unfacilitated vs. facilitated) between-subjects experiment (N = 64, 12 groups). Our findings are as follows: (1) Structured discussion positively affects discussion quality by generating diverse opinions within a group and resulting in a high level of perceived deliberative quality. (2) Facilitation drives a high level of opinion alignment between group consensus and independent individual opinions, resulting in authentic consensus reaching. Facilitation also drives more even contribution and a higher level of task cohesion and communication fairness. Our results suggest that a chatbot agent could partially substitute for a human moderator in deliberative discussions.

In the course of every member's integration into an online community, a decision must be made to participate for the first time. The challenges of effective recruitment, management, and retention of new users have been extensively explored in social computing research. However, little work has looked at in-the-moment factors that lead users to decide to participate instead of "lurk", conditions which can be shaped to draw new users in at crucial moments. In this work we analyze 183 million messages scraped from chatrooms on the livestreaming platform Twitch in order to understand differences between first-time participants' and regulars' behaviors and to identify conditions that encourage first-time participation. We find that presence of diverse types of users increases likelihood of new participation, with effects depending on the size of the community. We also find that information-seeking behaviors in first-time participation are negatively associated with retention in the short and medium term.

While the majority of research in chatbot design has focused on creating chatbots that engage with users one-on-one, less work has focused on the design of conversational agents for online communities. In this paper we present results from a three week test of a social chatbot in an established online community. During this study, the chatbot "grew up" from "birth" through its teenage years, engaging with community members and "learning" vocabulary from their conversations. We discuss the design of this chatbot, how users' interactions with it evolved over the course of the study, and how it impacted the community as a whole. We discuss how we addressed challenges in developing a chatbot whose vocabulary could be shaped by users, and conclude with implications for the role of machine learning in social interactions in online communities and potential future directions for design of community-based chatbots.

Research in online content moderation has a long history of exploring different forms that moderation can take, including both user-driven moderation models on community-based platforms like Wikipedia, Facebook Groups, and Reddit, and centralized corporate moderation models on platforms like Twitter and Instagram. In this work I review different approaches to moderation research with the goal of providing a roadmap for researchers studying community self-moderation. I contrast community-based moderation research with platforms and policies-focused moderation research, and argue that the former has an important role to play in shaping discussions about the future of online moderation. I provide six guiding questions for future research that, if answered, can support the development of a form of user-driven moderation that is widely implementable across a variety of social spaces online, offering an alternative to the corporate moderation models that dominate public debate and discussion.

Ensuring high-quality, civil social interactions remains a vexing challenge in many online spaces. In the present work, we introduce a novel approach to address this problem: using psychologically "embedded'' CAPTCHAs containing stimuli intended to prime positive emotions and mindsets. An exploratory randomized experiment (N = 454 Mechanical Turk workers) tested the impact of eight new CAPTCHA designs implemented on a simulated, politically charged comment thread. Results revealed that the two interventions that were the most successful at activating positive affect also significantly increased the positivity of tone and analytical complexity of argumentation in participants' responses. A focused follow-up experiment (N = 120 Mechanical Turk workers) revealed that exposure to CAPTCHAs featuring image sets previously validated to evoke low-arousal positive emotions significantly increased the positivity of sentiment and the levels of complexity and social connectedness in participants' posts. We offer several explanations for these results and discuss the practical and ethical implications of designing interfaces to influence discourse in online forums.

Chatbots have grown as a space for research and development in recent years due both to the realization of their commercial potential and to advancements in language processing that have facilitated more natural conversations. However, nearly all chatbots to date have been designed for dyadic, one-on-one communication with users. In this paper we present a comprehensive review of research on chatbots supplemented by a review of commercial and independent chatbots. We argue that chatbots' social roles and conversational capabilities beyond dyadic interactions have been underexplored, and that expansion into this design space could support richer social interactions in online communities and help address the longstanding challenges of maintaining, moderating, and growing these communities. In order to identify opportunities beyond dyadic interactions, we used research-through-design methods to generate more than 400 concepts for new social chatbots, and we present seven categories that emerged from analysis of these ideas.

People with health concerns go to online health support groups to obtain help and advice. To do so, they frequently disclose personal details, many times in public. Although research in non-health settings suggests that people self-disclose less in public than in private, this pattern may not apply to health support groups where people want to get relevant help. Our work examines how the use of private and public channels influences members' self-disclosure in an online cancer support group, and how channels moderate the influence of self-disclosure on reciprocity and receiving support. By automatically measuring people's self-disclosure at scale, we found that members of cancer support groups revealed more negative self-disclosure in the public channels compared to the private channels. Although one's self-disclosure leads others to self-disclose and to provide support, these effects were generally stronger in the private channel. These channel effects probably occur because the public channels are the primary venue for support exchange, while the private channels are mainly used for follow-up conversations. We discuss theoretical and practical implications of our work.

Bots, or programs designed to engage in social spaces and perform automated tasks, are typically understood as automated tools or as social "chatbots." In this paper, we consider their place alongside users in the emerging social ecosystem of audience participation platforms, through the application of Structural Role Theory. We perform a large-scale analysis of activity levels of user-designed bots on Twitch, finding that they communicate at a much greater rate than any other type of user. We build on a classification scheme derived from prior literature on bot functionalities to identify the roles bots play on Twitch, how these roles vary across different types of Twitch communities, and how users engage with them and vice versa. We conclude with a discussion of what roles are missing and where opportunities lie to re-conceptualize and re-design bots as social actors who help communities grow and evolve.

Research in computer-supported cooperative work has historically focused on behaviors of individuals at scale, using frames of interpersonal interaction such as Goffman's theories of self-presentation. These framings result in research detailing characteristics, personal identities, and behaviors of large numbers of connected and interacting individuals, while the social identity concepts that lead to intra- and inter-group dynamics have received less attention. We argue that the emergent properties of self-categorization and social identity, which are particularly fluid and complex in online spaces, provide a complementary perspective with which to re-examine traditional topics in social computing. We discuss the applicability of the Social Identity Perspective to established and new research domains in CSCW, proposing alternative perspectives on self-presentation, social support, collaboration, conflict, and leadership. We note methodological considerations emerging from this theory. Finally, we consider how broad concepts and lessons from the Social Identity Perspective might inspire CSCW work in the future.

Audience Participation Games challenge traditional assumptions about gameplay by blurring the line between audience and player, allowing audience members to impact gameplay in a meaningful way. Their recent rise in popularity has created new opportunities for game research and development. To better understand this design space, we developed several versions of two prototype games as design probes. We livestreamed them to an online audience in order to develop a framework for audience motivations and participation styles, to explore ways in which mechanics can affect audience members' sense of agency, and to identify promising design spaces. Our results show the breadth of opportunities and challenges that designers face in creating engaging Audience Participation Games.

Online communities have the potential to be supportive, cruel, or anywhere in between. The development of positive norms for interaction can help users build bonds, grow, and learn. Using millions of messages sent in Twitch chatrooms, we explore the effectiveness of methods for encouraging and discouraging specific behaviors, including taking advantage of imitation effects through setting positive examples and using moderation tools to discourage antisocial behaviors. Consistent with aspects of imitation theory and deterrence theory, users imitated examples of behavior that they saw, and more so for behaviors from high status users. Proactive moderation tools, such as chat modes which restricted the ability to post certain content, proved effective at discouraging spam behaviors, while reactive bans were able to discourage a wider variety of behaviors. This work considers the intersection of tools, authority, and types of behaviors, offering a new frame through which to consider the development of moderation strategies.