Researchers across domains have investigated children’s use of LLM-based chatbots through various perspectives and methodologies. However, prior research remains fragmented regarding anthropomorphism, the tendency for children to assign human characteristics to those large language Model (LLM) chatbots as non-human objects. By analyzing 35 empirical studies published between 2022 and 2025, this systematic literature review identifies the drivers of anthropomorphism in children’s LLM chatbot interactions and the subsequent outcomes of these interactions. We found that human-like persona construction, adaptive scaffolding, supportive companionship, and non-human embodied design drive children’s anthropomorphic interactions. Additionally, five anthropomorphic outcomes emerged, including children exhibiting paradoxical social and moral responses, dual consciousness about the chatbots, forming varying social ties, exploring social boundaries, and attributing human narratives to conversation breakdowns. The findings, including both benefits and risks, can inform the future design and development of LLM chatbots focused on children’s well-being and promoting sustainable interactions that meet children’s developmental needs.

Although social media use is ubiquitous among adolescents, research often emphasizes benefits and risks without considering the design-based affordances that may influence these experiences. Affordances enable or constrain user behavior and thus provide a valuable lens for promoting positive online engagement. To this end, we developed an affordance-centered framework and systematically coded 66 empirical studies on U.S. adolescents published in the past decade to map how platform features relate to specific user activities, the affordances they instantiate, and associated benefits and risks. For instance, Discoverability affordance, driven by algorithmic recommendations, supported Cognitive engagement by facilitating learning while heightening exposure to misinformation. Visibility affordance enabled Identity exploration through ephemeral sharing, lowering performance anxiety but raising post-disclosure regret when content reached hostile viewers. By clarifying how specific affordances facilitate adolescent experiences, our work contributes a design-grounded framework for future research that advances both theoretical understanding and the development of youth-centered interventions.

As Large Language Models (LLMs) become increasingly integral to daily life, users are engaging with multiple LLM chatbots for various needs; however, prior research on LLM risks often remains lab-based or focuses on single LLMs like ChatGPT or singular risks like privacy. To gain a multi-risk, cross-chatbot understanding of user experiences, we analyze Reddit discussions around seven major LLM chatbots using the NIST AI Risk Management Framework. We find that user-reported risks are unevenly distributed and chatbot-specific: ChatGPT is associated with safety and fairness concerns, Gemini with privacy, and Claude with security and resilience. Less frequent risks, such as explainability and privacy, appear as user trade-offs, whereas prevalent risks like fairness are experienced as direct harms. Our findings underscore the need to operationalize chatbot-specific risk mitigation, moving beyond system-centered risk mitigation to human-centered interventions that align with users’ lived experiences.

In addressing various risks on social media, the HCI community has advocated for teen-centered risk detection technologies over platform-based, parent-centered features. However, their real-world viability remains underexplored by secondary stakeholders beyond the family unit. Therefore, we present an evaluation of a teen-centered social media risk detection dashboard through online interviews with 33 online safety experts. While experts praised our dashboard’s clear design for teen agency, their feedback revealed five primary tensions in implementing and sustaining such technology: objective vs. context-dependent risk definition, informing risks vs. meaningful intervention, teen empowerment vs. motivation, need for data vs. data privacy, and independence vs. sustainability. These findings motivate us to rethink “teen-centered” and a shift from a “fail fast” to a “mature safely” paradigm for youth safety technology innovation. We offer design implications for addressing these tensions before system deployment with teens and strategies for aligning secondary stakeholders’ interests to deploy and sustain such technologies in the broader ecosystem of youth online safety.

AI companions enable deep emotional relationships by engaging a user’s sense of identity, but they also pose risks like unhealthy emotional dependence. Mitigating these risks requires first understanding the underlying process of identity construction and negotiation with AI companions. Focusing on Character.AI (C.AI), a popular AI companion, we conducted an LLM-assisted thematic analysis of 22,374 online discussions on its subreddit. Using Identity Negotiation Theory as an analytical lens, we identified a three-stage process: 1) five user motivations; 2) an identity negotiation process involving three communication expectations and four identity co-construction strategies; and 3) three emotional outcomes. Our findings surface the identity work users perform as both performers and directors to co-construct identities in negotiation with C.AI. This process takes place within a socio-emotional sandbox where users can experiment with social roles and express emotions without non-human partners. Finally, we offer design implications for emotionally supporting users while mitigating the risks.

Large language models (LLMs) chatbots like ChatGPT are increasingly used for mental health support. They offer accessible, therapeutic support but also raise concerns about misinformation, over-reliance, and risks in high-stakes contexts of mental health. We crowdsource large-scale users' posts from six major social media platforms to examine how people discuss their interactions with LLM chatbots across different mental health conditions. Through an LLM-assisted pipeline grounded in Value-Sensitive Design (VSD), we mapped the relationships across user-reported sentiments, mental health conditions, perspectives, and values. Our results reveal that the use of LLM chatbots is condition-specific. Users with neurodivergent conditions (e.g., ADHD, ASD) report strong positive sentiments and instrumental or appraisal support, whereas higher-risk disorders (e.g., schizophrenia, bipolar disorder) show more negative sentiments. We further uncover how user perspectives co-occur with underlying values, such as identity, autonomy, and privacy. Finally, we discuss shifting from "one-size-fits-all" chatbot design toward condition-specific, value-sensitive LLM design.

Social media platforms have faced increasing scrutiny over whether and how they protect youth online. While online risks to children have been well-documented by prior research, how social media platforms communicate about these risks and their efforts to improve youth safety have not been holistically examined. To fill this gap, we analyzed N = 352 press releases and safety-related blogs published between 2019 and 2024 by four platforms popular among youth: YouTube, TikTok, Meta (Facebook and Instagram), and Snapchat. Leveraging both inductive and deductive qualitative approaches, we developed a comprehensive framework of seven problem areas where risks arise, and social media platforms claim to address these risks through various online safety features. Our analysis revealed uneven emphasis across problem areas, with most communications focused on Content Exposure and Interpersonal Communication, whereas less emphasis was placed on Content Creation, Data Access, and Platform Access. Additionally, we identified three problematic communication practices related to their described safety features, including discrepancies between feature implementation and availability, unclear or inconsistent explanations of safety feature operation, and a lack of evidence regarding the effectiveness of safety features in mitigating risks once implemented. Based on these findings, we discuss the communication gaps between risks and the described safety features, as well as the tensions in achieving transparency in platform communication. Our analysis of platform communication informs guidelines for responsibly communicating about youth safety features.

On social media, teens must manage their interpersonal boundaries not only with other people, but also with the algorithms embedded in these platforms.In this context, we engaged seven teens in an Asynchronous Remote Community (ARC) as part of a multi-year Youth Advisory Board (YAB) to discuss how they navigate, cope, and co-design for improved boundary management.Teens had preconceived notions of different platforms and navigated boundaries based on specific goals; yet, they struggled when platforms lacked the granular controls needed to meet their needs.Teens enjoyed the personalization afforded by algorithms, but they felt violated when algorithms pushed unwanted content.Teens designed features for enhanced control over their discoverability and for real-time risk detection to avoid boundary turbulence.We provide design guidelines for improved social media boundary management for youth and pinpoint educational opportunities to enhance teens' understanding and use of social media privacy settings and algorithms.

Academic research is largely an adult endeavor that creates systemic power imbalances when studying teen-centered topics, such as adolescent online safety. To rectify this problem, we engaged seven teens as co-researchers through a year-and-a-half-long Youth Advisory Board (YAB) program to critically assess our research processes, lead online safety solutions, and to reflect on their experiences participating in a YAB. Teens pushed back on standard research practices such as parental consent, sought decision-making power in study documentation, design, and execution, and gave more meaningful feedback on research protocols when more deeply involved in the research. For safety interventions, teens proposed both incremental changes for social media platforms (e.g., advanced privacy settings) and more disruptive changes (e.g., decentralized social media platforms) that enhance individual control, digital resilience, and equity. For the YAB, teens highlighted challenges, such as losing momentum over time, lack of collaborative opportunities, and competing interests, fueling frustrations and rifts in engagement. Our research underscores the value of involving teens as co-partners in shaping online safety research. Finally, we provide design implications for social media safety interventions that strengthen teens' agency and actionable guidelines for developing future long-term programs to ensure meaningful contributions to online safety research.

Protecting children’s online privacy is paramount. Online platforms seek to enhance child privacy protection by implementing new classification systems into their content moderation practices. One prominent example is YouTube’s “made for kids” (MFK) classification. However, traditional content moderation focuses on managing content rather than users’ privacy; little is known about how users experience these classification systems. Thematically analyzing online discussions about YouTube’s MFK classification system, we present a case study on content creators’ and consumers’ experiences. We found that creators and consumers perceived MFK classification as misaligned with their actual practices, creators encountered unexpected consequences of practicing labeling, and creators and consumers identified MFK classification’s intersections with other platform designs. Our findings shed light on an interwoven network of multiple classification systems that extends the original focus on child privacy to encompass broader child safety issues; these insights contribute to the design principles of child-centered safety within this intricate network.

Moderation systems of online games often follow a retributive model inspired by real-world criminal justice, expecting that punishments can help users to reform behavior. However, decades of criminological research show that punishments alone do not work and call for a rehabilitative approach, such as community-based rehabilitation (CBR), to help offenders transform their minds and behavioral patterns. Motivated by this call, we explore how moderated users view punishments in a community context and how other community members respond in League of Legends (LoL), one of the largest online games. Specifically, we focus on how peer support is sought and provided on the /r/LeagueOfLegends subreddit, the largest LoL-related online community. Our content analysis of player discussions characterized the communication between moderated users and peers as informative, constructive, and reflexive. We highlight the importance of involving community in moderation systems and discuss implications for designing CBR mechanisms that could enhance moderation systems.

On June 24, 2022, the U.S. Supreme Court overturned Roe v. Wade, which has led to full bans on most abortions in 14 states within one year. Many people in the U.S. use period and fertility tracking apps for reproductive healthcare and concerns have arisen about the privacy risks these apps might pose in the wake of Roe reversal. Existing literature on privacy risks of period and fertility tracking apps has primarily examined the privacy policies and practices of these apps. However, how users make sense of the privacy risks of these apps, especially in the post-Roe time, remains understudied. This study explores collective privacy sensemaking on social media, a practice in which people collectively make sense of a privacy situation. Our findings reveal how people contextualize privacy issues, speculate about the associated risks, as well as explore risk mitigation strategies. We conclude with privacy design implications for privacy design in period and fertility tracking apps and contribute insights that could inform policymaking and legal perspectives.

Multiplayer online games seek to address toxic behaviors such as trolling and griefing through behavior moderation, where penalties such as chat restriction or account suspension are issued against toxic players in the hope that punishments create a teachable moment for punished players to reflect and improve future behavior. While punishments impact player experience (PX) in profound ways, little is known regarding how players experience behavior moderation. In this study, we conducted a survey of 291 players to understand their experiences with punishments in online multiplayer games. Through several statistical analyses, we found that moderation explanation plays a critical role in improving players’ perceived transparency and fairness of moderation; and these perceptions significantly affect what players do after punishments. We discuss moderation experience as an important facet of PX, bridge the game and moderation literature, and provide design implications for behavior moderation in multiplayer online games.

Online platforms like YouTube and Instagram have enabled the platformization and monetization of creative work, allowing content creators to derive revenue and thrive in a creator economy. While much work has been done to understand content creation on single platforms, the creative practice often involves content creators’ agency and practice to interact with multiple platforms and make strategic decisions to optimize such interactions. In this paper, we use an interview study with 21 cross-platform creators to understand how they negotiate with platforms in their creative practices through the construction of creator ecology. We found that participants developed priorities among platforms based on varied criteria, paid attention to cross-platform content synchronization, and stressed managing and converting audiences across platforms to grow their fanbase. Our findings highlight the complex interplay between creator agency and labor, as well as yield implications for future design possibilities of creator empowerment and support.

Transparency matters a lot to people who experience moderation on online platforms; much CSCW research has viewed offering explanations as one of the primary solutions to enhance moderation transparency. However, relatively little attention has been paid to unpacking what transparency entails in moderation design, especially for content creators. We interviewed 28 YouTubers to understand their moderation experiences and analyze the dimensions of moderation transparency. We identified four primary dimensions: participants desired the moderation system to present moderation decisions saliently, explain the decisions profoundly, afford communication with the users effectively, and offer repairment and learning opportunities. We discuss how these four dimensions are mutually constitutive and conditioned in the context of creator moderation, where the target of governance mechanisms extends beyond the content to creator careers. We then elaborate on how a dynamic, transparency perspective could value content creators' digital labor, how transparency design could support creators' learning, as well as implications for transparency design of other creator platforms.

Researchers across various fields have investigated how users experience moderation through different perspectives and methodologies. At present, there is a pressing need of synthesizing and extracting key insights from prior literature to formulate a systematic understanding of what constitutes a moderation experience and to explore how such understanding could further inform moderation-related research and practices. To answer this question, we conducted a systematic literature review (SLR) by analyzing 42 empirical studies related to moderation experiences and published between January 2016 and March 2022. We describe these studies' characteristics and how they characterize users' moderation experiences. We further identify five primary perspectives that prior researchers use to conceptualize moderation experiences. These findings suggest an expansive scope of research interests in understanding moderation experiences and considering moderated users as an important stakeholder group to reflect on current moderation design but also pertain to the dominance of the punitive, solutionist logic in moderation and ample implications for future moderation research, design, and practice.

Esports, like traditional sports, face governance challenges such as foul play and match fixing. The esports industry has seen various attempts at governance structure but is yet to form a consensus. In this study, we explore esports governance in League of Legends (LoL), a major esports title. Through a two-stage, mixed-methods analysis of rule enforcement that Riot Games, LoL's developer and publisher, has performed against esports participants such as professional players and teams, we qualitatively describe rule breaking behaviors and penalties in LoL esports, and quantitatively measure how contextual factors such as time, perpetrator identity, and region might influence governance outcomes. These findings about rule enforcement allow us to characterize the esports governance of LoL as top-down and paternalistic, and to reflect upon professional players' work and professionalization in the esports context. We conclude by discussing translatable implications for esports governance practice and research.

How social media platforms could fairly conduct content moderation is gaining attention from society at large. Researchers from HCI and CSCW have investigated whether certain factors could affect how users perceive moderation decisions as fair or unfair. However, little attention has been paid to unpacking or elaborating on the formation processes of users' perceived (un)fairness from their moderation experiences, especially users who monetize their content. By interviewing 21 for-profit YouTubers (i.e., video content creators), we found three primary ways through which participants assess moderation fairness, including equality across their peers, consistency across moderation decisions and policies, and their voice in algorithmic visibility decision-making processes. Building upon the findings, we discuss how our participants' fairness perceptions demonstrate a multi-dimensional notion of moderation fairness and how YouTube implements an algorithmic assemblage to moderate YouTubers. We derive translatable design considerations for a fairer moderation system on platforms affording creator monetization.

To manage user-generated harmful video content, YouTube relies on AI algorithms (e.g., machine learning) in content moderation and follows a retributive justice logic to punish convicted YouTubers through demonetization, a penalty that limits or deprives them of advertisements (ads), reducing their future ad income. Moderation research is burgeoning in CSCW, but relatively little attention has been paid to the socioeconomic implications of YouTube's algorithmic moderation. Drawing from the lens of algorithmic labor, we describe how algorithmic moderation shapes YouTubers' labor conditions through algorithmic opacity and precarity. YouTubers coped with such challenges from algorithmic moderation by sharing and applying practical knowledge they learned about moderation algorithms. By analyzing video content creation as algorithmic labor, we unpack the socioeconomic implications of algorithmic moderation and point to necessary post-punishment support as a form of restorative justice. Lastly, we put forward design considerations for algorithmic moderation systems.