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Zinan Zhang

Publications and source records attributed to Zinan Zhang.

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Improving Family Co-Play Experiences through Family-Centered Design

Cooperative play (co-play) is often positioned as a family-beneficial practice that can strengthen parent-child bonds and support parental mediation in games. Yet co-play in user-generated virtual worlds (UGVWs) can be disrupted by real-time harms that parents cannot easily prevent. Roblox, a platform with millions of user-generated virtual worlds and a large child player base, illustrates this challenge. Prior work on harmful UGVW design highlights risks beyond content problems, including manipulative monetization prompts, unmoderated social interactions, emergent in-world behaviors, and narrative designs that may normalize harmful ideologies. Current governance and moderation approaches, largely adapted from social media, focus on static artifacts and often fail to capture interactive and emergent harms in virtual worlds. This workshop paper asks: how might UGVWs and their platforms be designed to minimize harms that specifically impair family co-play experiences?

cs.HC

Children's Online Safety Risks and Ethical Considerations in XR Games

Emerging extended reality technologies are reshaping how children play, learn, and socialize. Yet, they also present serious safety risks. Gaming, a primary form of entertainment for children, is also one of the key applications of XR. While XR platforms offer immersive and engaging gaming experiences, recent news has highlighted safety concerns such as car accidents, lower judgment for real-world situations, and exposure to disturbing content like virtual rape. This research examines how XR game design may lead to online safety risks for children. Through analysis of player forums, game developer forums, and interviews with child players, we identify harmful XR design patterns, explore how developers collaboratively generate and implement risky game ideas, and document children's firsthand experiences of online safety risks. Existing ethical frameworks often fail to address the immersive and socially dynamic nature of XR games. We advocate for a child-centered, design-aware approach to ethical considerations in XR games, urging platforms and policymakers to prioritize children's developmental needs. Our work aims to help shape safer, more inclusive XR environments through research and cross-sector collaboration.

cs.HC

HippoCamp: Benchmarking Contextual Agents on Personal Computers

We present HippoCamp, a new benchmark designed to evaluate agents' capabilities on multimodal file management. Unlike existing agent benchmarks that focus on tasks like web interaction, tool use, or software automation in generic settings, HippoCamp evaluates agents in user-centric environments to model individual user profiles and search massive personal files for context-aware reasoning. Our benchmark instantiates device-scale file systems over real-world profiles spanning diverse modalities, comprising 42.4 GB of data across over 2K real-world files. Building upon the raw files, we construct 581 QA pairs to assess agents' capabilities in search, evidence perception, and multi-step reasoning. To facilitate fine-grained analysis, we provide 46.1K densely annotated structured trajectories for step-wise failure diagnosis. We evaluate a wide range of state-of-the-art multimodal large language models (MLLMs) and agentic methods on HippoCamp. Our comprehensive experiments reveal a significant performance gap: even the most advanced commercial models achieve only 48.3% accuracy in user profiling, struggling particularly with long-horizon retrieval and cross-modal reasoning within dense personal file systems. Furthermore, our step-wise failure diagnosis identifies multimodal perception and evidence grounding as the primary bottlenecks. Ultimately, HippoCamp exposes the critical limitations of current agents in realistic, user-centric environments and provides a robust foundation for developing next-generation personal AI assistants.

cs.AI

Teen Vigilance: Navigating Risky Social Interactions on Discord

Teenagers are avid users of Discord, a fast growing platform for synchronous communication where they often interact with strangers. Because Discord combines private DMs, semi-private voice channels, and public servers in one place, it creates a hybrid environment that can produce complex and underexplored safety risks for teenagers. Drawing on 16 interviews with teenage Discord users, this study examines their strategies for navigating risky social interactions in the platform. Our findings reveal that when teenagers encounter risks during social interactions, they exercise vigilance by evaluating suspicious interactions before forming friendships, using safety tools, and engaging in controlled risk-taking to safeguard their privacy and security. At the community level, they mitigate risks through selective participation in servers, a practice supported by vigilant governance structures. We discuss how vigilance enables teenagers to act during risky encounters to protect themselves, advancing understanding of teenagers' agency in risk navigation and informing teen-centered designs for safer online environments.

cs.HC

Understanding and Mitigating Harmful Design in User-Generated Virtual Worlds

Virtual space offers innovative ways for individuals to engage with one another in a digital setting. Prominent virtual social platforms, such as Facebook Spaces, VR Chat, and AltspaceVR, facilitate social connections, allowing users to interact seamlessly. Additionally, certain video games, like Second Life and World of Warcraft, are set within these virtual spaces as well, providing immersive player experiences. As the popularity of virtual space grows, various companies have begun to democratize the process of creating these spaces, shifting the development from skilled professionals to hobbyist creators. Platforms like Minecraft, Roblox, and RecRoom enable users to create and publish their own virtual environments, hosting a wide range of interactions and narratives. This shift echoes the rise of user-generated content, where content creators create and publish content on platforms, such as social media platforms [6]. For example, YouTubers upload videos on YouTube and Reddit users post text-based content on Reddit. For a long time, user-generated content has predominantly contained text, videos, and images. However, with the emergence of virtual spaces, some platforms now allow creators to create and publish their own virtual spaces, leading to the emergence of user-generated virtual worlds.

cs.HC

Exploring the effect of social media and spatial characteristics during the COVID-19 pandemic in China

The declaration of COVID-19 as a pandemic has largely amplified the spread of related information on social media, such as Twitter, Facebook, and WeChat.Unlike the previous studies which focused on how to detect the misinformation or fake news related toCOVID-19, we investigate how the disease and information co-evolve in the population. We focus onCOVID-19and its information during the period when the disease was widely spread in China, i.e., from January 25th to March 24th, 2020. We first explore how the disease and information co-evolve via the spatial analysis of the two spreading processes. We visualize the geo-location of both disease and information at the province level and find that disease is more geo-localized compared to information. We find a high correlation between the disease and information data, and also people care about the spread only when it comes to their neighborhood. Regard to the content of the information, we find that positive messages are more negatively correlated with the disease compared to negative and neutral messages. Additionally, we introduce machine learning algorithms, i.e., linear regression and random forest, to further predict the number of infected using different disease spatial related and information-related characteristics. We obtain that the disease spatial related characteristics of nearby cities can help to improve the prediction accuracy. Meanwhile, information-related characteristics can also help to improve the prediction performance, but with a delay, i.e., the improvement comes from using, for instance, the number of messages 10 days ago, for disease prediction. The methodology proposed in this paper may shed light on new clues of emerging infections

physics.soc-ph