Simona Liao

If you are at CSCW 2025, come check out my work this morning! I will present in Salong Nina room on Level 3 during the 9-10:30 session. :)

Finally, many thanks to my advisor @axz.bsky.social 💖 and @socialfutureslab.bsky.social for all the guidance and support, to the second author Evan (Hanwen) 👨🏻‍💻 who is a VR expert, and to UW Reality Lab for the VR devices 🕹️!

3 (continued) instead of automating the process for convenience, which could be a potential pitfall for proactive protection.

3. Safety needs are dynamic and can sometimes be contradictory, both between players and even for the same player over time. Thus, features should (1) focus on supporting players in making choices and (2) provide players with multiple ways to make that choice effortlessly.

2. Safety features should center marginalized players’(those with higher risks of encountering harassment) experiences and not conflict with the existing safety practices they have developed.

1. Proactive safety features could improve overall social and gaming experiences. Even experienced players not concerned about harassment praised these features, highlighting the potential for safety designs to enhance general gameplay, moving away from solely damage-focused.

Three key takeaways for designing future proactive and instant reactive strategies✍🏼

Puffer 🐡 is our safety system prototype, including four proactive and instant-reactive features, and built with Meta XR All-in-One SDK. See our prototype demo video: youtu.be/Nr-X7fps2xc

3. We empowered bystanders to intervene in harassment, since they are more accessible than moderators. Lowering the barrier to intervene fosters mutual support and encourages positive player behaviors. 👭

2. We sought to clarify the ambiguous social norms in VR, making it easier for players to understand how to interact with one another appropriately.

We explored three approaches:
1. We improved the design of personal bubble, an existing proactive safety feature available in most social VR games. We made it more usable, learnable, and context-aware.

Embodied harassment is also harder to remedy with delayed reactive strategies that respond only after the incident occurred (e.g, reporting)

How can we create more proactive and instant reactive strategies that prevent harassment or stop it immediately, to reduce its damage? 🤔

Building Proactive and Instant-Reactive Safety Designs to Address Harassment in Social Virtual Reality | Proceedings of the ACM on Human-Computer Interaction Social Virtual Reality (VR) games offer immersive socialization experiences but pose significant challenges of harassment. Common solutions, such as reporting and moderation, address harassment after ...

🔗 Full paper link: dl.acm.org/doi/10.1145/...

Excited to share my first research paper on social virtual reality✨🎮 (for #cscw2025 )!

Social VR provides an immersive gaming experience that builds closer relationships. However, this same immersiveness also makes embodied harassment in social VR more traumatizing :(

Pie chart representing the racial groups, with count in brackets 51.4% White/Caucasian (127) 17.8% Asian (44) 8.9% Prefer not to disclose (22) 8.1% Prefer to self-describe (20) 8.1% Hispanic and Latino (20) 3.6% Black or African American (9) 2.0% Middle Eastern (5)

Thanks for your question, Sung! Here is a pie chart representing the racial identities of non-native English speakers. White folks take up ~50% (most are from European countries) followed up by ~20% Asian folks

5. Researchers in computer science fields are more comfortable disclosing their LLM usage and have lower ethical concerns compared to researchers in other disciplines (see fig 4).

4. Women and non-binary researchers have greater ethical concerns, as do those with more years of research experience (see fig 4).

Figure 4: A collection of 36 heatmaps. The y-axis or each row represents a demographic breakdown (from top to bottom: Race, Gender, Langauge, Experience, Field of Study, and All participants). Each column, from left to right, represents LLM usage frequency, perception of risk, benefits, ethics, the comfort level of disclosing to peers, and of disclosing to reviewers. For each individual heatmap, the x-axis includes the six types of LLM usage (from left to right, information seeking, editing, ideation & framing, direct writing, data cleaning & analysis, and data generation.).

2. Researchers who are non-White, non-native English speaking, and junior researchers both use LLMs more frequently and also perceive higher benefits and lower risks (see Fig 4).

3. Equity was a large theme in respondents’ discussions of the benefits of LLMs.

Fig 2: Overview of Usage Frequency Divided by LLM Usage Type (N=816). The left diverging bar chart displays the distribution of usage frequency across different types of LLM usage, with each type represented by a separate row. The frequency levels, from left to right, are: Very Rarely, Rarely, Occasionally, Frequently, and Very Frequently, with the midpoint of the chart centered at "Occasionally." The grey bar chart on the right indicates the percentage of responses that report "Never" using LLMs for each corresponding type. From this plot, we can tell that researchers report using LLMs for Information Seeking and Editing most frequently, and for Data Cleaning & Analysis and Data Generation the least frequently.

Our Key Takeaways:

1. 81% of researchers we surveyed have used LLMs in one or more places in their research pipeline, with the tasks of Information Seeking and Editing reported most frequently and Data Analysis and Generation reported least frequently (see fig 2).

LLMs as Research Tools: A Large Scale Survey of Researchers' Usage and Perceptions The rise of large language models (LLMs) has led many researchers to consider their usage for scientific work. Some have found benefits using LLMs to augment or automate aspects of their research pipe...

Hi everyone, I am excited to share our large-scale survey study with 800+ researchers, which reveals researchers’ usage and perceptions of LLMs as research tools, and how the usage and perceptions differ based on demographics.

See results in comments!

🔗 Arxiv link: arxiv.org/abs/2411.05025