Beyond Mute and Block: Adoption and Effectiveness of Safety Tools in Social VR, from Ubiquitous Harassment to Social Sculpting
Bibliographic record
Abstract
Harassment in Social Virtual Reality (SVR) is a growing concern. The current SVR landscape features inconsistent access to non-standardised safety features, with minimal empirical evidence on their real-world effectiveness, usage and impact. We examine the use and effectiveness of safety tools across 12 popular SVR platforms by surveying 100 users about their experiences of different types of harassment and their use of features like muting, blocking, personal spaces and safety gestures. While harassment remained common-including hate speech, virtual stalking, and physical harassment-many find safety features insufficient or inconsistently applied. Reactive tools like muting and blocking are widely used, largely driven by users' familiarity from other platforms. Safety tools are also used to proactively curate individual virtual experiences, protecting users from harassment, but inadvertently leading to fragmented social spaces. We advocate for standardising proactive, rather than reactive, anti-harassment tools across platforms, and present insights into future safety feature development.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".