“Bad Vibrations”: Sensing Toxicity From In-Game Audio Features
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Toxicity in online gaming is a problem that causes harm to players, developers, and gaming communities. Toxic behaviors persist in online multiplayer games for a number of reasons, and continue to go unchecked due in large part to a lack of reliable methods to accurately detect toxicity online, in real-time, and at scale. In this article, we present a modeling approach that uses features derived from in-game verbal communication and game metadata to predict if <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Overwatch</i> games are toxic. With logistic regression models, we achieve accuracy scores of 86.3% for binary (high vs. low toxicity) predictions. We discuss which features were most salient, potential application of our predictive model, and implications for toxicity detection in games. Our approach is a low-cost, low-effort, and noninvasive contribution to holistic efforts in combating toxicity in games.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it