How To Tame a Toxic Player? A Systematic Literature Review on Intervention Systems for Toxic Behaviors in Online Video Games
Bibliographic record
Abstract
Toxic behavior is known to cause harm in online games. Players regularly experience negative, hateful, or inappropriate behavior. Interventions, such as banning players or chat message filtering, can help combat toxicity but are not widely available or even comprehensively studied regarding their approaches and evaluations. We conducted a systematic literature review that provides insights into the current state of interventions literature, outlining their strengths and shortcomings. We identified 36 interventions and qualitatively analyzed their approaches. We describe the types of toxicity being addressed, the entities through which they act, the methods used by intervention systems, and how they are evaluated. Our results provide guidance for future interventions, outlining a design space based on known systems. Furthermore, our findings highlight gaps in the literature, e.g., a sparsity of empirical evaluations, and underexplored areas in the design space, enabling researchers to explore novel directions for future interventions.
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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 | 0.021 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".