Help, My Game Is Toxic! First Insights from a Systematic Literature Review on Intervention Systems for Toxic Behaviors in Online Video Games
Bibliographic record
Abstract
Toxicity is a common problem in online games. Players regularly experience negative, hateful, or inappropriate behavior during gameplay. Intervention systems can help combat toxicity but are not widely available and or even comprehensively studied regarding their approaches and effectiveness. To assess the current state of toxicity intervention research, we are conducting a systematic literature review about intervention methods for toxic behaviors in online video games. In this work-in-progress, we report the research protocol for this review and the results from a preliminary analysis. We collected 1176 works from 4 digital libraries and performed abstract and full-text screening, resulting in 30 relevant papers containing 36 intervention systems. By analyzing these intervention systems, we found: 1) Most research proposes novel approaches (n = 28) instead of analyzing existing interventions. 2) Most systems intervene only after toxicity occurs (n = 31) with few interventions that act before toxicity. 3) Only few interventions are evaluated with players and in commercial settings (n = 5), highlighting the potential for more research with higher external validity. In our ongoing work, we are conducting an in-depth analysis of the interventions providing insights into their approaches and effectiveness. This work is the first step toward effective toxicity interventions that can mitigate harm to players.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.140 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.018 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".