Suspecting Sarcasm: How League of Legends Players Dismiss Positive Communication in Toxic Environments
Bibliographic record
Abstract
Toxicity in multiplayer gaming is an ongoing problem that threatens the well-being of players, gaming communities, and game developers. Meanwhile, interventions that promote positive interactions and proactively create positive gaming spaces are still in their infancy; little is known about how players respond to positivity. In our study, 959 League of Legends players were presented with either 10 positive chat logs or 10 negative chat logs, and asked to reflect on the content and how representative such communication is of their own gaming experiences. We thematically coded participants' free-form answers (identifying the themes normalize, acknowledge, downplay, cope, blame, and make personal), and compared the positive and negative conditions in terms of theme prevalence. Our findings show that participants were more likely to normalize and acknowledge toxic negativity than positivity. Furthermore, the dominant response to positivity consisted of downplaying messages as not representative and rare, and even expressing suspicion that messages must have been fabricated or intended as sarcasm. Participants overwhelmingly cope by muting chat, protecting them from toxic interactions, but leaving them unexposed to positive communication and other beneficial social interactions within play.
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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".