Toxic Behavior Prediction
“Which players will receive reports for toxic behavior?”
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A real-world example
Which players will receive reports for toxic behavior?
Toxic players drive away 15% of the non-toxic player base, costing games with 8M MAU roughly $22M annually in lost revenue. Reactive moderation (banning after reports pile up) means the damage is already done. By the time a player gets banned, they have ruined hundreds of matches. A proactive system that flags players before their behavior escalates would prevent the majority of community damage.
How KumoRFM solves this
Graph-learned player intelligence across your entire game ecosystem
Kumo models the social contagion of toxic behavior across the player network. It connects players, matches, chat patterns, reports, and social connections to learn that players who recently lost 5+ matches, whose teammates reported others in those matches, and who are connected to previously banned accounts are 7x more likely to receive reports in the next 48 hours. The temporal graph captures escalation patterns: increasingly negative chat, shorter sessions, and match-quitting streaks that precede toxicity outbursts.
From data to predictions
See the full pipeline in action
Connect your tables, write a PQL query, and get predictions with built-in explainability — all in minutes, not months.
Your data
The relational tables Kumo learns from
PLAYERS
| player_id | account_age_days | honor_level | prior_bans |
|---|---|---|---|
| PLR501 | 340 | 3 | 0 |
| PLR502 | 45 | 1 | 1 |
| PLR503 | 720 | 5 | 0 |
MATCHES
| match_id | player_id | result | duration_min | quit_early |
|---|---|---|---|---|
| M501 | PLR502 | Loss | 8 | Y |
| M502 | PLR502 | Loss | 12 | N |
| M503 | PLR501 | Win | 25 | N |
CHAT_LOGS
| chat_id | match_id | player_id | message_count | flagged_words |
|---|---|---|---|---|
| CL01 | M501 | PLR502 | 42 | 5 |
| CL02 | M502 | PLR502 | 28 | 3 |
| CL03 | M503 | PLR501 | 15 | 0 |
REPORTS
| report_id | reported_player | reporter_id | reason | timestamp |
|---|---|---|---|---|
| R001 | PLR502 | PLR501 | Verbal abuse | 2025-03-01 |
| R002 | PLR502 | PLR503 | AFK/griefing | 2025-03-02 |
SOCIAL_CONNECTIONS
| edge_id | player_a | player_b | type |
|---|---|---|---|
| SC501 | PLR501 | PLR503 | Friend |
| SC502 | PLR502 | PLR501 | Recent opponent |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT COUNT(REPORTS.*, 0, 48, hours) > 0 FOR EACH PLAYERS.PLAYER_ID WHERE PLAYERS.HONOR_LEVEL < 4
Prediction output
Every entity gets a score, updated continuously
| PLAYER_ID | HONOR_LEVEL | ACCOUNT_AGE | TOXIC_48H_PROB |
|---|---|---|---|
| PLR501 | 3 | 340d | 0.05 |
| PLR502 | 1 | 45d | 0.88 |
| PLR503 | 5 | 720d | 0.02 |
Understand why
Every prediction includes feature attributions — no black boxes
Player PLR502 -- Honor 1, 45-day account
Predicted: 88% toxic behavior probability in next 48 hours
Top contributing features
Loss streak (last 24h)
6 consecutive losses
29% attribution
Flagged chat words (last 48h)
8 instances
25% attribution
Early quit rate (last 7d)
40% of matches
19% attribution
Connection to banned players
2 banned friends
15% attribution
Prior ban history
1 previous ban
12% attribution
Feature attributions are computed automatically for every prediction. No separate tooling required. Learn more about Kumo explainability
PQL Documentation
Learn the Predictive Query Language — SQL-like syntax for defining any prediction task in 2–3 lines.
Python SDK
Integrate Kumo predictions into your pipelines. Train, evaluate, and deploy models programmatically.
Explainability Docs
Understand feature attributions, model evaluation metrics, and how to build trust with stakeholders.
Bottom line: A game with 8M MAU that proactively intervenes on toxic players before reports accumulate retains 15% more of its non-toxic community, saving $22M in annual revenue. Kumo detects escalation patterns across match outcomes, chat sentiment, social connections, and behavioral trajectories that reactive report-based systems catch too late.
Related use cases
Explore more gaming use cases
Topics covered
One Platform. One Model. Predict Instantly.
KumoRFM
Relational Foundation Model
Turn structured relational data into predictions in seconds. KumoRFM delivers zero-shot predictions that rival months of traditional data science. No training, feature engineering, or infrastructure required. Just connect your data and start predicting.
For critical use cases, fine-tune KumoRFM on your data using the Kumo platform and Data Science Agent for 30%+ higher accuracy than traditional models.
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
