How AI automates community moderation

AI moderation - not the "magic ban Hummer", and the operated system: policy → the models given → → pleybuk → metrics → improvements. The goal is a safe, respectful space without losing the "liveliness" of communication and with a transparent appeal.

1) Basic principles of responsible AI moderation

1. Rules before models. Public code with examples of violations and a table of sanctions.

2. Human-in-the-loop. Auto actions - only soft; tough measures after checking by the moderator.

3. Transparency. Placard "message hidden by the algorithm according to paragraph X.Y," appeal channel (SLA ≤ 72 hours).

4. Data minimization. We store only what is needed for security; PII - under the filter.

5. Responsible Gaming (if relevant). Bots do not push for risk, the priority is help and limits.

2) The tasks AI closes best

Toxicity/hate/threats (classification + thresholds).

Spam/phishing/suspicious links (rules + URL reputation + anomalies).

Offtop and flood (theme/intent → soft redirection to the correct channel).

PII/sensitive data (detection and auto-replace/hide).

Coordinated attacks/botnets (network/behavioral analysis).

Summary of threads (summary for the moderator and quick fixes).

3) Pipeline moderation: from event to action

1. Collection: messages/attachments/metadata (channel, author, time), user complaints.

2. Preprocessing: language normalization/emoji, deduplication, basic rules (stopwords/links).

3. Model Analytics:

toxicity/hate/insults, PII/phishing/suspicious URLs, intent/offtop, emotions (anger/anxiety), risk of coordination (behavioral and graph signals).
4. Playbook solution: soft measure → escalation → manual review.
5. Communication: notification to the user with a link to the rule and appeal.
6. Feedback: marking of challenged cases → additional training/calibration.

4) Model layer (practical and explainable)

Toxicity/stroke/hate classifiers on compact transformers calibrated to your tone.

PII/phishing/spam: regulars + dictionaries + gradient boosting by URL/patterns.

Themes/offtop: BERTopic/clustering for "where to move" markers.

Emotion/tension: auxiliary tags to prioritize the review.

Anomalies/botnets: Isolation Forest/Prophet + graph metrics (PageRank/Betweenness).

Explainability: SHAP/feature importance + solution log.

5) Playbooks of measures: from soft to hard

Soft (car, without a person):

Hide the message from everyone except the author; propose to reformulate.
PII AutoCorrect to "[hidden]."
Auto-transfer to the channel on the topic/ping of the moderator-mentor.
Rate-limit: delay of posting/reactions by N minutes.

Average (auto + post-fact review):

Shadow moderation (visible to the author, hidden by the rest) until verified.
Temporary mut 15-60 minutes per repeat of toxicity.
Limiting links/media to verification.

Hard (only after moderator):

Mut/ban for the term; withdrawal of the right to participate in draws.
Deletion of posts/revocation of prizes in case of violation of the promo conditions.

6) Communication templates (short and respectful)

Delete/Hide:

key> Message Hidden under item 3. 2 Codex (personal attacks). Please reformulate and send again. If you do not agree - appeals in # appeals (answer ≤ 72 hours).

Offtop → redirect:

💡 Sounds like a better topic for # payments. We moved there. Here are the rules for navigating the channels.

PII/Confidentiality:

💡 We have hidden personal data in the message (rule 4. 1). If necessary, edit the post without PII.

Phishing/Links:

💡 The link is marked as risky (rule 5. 4). Please confirm the domain or delete the URL.

7) Dashboards and alerts (daily/weekly)

Daily:

Toxicity/1000 messages, spam rate, PII detections.
"Burning" threads (risk: high), time to the first mod action.
Share of auto-solutions, share of contested ones.

Weekly:

FPR/FNR by class (toxicity, offtop, spam).
Appeals CSAT, mean parsing time, p95 by SLA.
Repeated violations (relapses), the effectiveness of playbooks.
Trends by topic/channel, toxic clock map.

8) Quality metrics and goals

SLA moderation: median ≤ 5 min (ram), p95 ≤ 30 min.

Toxicity accuracy: F1 ≥ 0. 85 on your examples, FPR ≤ 2% on the "net" sample.

Appeals CSAT: ≥ 4. 2/5, the share of canceled actions ≤ 10%.

Noise reduction: − 30% spam, − 25% toxicity/1000 in 90 days.

Impact on experience: time to first response to newcomer ↓, proportion of constructive messages ↑.

9) 90-day implementation roadmap

Days 1-30 - Foundation

Adopt/publish code, sanctions table, AI and appeals policy.

Connect event collection; Enable basic filters (spam/PII/tox keys).

Start AI in "prompt" mode (without automatic sanctions), configure the log.

Mini-dashboard: toxicity/spam/PII, SLA, "burning" threads.

Days 31-60 - Semi-automatic

Enable soft auto-actions: hide, PII auto-correct, rate-limit, offtop transfer.

Additional training of models using local examples, calibration of thresholds.

Introduce anomaly/botnet alerts; the start of weekly retro false positives.

Days 61-90 - Scale and Robustness

Add shadow moderation and temporary muddies (with post-human review).

Integrate mod solutions into kanban (who/what/when/why).

Quarterly report "before/after": toxicity/1000, spam, Appeals CSAT, SLA.

10) Checklists

Ready for launch

Code with examples + sanctions table.
# appeals channel and response patterns.
AI/privacy policy published.
Marking 500-2,000 local examples for additional training.
Dashboard and moderation log are active.

Quality and ethics

Human-in-the-loop for tough measures.
SHAP/feature importance for explainability.
Monitor data drift/model quality.
Weekly retro bugs and threshold updates.
RG frame and data minimization are met.

11) Frequent mistakes and how to avoid them

Auto sanctions "on the go." First tips/soft measures, then escalation.

A single threshold "for everything." Tune by channel/language/content type.

Black box. Without explainability, the quality of appeals and trust falls.

There are no retro false positives. Data drift is inevitable - a constant cycle of improvement is needed.

Localization ignore. Jargon/humor/regional features break models without additional training.

12) Mini-FAQ for fastening

Is AI banning people?

No, it isn't. Auto - only soft measures. Hard - after checking by the moderator.

How to appeal?

Leave a request in # appeals. We will answer before 72 hours and explain the decision.

What data is analyzed?

Only content/message metadata needed for security. Personal data - do not collect/do not publish.

AI moderation is the team's "second pair of hands": it quickly notices toxicity, spam, PII and escalation, and people make subtle decisions. With clear rules, transparent appeal and discipline of improvement, you will reduce noise and conflict, accelerate reactions and maintain a respectful atmosphere - without losing the living voice of the community.