How AI analyzes user behavior in chats

AI helps you understand exactly what people in chats are doing, why they are doing it and what the team should do about it. This is not about "peeping," but about structuring signals to improve rules, onboarding, support and safety.

1) What signals AI extracts from chats

Text:

Intent: question, feedback, complaint, gratitude, offtop, UGC, toxicity/flame.
Themes/subthemes: product, payments, bugs, tournaments, RG (limits, timeouts), security.
Tone/emotions: positive/neutral/negative + anxiety, anger, joy, trust.
Arguments/facts: availability of screens/ID tickets, specific cases.

Behavioral:

Rhythm of participation: time of day, frequency, "silence"> X days.
Format of interactions: initiator of discussions, answers to beginners, "bridge" between branches.
Roles in fact: mentor (many answers), creator (UGC), de facto moderator.

Structural:

Communication graph: who speaks to whom, who connects clusters.
Thread branching: where conflicts/ideas arise, where unanswered questions stick.
Anomalies: spikes in spam, coordinated attacks, repetitive patterns.

2) Pipeline: From "raw messages" to action

1. Collection: events from Discord/Telegram/forums (message, author, channel, time, attachments).

2. Cleaning: removing bots/duplicates, normalizing language and emoji.

3. Enrichment: language, time zone, author type (beginner/helper/moderator).

4. Models:

Classification of intent/themes/tonality/toxicity.
BERTopic/plot clustering.
Columns of influence (centrality, community detection).
Predictor (churn, risk of escalation, probability of participation in the event).
5. Storage: "event lake" + storefronts by day/channel/theme.
6. Activation: dashboards, alerts (SLA/toxicity/escalation), kanban "questions/ideas/complaints," response templates.

3) Model layer: what to choose and why

Intent/tonality/toxicity: compact transformers, further trained on your examples; thresholds are adjustable.

Themes: BERTopic (embeddings + clustering) with auto-shortcuts; monthly update of the dictionary.

Communications graph: NetworkX; PageRank/Betweenness metrics, finding "bridges."

Sequences of events: simple Markov circuits or LSTM/Transformer by user sessions for "question → answer → satisfied/gone" patterns.

Predictive: gradient boosting/logistic regression (explainable) for churn/escalations.

Anomalies: STL/Prophet on time series + alert rules.

4) Daily and weekly dashboards

Daily (RAM):

SLA of response to beginners (median/p95), "hangmen"> X hours.
Toxicity/1000 messages, active disputes, phishing/bot patterns.
Top topics of the day, spikes in bugs/payments/RG.

Weekly (strategy):

New clusters of themes, their dynamics vs last week.
"Bridges" and leaders: who connects groups, who generates constructive.
Funnel of ideas: in the plan → in the work → in the prod.
Risk segments: falling participation, growing negativity, "silence."

5) Practical application scenarios

A. Accelerating onboarding

AI marks the questions of beginners, pings mentors, offers ready-made answers from the knowledge base.

Effect: reduced time to first response, increased novice → active conversion.

B. De-escalation of conflicts

The classifier of emotions + toxicity gives the flag "risk: high," offers the moderator a soft template, indicates a clause of the code.

Effect: less public "battles," less outflow of constructive participants.

C. Product Insights

BERTopic pulls recurring pain on UX/payments; auto-export to kanban with owner and due date.

Effect: quick fixes, visible feedback "what changed."

D. Outflow predictor

Reducing the frequency of messages + negative key + no answers → the "re-onboarding" trigger (selection of relevant channels/events).

Effect: keeping "on the edge," early return of interest.

E. Anti-fraud/safety

Signals of the same patterns (time/device/vocabulary) + links with phishing → auto-alert, restriction of beginners' rights.

Effect: less spam and coordinated attacks.

6) Metrics that really help

Help: SLA of the first reaction (median/p95), the proportion solved for 1 response.

Quality: proportion of constructive messages (guides/answers/reports), UGC/week, number of authors.

Trust/safety: toxicity/1000, controversial cases, proportion of appeals granted.

Impact on the product: ideas → plan → work → production (conversions), time to fix bugs.

Retention: retention D7/D30/M3, "stickiness" (DAU/MAU), the proportion returning to rituals.

Predictive: accuracy of models (ROC-AUC/F1) by churn/escalations; share of saved cases.

7) Ethics, Privacy, Responsible Gaming

Data minimization: Store only what you need for moderation/assistance.

Transparency: closed "how do we apply AI" + appeal channel (SLA ≤ 72 h).

Human-in-the-loop: People have the final decisions on sanctions.

Default RG: Bots do not push for risky behavior; quick links to limits, timeouts, self-exclusion.

Right to delete: clear procedure at the request of the user.

8) 90-day road map

Days 1-30 - Foundation

Describe the AI/privacy/RG policy; turn on the # appeals channel.

Enable collection of chat events; baseline models: intent/tonality/toxicity.

Mini-dashboard: SLA, "hangmen," toxicity, top topics, spam alerts.

Days 31-60 - Insights and co-creation

Enable BERTopic/clusters; communications graph (bridges/leaders).

Create a kanban "questions/ideas/complaints" with owners and deadlines.

Moderator response templates, UGC Plan of the Week/Digest auto-draft.

Days 61-90 - Predictive and persistence

Outflow/escalation risk models; re-onboarding and de-escalation scenarios.

Anomaly alerts for toxicity/bots; monthly revision of the topic dictionary.

Quarterly Report: Before/After for SLA, Toxicity, Retention, ideyam→v Prod.

9) Checklists

Readiness for AI moderation

Code with examples of violations and sanctions table.
Mod response templates with reference to the code clause.
Moderation log and appeal policy.
Test period "tips without auto-actions."
Metrics: toxicity/1000, controversial cases, SLA parsing.

Q & A/onboarding bot

The knowledge base (FAQ, guides, RG) is structured and relevant.
Bot response = short output + guide reference.
Call mentor button when confidence is low.
Question logs → weekly database replenishment.
CSAT after bot response.

10) Ready-made promptts (copy)

a) Thread sum:

💡 "Collect 7-10 points: main theses, solutions, unresolved issues, mentions of bugs/payments/RG. The tone is neutral"

b) Idea extract:

💡 "Highlight unique ideas, combine duplicates, rate frequency and complexity from weekly posts. Table: idea/frequency/complexity/next step"

c) Polite mod-response:

💡 "Formulate a short deletion message according to paragraph X.Y of the code. Offer to reformulate and give a link to the rules. Up to 3 sentences"

d) Re-onboarding for "quiet" participants:

💡 "Generate personal 3 steps based on interests and recent activities: 2 relevant channels, 1 event, 1 guide on the topic. The tone is friendly"

11) Frequent mistakes and how to avoid them

Car sanctions without a person: keep human-in-the-loop, especially in controversial cases.

"Black box" of models: use explainable features and error reports.

Polls without action: Always post resumes and changes to results.

Overheating of "messages" metrics: measure quality (konstruktiv/UGC/idei→v prod).

Localization ignores: Language and prime time regions are critical to model accuracy and engagement.

AI in chats is a magnifying glass and a compass at the same time: it highlights important signals and tells you where to move - in moderation, onboarding, product and safety. With clear rules, respect for privacy and RG, as well as understandable before/after metrics, AI helps to make the community calmer, healthier and more stable - without losing the "lively" nature of communication.