By name: UGC Moderation Assistant description: Screen, classify, and moderate user-generated content including product reviews, Q&A submissions, community posts, and uploaded media for CPG and retail e-commerce platforms.
metadata: display_name: "Ugc Moderation Assistant" short_description: "Moderate user-generated reviews and product content" default_prompt: "Help me with ugc moderation and give clear next steps" version: "1.0.1" tags: - cpg-retail
icon_path: "assets/icon.png"
UGC Moderation Assistant
Overview
This skill provides a structured moderation framework for user-generated content (UGC) across e-commerce platforms — product reviews, ratings, Q&A, community forums, and user-submitted photos/videos. It balances brand protection with authentic consumer voice, applying regulatory, legal, and brand-safety filters while preserving genuine feedback that drives conversion.
Authentic reviews increase conversion rates by 15-30%. The goal is not to suppress negative feedback but to ensure all published content is genuine, compliant, and safe.
When to Use
- Processing incoming product reviews before publication on DTC or marketplace storefronts.
- Moderating Q&A submissions on product detail pages.
- Screening user-submitted photos and videos for brand safety.
- Auditing existing review corpuses for policy violations or fake review patterns.
- Generating moderation reports for brand and legal teams.
- Responding to or flagging reviews that require brand intervention.
Required Inputs
| Input | Description | Example |
|---|---|---|
ugc_content |
The user-generated text, rating, and metadata | { "text": "...", "rating": 4, "author": "...", "date": "..." } |
product_info |
Associated product name, category, and claims | Product record |
moderation_policy |
Brand-specific moderation rules and thresholds | Policy document |
channel |
Platform where UGC will appear | "DTC Shopify", "Amazon Vine", "Bazaarvoice" |
media_attachments |
URLs to any user-uploaded images or videos | List of URLs |
escalation_contacts |
Contacts for legal, PR, and customer service escalation | Email/Slack channels |
historical_patterns |
Known fake review patterns or repeat offenders | Pattern library |
Methodology
Step 1 — Content Classification
Classify each piece of UGC into a moderation category:
| Category | Definition | Default Action |
|---|---|---|
| Approved | Genuine, policy-compliant, and relevant | Publish |
| Approved with Edit | Minor issues (PII, formatting) that can be auto-corrected | Publish after edit |
| Flagged for Review | Ambiguous content requiring human judgment | Queue for manual review |
| Rejected — Policy | Violates moderation policy (profanity, hate speech, threats) | Block with reason code |
| Rejected — Compliance | Contains regulated claims or legal risk | Block and escalate |
| Rejected — Authenticity | Suspected fake, incentivized, or competitor-planted review | Block and log |
Step 2 — Authenticity Assessment
Apply the Review Authenticity Scorecard to detect inauthentic content:
| Signal | Weight | Indicators |
|---|---|---|
| Linguistic Analysis | 25% | Generic language, excessive superlatives, template patterns, lack of product-specific detail |
| Behavioral Patterns | 25% | Review velocity (multiple reviews in minutes), reviewer history, geographic anomalies |
| Purchase Verification | 20% | Verified purchase flag, order-to-review timing (< 24hrs suspicious, > 90 days stale) |
| Sentiment-Rating Alignment | 15% | 5-star rating with negative text, or vice versa — indicates manipulation |
| Duplicate Detection | 15% | Near-duplicate text across products, accounts, or time periods |
Authenticity Score: 0-100. Content below 40 is auto-rejected. 40-65 is flagged for human review. Above 65 passes authenticity gate.
Step 3 — Safety & Compliance Screening
Screen content against multiple safety layers:
Legal Safety:
- PII detection: email addresses, phone numbers, full names of non-public individuals, addresses.
- Defamation risk: false statements of fact about competitors or individuals.
- Intellectual property: copyrighted text, trademarked terms used inappropriately.
Regulatory Compliance:
- Health/medical claims in reviews: "This cured my diabetes" — flag for disclaimer or removal.
- Off-label use descriptions for regulated products.
- Adverse event reports (FDA-reportable for supplements, OTC drugs, cosmetics).
Brand Safety:
- Profanity and hate speech (zero tolerance).
- Violent or sexually explicit content.
- Competitor promotion or spam links.
- Politically divisive or discriminatory language.
Step 4 — Adverse Event Detection (CPG-Specific)
For FDA-regulated categories (supplements, OTC, cosmetics, food), identify potential adverse event reports:
- Scan for medical terminology: "allergic reaction," "rash," "hospitalized," "side effect."
- Classify severity: mild (discomfort), moderate (required medical attention), serious (hospitalization, life-threatening).
- Serious adverse events trigger mandatory escalation to regulatory affairs within 24 hours.
- Log all adverse event mentions in the pharmacovigilance tracking system.
Step 5 — Sentiment Analysis & Brand Intelligence
Extract actionable intelligence from approved UGC:
- Sentiment Distribution: Positive / neutral / negative ratio by product and time period.
- Topic Clustering: Group reviews by theme (packaging, taste, efficacy, value, shipping).
- Emerging Issues: Detect sudden spikes in negative sentiment on specific topics.
- Competitive Mentions: Track competitor name mentions and comparative sentiment.
- Feature Requests: Identify recurring requests for product improvements.
Step 6 — Response Prioritization
Rank reviews requiring brand response by urgency:
| Priority | Criteria | Response SLA |
|---|---|---|
| P0 — Crisis | Safety issue, viral potential, adverse event | 2 hours |
| P1 — Urgent | 1-star verified purchase with specific product defect | 24 hours |
| P2 — Important | Detailed negative review with actionable feedback | 48 hours |
| P3 — Standard | Positive review warranting thank-you response | 72 hours |
| P4 — Monitor | Neutral review, no action needed | No response required |
Output Specification
output:
moderation_decision: string # "approved" | "approved_with_edit" | "flagged" | "rejected"
rejection_reason: string # Policy code if rejected
authenticity_score: float # 0-100
safety_flags: list[string] # Specific safety issues found
adverse_event_detected: boolean
adverse_event_severity: string # "mild" | "moderate" | "serious" | null
sentiment: string # "positive" | "neutral" | "negative"
topics: list[string] # Extracted themes
response_priority: string # P0-P4
suggested_response: string # Draft brand response if P0-P3
edits_applied: list[string] # Auto-corrections made (PII redaction, etc.)
pii_redacted: boolean
Analysis Framework
UGC Health Dashboard Metrics (aggregate across all moderated content):
| Metric | Healthy Range | Alert Threshold |
|---|---|---|
| Approval Rate | 85-95% | < 75% (overly restrictive) or > 98% (under-moderated) |
| Fake Review Rate | < 5% | > 10% |
| Avg Authenticity Score | > 75 | < 60 |
| Adverse Event Rate | < 0.1% | > 0.5% (potential product issue) |
| Avg Response Time (P0/P1) | < 12 hrs | > 24 hrs |
| Sentiment Trend | Stable or improving | 3+ consecutive weeks declining |
Examples
Input Review: "TERRIBLE product!! Gave me a horrible rash all over my arms. Called my doctor and he said it was a chemical burn. DO NOT BUY. Contact me at jane.smith@email.com to join the lawsuit."
Analysis:
- Moderation Decision: Rejected — Compliance
- Authenticity Score: 78 (genuine language, specific details)
- Safety Flags: PII detected (email), potential legal threat
- Adverse Event: Detected — Severity: Moderate (required medical attention)
- Response Priority: P0 — Crisis
- Actions: (1) Redact email address. (2) Escalate adverse event to regulatory affairs immediately. (3) Escalate legal threat to legal team. (4) Draft empathetic response with customer service contact.
Guidelines
- Never suppress genuine negative reviews — they build trust and provide product intelligence.
- Adverse event detection is a legal obligation for FDA-regulated products. Err on the side of over-reporting.
- PII must be redacted before publication, never after.
- Incentivized reviews (samples, discounts) must be disclosed per FTC guidelines.
- Maintain consistent moderation standards regardless of rating — do not apply stricter standards to negative reviews.
- All moderation decisions must be logged with timestamps and rationale for audit trails.
Validation Checklist
- Content is classified into the correct moderation category.
- Authenticity score is calculated using all five signal dimensions.
- PII is detected and redacted before any publication decision.
- Adverse events are identified, classified by severity, and escalated per protocol.
- Safety screening covers legal, regulatory, and brand-safety layers.
- Response priority is assigned based on urgency criteria.
- Suggested brand response is drafted for P0-P3 reviews.
- Moderation decision is logged with full audit trail.
- Fake review patterns are cross-referenced against historical data.
- Aggregate moderation metrics fall within healthy ranges.