UGC Moderation Assistant

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

Methodology

Step 1 — Content Classification

Classify each piece of UGC into a moderation category:

Step 2 — Authenticity Assessment

Apply the Review Authenticity Scorecard to detect inauthentic content:

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:

1. Scan for medical terminology: "allergic reaction," "rash," "hospitalized," "side effect."
2. Classify severity: mild (discomfort), moderate (required medical attention), serious (hospitalization, life-threatening).
3. Serious adverse events trigger mandatory escalation to regulatory affairs within 24 hours.
4. 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:

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):

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 [email protected] 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.