Scoring Methodology

How Influencer Brand Safety Scoring Works

Every creator receives a brand safety score from 1 to 100, calculated by 7 independent AI scoring agents. CreatorScore provides a transparent, explainable influencer vetting methodology — so brands know exactly why a creator scored the way they did.

Score Ranges

Scores range from 1 (highest risk) to 100 (lowest risk). Higher is always better.

90–100
Exceptional

Top-tier creator. Consistently brand-safe, authentic audience, positive community. Ideal for premium brand partnerships.

80–89
Excellent

Very low risk. Strong performance across all agents. Suitable for most brand campaigns with confidence.

70–79
Good

Low risk overall. Minor areas for improvement but generally safe for brand partnerships.

60–69
Fair

Some caution needed. Review the score breakdown to understand specific concerns before partnering.

1–59
Poor

Significant concerns identified. High risk for brand reputation. Detailed review strongly advised before partnering.

7 Scoring Agents

Each creator is evaluated by 7 independent agents. Each scores 0–100 internally, then a weighted average produces the final 1–100 CreatorScore.

Content Risk

20%

Evaluates content for hate speech, explicit/NSFW material, violence, extremist ideology, and profanity. This is the most heavily weighted agent because a single brand safety incident can cause lasting reputational damage.

What We Analyze

  • Every post caption and video transcript through AI-powered hate speech detection
  • Thumbnails and video frames through computer vision for NSFW and violence detection
  • Text-on-screen extraction to catch hidden messages that differ from spoken audio
  • Pattern matching for extremist ideology across 35+ patterns
  • Profanity frequency with niche-aware tolerance (comedy creators aren't penalized the same as family creators)

Why It Matters

A single offensive post can go viral and damage both the creator's and brand's reputation overnight.

Authenticity

20%

Detects fake followers, bot commenters, spam engagement, engagement pods, and artificially inflated metrics. Ensures the audience is real people, not purchased bots.

What We Analyze

  • Commenter profiles for bot indicators (suspicious usernames, empty profiles, extreme following ratios)
  • Duplicate and spam comments via content fingerprinting
  • Low-effort comment ratios (emoji-only, single-word responses)
  • Engagement pod detection (coordinated groups artificially boosting numbers)
  • Comment timing patterns for naturalness vs. suspicious bursts
  • Engagement-to-follower ratio anomalies

Why It Matters

Fake engagement means your ad spend reaches bots, not real customers. Authenticity directly impacts ROI.

Brand Safety

15%

Assesses overall brand association risk by combining content safety signals with FTC ad disclosure compliance. Proper disclosure protects both brands and creators legally.

What We Analyze

  • Brand safety signals weighted alongside content risk factors
  • FTC ad disclosure compliance — are sponsored posts properly labeled with #ad, #sponsored, or paid partnership tags?
  • Historical brand partnership track record and controversy associations
  • Network-level risk from collaborations and cross-promotions

Why It Matters

Brands need partners who protect the relationship. Missing FTC disclosures expose brands to legal liability, and past controversies signal future risk.

Audience Quality

15%

Measures the quality and engagement of a creator's audience — combining community health signals with engagement performance metrics, normalized by tier and platform.

What We Analyze

  • Engagement rate relative to audience size and platform benchmarks
  • View-to-follower performance (are posts reaching their audience?)
  • Audience comment sentiment — are fans positive, negative, or hostile?
  • Platform-specific normalization (TikTok vs. Instagram vs. YouTube have very different benchmarks)
  • Tier-specific normalization across 6 tiers: nano, micro, mid, macro, mega, celebrity

Why It Matters

High-quality audiences mean real reach and genuine influence. Poor audience quality means wasted ad spend.

Sentiment

10%

Measures sentiment stability and audience reception. Combines creator content sentiment with how the audience responds — erratic swings or persistent negativity signal unpredictable risk.

What We Analyze

  • Sentiment analysis on every post caption using AI language models
  • Detection of extreme negativity spikes (posts with >80% negative sentiment)
  • Sentiment variance across all posts — erratic swings indicate instability
  • Audience sentiment trends and comment tone analysis

Why It Matters

Brands need predictable partners. A creator who frequently posts inflammatory or highly negative content creates risk even if individual posts aren't policy violations.

Community Trust

10%

Evaluates creator conduct and consistency — how they engage with their community and whether they maintain a reliable posting cadence that brands can depend on.

What We Analyze

  • Creator reply rate to comments (tier-normalized: mega-influencers aren't penalized for not replying to millions)
  • Reply quality — genuine engagement vs. generic 'thanks!' responses
  • Creator toxicity in responses — do they engage respectfully?
  • Posting cadence — how regular and predictable is their schedule?
  • Gap analysis — long unexplained absences signal unreliability

Why It Matters

A creator's community reflects their brand. Hostile audiences, disengaged creators, or inconsistent posting lead to negative brand association and unpredictable campaign delivery.

ROI Prediction

10%

Projects likely campaign return by combining engagement performance, content consistency, and growth trajectory. Helps brands estimate the value of a partnership before committing budget.

What We Analyze

  • Engagement trend — growing, stable, or declining over recent posts
  • Content consistency and posting reliability for campaign predictability
  • Growth trajectory and momentum indicators
  • Historical engagement performance relative to similar creators

Why It Matters

High engagement and consistent growth mean the audience actively cares about the content. Declining engagement signals a creator whose influence — and your campaign ROI — is fading.

Weight Allocation

Content Risk
20%
Authenticity
20%
Brand Safety
15%
Audience Quality
15%
Sentiment
10%
Community Trust
10%
ROI Prediction
10%
Total
100%

Multi-Layered Analysis

CreatorScore combines multiple layers of AI analysis, each specialized for a different type of content and risk detection.

Natural Language Processing

Advanced AI language models analyze every caption, comment, and transcript for hate speech, sentiment, toxicity, and spam.

Computer Vision

Purpose-built vision models scan thumbnails and video frames for explicit, violent, or inappropriate visual content.

Text-on-Screen Detection

Optical character recognition reads text overlaid on images and videos to catch hidden messages not in the caption or audio.

Speech-to-Text

Audio transcription analyzes what creators actually say in videos, not just what they write in captions.

Contextual AI Review

A large language model reviews all findings in context — understanding niche, tone, and intent to minimize false positives.

Explainable Scoring

Every score comes with a transparent breakdown so brands and legal teams can see exactly which factors raised or lowered it.

Fair Scoring Across All Creator Sizes

A nano-influencer with 5,000 followers and a celebrity with 10 million followers operate in completely different realities. CreatorScore uses tier-based normalization so every creator is scored against benchmarks appropriate for their size and platform.

Engagement Rate

A 2% rate is excellent for a mega-influencer but below average for a nano-creator. Both are scored fairly.

Reply Rate

Mega-influencers can't reply to millions of comments. We weight reply quality over quantity at scale.

Niche Context

Comedy creators aren't penalized for casual language the same way family creators would be.

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