Instagram Influencer Audit — Fake Followers, Engagement & Authenticity

Vet a creator before you pay them. Give Instagram handles and get back the full agency due-diligence brief for each: engagement benchmarked to the creator's follower tier, fake-follower / bought-engagement signals, audience & activity quality, brand-safety flags, a fair rate-card estimate, and composite authenticity + influence scores with a verdict. It runs Instagram's data scraper under the hood — you just pass handles. Agent-callable.

Built for influencer-marketing agencies, brand partnerships, and creator-marketplace teams who lose budget to inflated followers and bought engagement.

Tiers (opt-in — deep layers only run when you ask)

• Profile (default, fast): tier-benchmarked engagement, fake-follower signals, audience/structure sanity, brand safety, rate card, authenticity + influence scores.
• Comment forensics (deepComments: true): scrapes recent-post comments and grades real engagement authenticity — % bot/spam/duplicate/emoji-only comments, script-mismatch (bot-farm signal), commenter red flags → a commentAuthenticityScore blended into the verdict. (Runs a second scraper; takes longer — best for scheduled/webhook use.)
• Growth tracking (historyStore, optional): pick (or create) a key-value store and each run saves a snapshot per creator and flags anomalies over time: followers up while engagement is flat = possible purchased followers, engagement decay, posting stall (a stateless agent can't do this). Schedule it on your roster (e.g. weekly). The Actor only gets access to the store you pick.

Authenticity signals computed from public creator/business-account data for due diligence — not a definitive accusation that a creator is fraudulent, and estimates (rate card, benchmarks) are indicative. Verify before contracting.

What makes it deep (vs the single-purpose tools)

Most tools do one thing — just a fake-follower %, or just an engagement number. This composes the whole picture:

Dimension	What it computes
Engagement vs tier	ER = (avg likes+comments)/followers, scored against the real norm for the creator's tier (nano→global) — a 0.03% ER is a red flag for a micro creator but normal for a 100M brand
Fake / bought signals	engagement-vs-benchmark mismatch (inflated followers), like-to-comment ratio extremes (bought likes / comment pods), engagement consistency (CV — spiky = bought spikes, too-uniform = bots), video view-to-like sanity
Audience & structure	follower/following ratio, followers-vs-post-count sanity, verified/established gating (so legit big brands aren't false-flagged)
Activity	posting cadence, days since last post (dormant), sponsored-post % (ad fatigue)
Brand safety & fit	risky-content flags (adult/gambling/crypto-promo/…); optional brand-keyword fit
Commercials	estimated fair rate card (USD range) from tier × engagement
Verdict	authenticityScore + influenceScore + a signal (authentic-healthy / authentic-modest / inflated-followers-suspected / authenticity-concerns / brand-safety-review) + findings

Why not just ask an AI agent?

An agent can open a profile. It can't fetch a creator's recent posts, benchmark engagement to the correct follower-tier norm, run the bought-engagement forensics (ratios, variance, view-to-like), and roll it into a defensible score — deterministically, for a roster of creators. That encoded influencer-marketing expertise + scale is the product.

Input

Field	Type	Required	Description
usernames	array of strings	✅*	Handles or profile URLs.
brandKeywords	array of strings		Topics for a brand-fit read.
datasetId	dataset	✅*	…or read handles from another dataset (e.g. a discovery run).
historyStore	key-value store		Your store for growth tracking across runs.
maxCreators	integer		Cap per run (default 20).
webhookUrl	string		POST the audits here as JSON.

* provide usernames or datasetId.

Example input

{ "usernames": ["natgeo", "nike"], "brandKeywords": ["travel", "wildlife"] }

Output

One audit per creator. Example (real run, trimmed):

{
  "username": "natgeo",
  "verified": true,
  "tier": "global",
  "followersCount": 268351305,
  "engagementRatePct": 0.034,
  "engagementVsBenchmark": 0.28,
  "avgLikes": 92226, "avgComments": 266, "likeToCommentRatio": 347.0,
  "engagementConsistencyCv": 1.34,
  "sponsoredPostPct": 17,
  "cadence": {"avgDaysBetweenPosts": 3.4, "daysSinceLastPost": 0.1},
  "authenticityScore": 57, "influenceScore": 73,
  "estimatedRateCardUsd": {"low": 483032, "mid": 805054, "high": 1207581, "currency": "USD"},
  "signal": "authentic-modest-engagement",
  "findings": [
    {"severity": "low", "code": "below-benchmark-engagement", "detail": "Engagement 0.03% is low vs the global-tier norm (0.12%)..."}
  ]
}

An unverified account with followers far above its engagement returns inflated-followers-suspected; risky content returns brand-safety-review.

Use cases

• Pre-deal vetting — confirm a creator's audience and engagement are real before signing.
• Roster screening — audit a whole shortlist (or a discovery dataset) in one run and rank by authenticity.
• Rate negotiation — an independent fair-price estimate.
• Brand safety — flag risky content and ad-fatigue before association.
• AI agents — a structured creator-DD verdict to gate outreach.

Ready-made tasks

• Instagram Fake Follower Check
• Instagram Engagement Rate Calculator by Follower Tier

For AI agents / MCP

https://mcp.apify.com/?tools=actors,docs,centorprat/instagram-influencer-audit

Loads as centorprat--instagram-influencer-audit. Claude Code:

claude mcp add apify -- npx -y @apify/actors-mcp-server --actors centorprat/instagram-influencer-audit

FAQ

Where does the data come from? Public Instagram profiles + recent posts, via Apify's Instagram Profile Scraper (run under the hood). You don't manage any key.

Private accounts? Reported as private-account — public metrics only; posts can't be audited.

Does it accuse creators of fraud? No — it surfaces authenticity signals for your review. Legit verified/large accounts are gated so they aren't false-flagged for naturally low engagement.

How is engagement "benchmarked"? Against typical ranges for the creator's follower tier, because a healthy ER for a micro creator (~2.5%) is very different from a 100M brand (~0.1%).

How are the follower/engagement forensics done? Ratios and variance across recent posts (like-to-comment, engagement consistency, view-to-like) that diverge when followers or engagement are bought.

Cost? Pay-per-event per creator audited, plus the underlying scraper usage billed to your run: a fraction of a cent per profile. With deepComments, Apify's Instagram Comment Scraper also runs, which adds roughly $0.10-0.12 per creator on the Free plan (about 45 comments from 3 posts; less with commentPostsSampled: 1 or on paid plans).

Data & attribution

Public Instagram data via Apify's Instagram Profile Scraper. Signals for influencer due diligence, not accusations or guarantees; rate-card and benchmark figures are indicative.

Related actors

• TikTok Influencer Audit
• YouTube Influencer Audit
• Google Maps Leads & Website Audit
• Shopify Store Leads & App Audit