# Instagram Fake Follower Checker & Audience Audit (`seemuapps/instagram-fake-follower-auditor`) Actor Check any Instagram account for fake followers - bot score, audience quality grade, and follower authenticity report from a real follower sample. - **URL**: https://apify.com/seemuapps/instagram-fake-follower-auditor.md - **Developed by:** [Andrew](https://apify.com/seemuapps) (community) - **Categories:** Social media, Lead generation, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $990.00 / 1,000 audit reports This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Instagram Fake Follower Checker & Audience Audit Audit any public Instagram account for fake followers and bots. The actor samples the account's real follower list, scores every sampled follower on proven bot signals, and returns a fake-follower percentage, an audience quality grade, and the full per-follower evidence — the same audit influencer-marketing platforms charge hundreds per month for. ### What you get **One audit summary:** - **Fake follower %** and **suspicious %** from a real follower sample (not an estimate) - **Audience quality grade** (A–D) - **Signal breakdown** — how many sampled followers had no profile picture, zero posts, suspicious follow ratios, spammy handles, or empty bios - **Sample statistics** — private %, verified count, average follower/following counts across the sample **Plus one record per sampled follower:** - Bot score (0–100) with a `likely_fake` / `suspicious` / `likely_real` verdict - The exact flags that fired, follower/following/post counts, and profile URL - Optional **deep check**: each follower's account creation date — brand-new accounts are the strongest bot signal Export everything to JSON, CSV, Excel, or Google Sheets from the Apify console. ### Use cases - **Influencer vetting** — check an influencer's audience authenticity before signing a sponsorship deal - **Agency audits** — screen creator rosters and report audience quality to clients - **Brand safety** — verify partner accounts aren't inflated with purchased followers - **Competitor benchmarking** — compare audience quality across accounts in your niche - **Account purchase due diligence** — know how much of an audience is real before buying ### How it works The actor samples up to 1,000 real followers of the target account and scores each one on bot signals: missing profile picture, zero posts, follow-ratio anomalies, machine-generated handle patterns, empty bios, and (with deep check) account age under 6 months. The fake-follower percentage is the share of sampled followers scoring as likely fake. ### How to use 1. Enter the Instagram **username** to audit (with or without @) 2. Choose a **sample size** (200 is a good default; 500+ for large accounts) 3. Optionally enable **Deep check** for account-age verification 4. Run the actor — the summary is the first record in the **Dataset** tab, follower evidence follows ### Output format Summary record: ```json { "recordType": "summary", "auditedUsername": "example", "followerCount": 152340, "sampleSize": 200, "fakeFollowerPct": 18.5, "suspiciousPct": 12.0, "qualityGrade": "B", "signalBreakdown": { "no_profile_picture": 31, "zero_posts": 44, "suspicious_follow_ratio": 19 }, "sampleStats": { "privatePct": 41.5, "verifiedCount": 2, "noPostsPct": 22.0, "anonymousAvatarPct": 15.5, "avgFollowers": 412, "avgFollowing": 890 } } ```` Follower record: ```json { "recordType": "follower", "auditedUsername": "example", "followerUsername": "user12345678", "botScore": 65, "verdict": "likely_fake", "flags": ["no_profile_picture", "zero_posts", "spammy_handle"], "followers": 3, "following": 1840, "posts": 0, "profileUrl": "https://www.instagram.com/user12345678/" } ``` ### Free tier limits Runs on a free Apify plan are limited to: - **20 followers sampled per run** (the sample size input is capped automatically) - **3 runs per day** - **10 minutes between runs** Upgrade to any paid Apify plan to remove all three limits and audit with full sample sizes. ### FAQ **Do I need to log in or provide cookies?** No. No login, no cookies, no risk to your own account. **How accurate is it?** The sample is drawn from the account's live follower list. At 200 followers the fake percentage is typically within a few points of a full audit; increase the sample size for tighter confidence. # Actor input Schema ## `username` (type: `string`): The public Instagram account to audit (with or without @). ## `sampleSize` (type: `integer`): How many followers to sample and score. Larger samples give more accurate fake-follower percentages. Max 1000. ## `deepCheck` (type: `boolean`): Also check each sampled follower's account creation date - brand-new accounts are a strong bot signal. Slower and billed as an extra event per follower. ## Actor input object example ```json { "username": "natgeo", "sampleSize": 200, "deepCheck": false } ``` # Actor output Schema ## `results` (type: `string`): One summary record (recordType 'summary': fakeFollowerPct, suspiciousPct, qualityGrade, signalBreakdown, sampleStats) followed by one record per sampled follower (recordType 'follower': followerUsername, botScore, verdict, flags, followers, following, posts, accountCreated). # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "username": "natgeo" }; // Run the Actor and wait for it to finish const run = await client.actor("seemuapps/instagram-fake-follower-auditor").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "username": "natgeo" } # Run the Actor and wait for it to finish run = client.actor("seemuapps/instagram-fake-follower-auditor").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "username": "natgeo" }' | apify call seemuapps/instagram-fake-follower-auditor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=seemuapps/instagram-fake-follower-auditor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Instagram Fake Follower Checker & Audience Audit", "description": "Check any Instagram account for fake followers - bot score, audience quality grade, and follower authenticity report from a real follower sample.", "version": "1.0", "x-build-id": "7JDeLwyfLH0rjX81l" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/seemuapps~instagram-fake-follower-auditor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-seemuapps-instagram-fake-follower-auditor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/seemuapps~instagram-fake-follower-auditor/runs": { "post": { "operationId": "runs-sync-seemuapps-instagram-fake-follower-auditor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/seemuapps~instagram-fake-follower-auditor/run-sync": { "post": { "operationId": "run-sync-seemuapps-instagram-fake-follower-auditor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "username" ], "properties": { "username": { "title": "Username", "type": "string", "description": "The public Instagram account to audit (with or without @)." }, "sampleSize": { "title": "Follower sample size", "minimum": 10, "maximum": 1000, "type": "integer", "description": "How many followers to sample and score. Larger samples give more accurate fake-follower percentages. Max 1000.", "default": 200 }, "deepCheck": { "title": "Deep check (account age)", "type": "boolean", "description": "Also check each sampled follower's account creation date - brand-new accounts are a strong bot signal. Slower and billed as an extra event per follower.", "default": false } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```