# Influencer Fake-Follower Risk Scorer (`kayhermes/influencer-fake-follower-risk-scorer`) Actor - **URL**: https://apify.com/kayhermes/influencer-fake-follower-risk-scorer.md - **Developed by:** [Khoa Nguyen](https://apify.com/kayhermes) (community) - **Categories:** Social media - **Stats:** 2 total users, 1 monthly users, 60.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## What's an Apify Actor? Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## Influencer Fake-Follower Risk Scorer Screen creator audience samples for synthetic or low-quality follower risk using account completeness, following ratios, account age, inactivity, usernames, duplicate avatars, comments, geography, language, growth spikes, and engagement anomalies. ### What it actually does Profile risk score, confidence, sample metrics, growth metrics, weighted signals, evidence and review recommendation. Supports inline JSON, an Apify Dataset, and public/signed JSON or CSV URLs. Source collection from social platforms is intentionally decoupled so users can connect any permitted scraper or export. ### Inputs Use `profiles[]`, `datasetId`, or `sourceUrls`. See `sample_input.json` and the Apify input schema for exact fields. ### Outputs - Default Dataset: detailed result rows and a run summary - `OUTPUT`: machine-readable run summary - `REPORT.html`: reviewable HTML report ### Limitations The result is a risk screen based on supplied samples, not proof that followers are fake or that misconduct occurred. The Actor analyzes supplied data; it does not bypass platform access controls or guarantee that upstream data is complete. ### Local validation ```bash npm ci npm test npm run check npm start ```` ### Deploy ```bash apify login apify push ``` Use `APIFY_TOKEN` from secure environment storage. Never commit credentials. # Actor input Schema ## `profiles` (type: `array`): Include profile metrics, optional followerGrowthHistory, and optional audienceSample records. ## `datasetId` (type: `string`): Configure source dataset id for this Actor. ## `sourceUrls` (type: `array`): Configure csv or json source urls for this Actor. ## `config` (type: `object`): Optional engagement benchmark and expected audience country/language overrides. ## `maxItems` (type: `integer`): Configure maximum profiles for this Actor. ## `webhookUrl` (type: `string`): Configure high-risk webhook for this Actor. ## Actor input object example ```json { "profiles": [ { "profileId": "creator-risk-1", "platform": "instagram", "followers": 500000, "engagementRate": 0.001, "expectedAudienceCountries": [ "US" ], "expectedAudienceLanguages": [ "en" ], "followerGrowthHistory": [ { "date": "2026-01-01", "followers": 100000 }, { "date": "2026-01-02", "followers": 200000 }, { "date": "2026-02-01", "followers": 210000 } ], "audienceSample": [ { "username": "abc12345678", "followers": 2, "following": 2400, "posts": 0, "hasProfilePhoto": false, "bio": "", "accountAgeDays": 15, "recentPosts": 0, "country": "ID", "language": "id", "avatarHash": "dup-a", "comments": [ "Nice", "Amazing" ] }, { "username": "xyz12345679", "followers": 1, "following": 1800, "posts": 0, "hasProfilePhoto": false, "bio": "", "accountAgeDays": 20, "recentPosts": 0, "country": "PH", "language": "tl", "avatarHash": "dup-a", "comments": [ "Nice", "Nice" ] } ] } ], "sourceUrls": [], "config": {}, "maxItems": 1000 } ``` # Actor output Schema ## `results` (type: `string`): No description ## `reportUrl` (type: `string`): No description ## `summaryUrl` (type: `string`): No description # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("kayhermes/influencer-fake-follower-risk-scorer").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/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("kayhermes/influencer-fake-follower-risk-scorer").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call kayhermes/influencer-fake-follower-risk-scorer --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=kayhermes/influencer-fake-follower-risk-scorer", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Influencer Fake-Follower Risk Scorer", "version": "2.0", "x-build-id": "OUpzjiXtuFNUa2olW" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/kayhermes~influencer-fake-follower-risk-scorer/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-kayhermes-influencer-fake-follower-risk-scorer", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/kayhermes~influencer-fake-follower-risk-scorer/runs": { "post": { "operationId": "runs-sync-kayhermes-influencer-fake-follower-risk-scorer", "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/kayhermes~influencer-fake-follower-risk-scorer/run-sync": { "post": { "operationId": "run-sync-kayhermes-influencer-fake-follower-risk-scorer", "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", "properties": { "profiles": { "title": "Creator profile records", "type": "array", "description": "Include profile metrics, optional followerGrowthHistory, and optional audienceSample records.", "default": [ { "profileId": "creator-risk-1", "platform": "instagram", "followers": 500000, "engagementRate": 0.001, "expectedAudienceCountries": [ "US" ], "expectedAudienceLanguages": [ "en" ], "followerGrowthHistory": [ { "date": "2026-01-01", "followers": 100000 }, { "date": "2026-01-02", "followers": 200000 }, { "date": "2026-02-01", "followers": 210000 } ], "audienceSample": [ { "username": "abc12345678", "followers": 2, "following": 2400, "posts": 0, "hasProfilePhoto": false, "bio": "", "accountAgeDays": 15, "recentPosts": 0, "country": "ID", "language": "id", "avatarHash": "dup-a", "comments": [ "Nice", "Amazing" ] }, { "username": "xyz12345679", "followers": 1, "following": 1800, "posts": 0, "hasProfilePhoto": false, "bio": "", "accountAgeDays": 20, "recentPosts": 0, "country": "PH", "language": "tl", "avatarHash": "dup-a", "comments": [ "Nice", "Nice" ] } ] } ] }, "datasetId": { "title": "Source Dataset ID", "type": "string", "description": "Configure source dataset id for this Actor." }, "sourceUrls": { "title": "CSV or JSON source URLs", "type": "array", "description": "Configure csv or json source urls for this Actor.", "default": [], "items": { "type": "string" } }, "config": { "title": "Benchmark overrides", "type": "object", "description": "Optional engagement benchmark and expected audience country/language overrides.", "default": {} }, "maxItems": { "title": "Maximum profiles", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Configure maximum profiles for this Actor.", "default": 1000 }, "webhookUrl": { "title": "High-risk webhook", "type": "string", "description": "Configure high-risk webhook for this Actor." } } }, "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 } } } } } } } } } } ```