# Brand Username and Impersonation Monitor (`thescrapelab/brand-username-profile-monitor`) Actor Monitor brand names, usernames, and public handles across social media, developer sites, forums, marketplaces, and other public websites. Use it as a brand username checker to find public profile matches, username availability signals, and possible impersonation risks without cross-run state. - **URL**: https://apify.com/thescrapelab/brand-username-profile-monitor.md - **Developed by:** [Inus Grobler](https://apify.com/thescrapelab) (community) - **Categories:** Social media, Marketing, SEO tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 1 bookmarks - **User rating**: No ratings yet ## Pricing from $2.49 / 1,000 results 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 ## Brand Username and Impersonation Monitor Monitor brand usernames, public handles, social profiles, and possible impersonation matches across public platforms with Sherlock and Maigret-powered checks. Use it as a brand username checker for current-run public profile review. ### What this Actor does Brand Username and Impersonation Monitor checks brand names, product names, organization names, usernames, and public handles across social media, developer sites, forums, marketplaces, and other public platforms. It helps teams find public profile matches, review username availability signals, and surface possible impersonation signals without logging in to platforms or collecting private data. The Actor combines Sherlock username checker style discovery with Maigret username search coverage where possible. It normalizes results, merges duplicates, assigns confidence scores, and adds review-focused risk labels for the current run. Use it as a brand username monitor, username availability checker, social media username checker, brand profile monitor, social profile finder, username search tool, public profile monitor, brand protection tool, Apify username checker, Sherlock username checker, or Maigret username search workflow. ### Who this is for - Startups preparing a product launch - SaaS companies monitoring brand handles - E-commerce brands checking public marketplaces and profiles - Creators monitoring public creator handles - Agencies managing brand protection for clients - Brand protection teams reviewing possible impersonation - Trademark teams checking public profile use - Reputation monitoring teams tracking public brand mentions in usernames - Product launch teams checking username availability before release ### Use cases - Check if a brand username is already used - Review public profile matches for a current brand check - Find public profile matches across platforms - Surface possible brand impersonation signals for review - Check username availability before a product launch - Monitor product names and creator handles - Build a current-run public profile review dataset ### How it works The Actor checks public platforms for the names you provide. It generates safe username variants, runs Sherlock as the fast primary engine, and uses Maigret for fallback, enrichment, and cross-checking with sensible defaults. Results are merged, deduplicated, and normalized into a clean default dataset for the current run. Each result includes the platform, public profile URL, confidence score, risk label, source engines, and whether both engines agreed. If a run succeeds but produces no profile matches, the Actor emits one diagnostic dataset item explaining the likely cause instead of leaving users with a blank output. The Actor does not write persistent state, named datasets, key-value store summaries, or feed records. ### Input - `brandNames`: Brand names, product names, organization names, or usernames you own or are authorized to monitor. - `scanMode`: Scan level. Choose `fast`, `balanced`, or `deep`. - `sources`: Broad source groups to check, such as `social`, `developer`, `forums`, `marketplaces`, `video`, or `all`. - `includeVariants`: Generate common username variants. - `maxVariantsPerName`: Limit generated variants per brand name. - `maxSitesPerName`: Limit platform definitions checked per username variant. - `maxResults`: Stop after this many profile match records. - `proxyConfiguration`: Apify Proxy settings for public platform checks and lightweight verification. Default behavior: - Runs a balanced scan. - Checks common social, developer, forum, and marketplace platforms. - Generates safe username variants automatically. - Returns found or review-worthy results. - Uses Apify Proxy by default. - Caps platform checks and output size to keep broad scans practical. - Does not write persistent monitoring state, named storage, or key-value store artifacts. ### Output Example output item: ```json { "recordType": "profile_match", "searchedName": "openclaw", "searchedVariant": "openclaw", "platform": "YouTube", "platformDomain": "youtube.com", "profileUrl": "https://www.youtube.com/@openclaw", "status": "found", "confidence": 0.92, "riskLevel": "review", "riskType": "possible_brand_match", "sourceEngines": ["sherlock", "maigret"], "engineAgreement": true, "scrapedAt": "2026-05-17T10:00:00.000Z" } ```` ### What confidence means High confidence means stronger evidence from engine agreement and/or lightweight public verification. Medium confidence means the result is likely relevant but should be reviewed. Low confidence means the match is weak, uncertain, unverified, or from a lower-relevance signal. ### What riskLevel means `riskLevel` is a prioritization label for brand review. It is not a legal conclusion and does not prove impersonation or infringement. `review` means a human should inspect the public profile before taking action. ### Sherlock and Maigret engine behavior Sherlock is used for fast username checks across supported public websites. Maigret is used for fallback, enrichment, and cross-checking. The Actor merges and normalizes results from both engines. When Sherlock and Maigret find the same public profile, engine agreement increases confidence. Scan levels: - `fast`: Uses Sherlock as the primary engine, with Maigret only as a fallback if Sherlock fails for a username. - `balanced`: Recommended default. Uses Sherlock first, then Maigret for sparse results, important gaps, and enrichment. - `deep`: Uses both Sherlock and Maigret for each checked variant. Keep `maxSitesPerName` practical when using `sources: ["all"]`. Source groups: - `all` - `social` - `video` - `developer` - `forums` - `marketplaces` - `gaming` - `crypto` - `music` - `blogging` ### Storage behavior The Actor only pushes current scan results and no-result diagnostics to the default dataset, which is the standard Apify result output. It does not create named datasets, write persistent state, save index stores, or write `RUN_SUMMARY`, `RSS_XML`, `JSON_FEED`, or debug records to the key-value store. ### Python API example ```python from apify_client import ApifyClient APIFY_TOKEN = "YOUR_APIFY_TOKEN" ACTOR_ID = "thescrapelab/brand-username-profile-monitor" client = ApifyClient(APIFY_TOKEN) run_input = { "brandNames": ["openclaw", "openclawai"], "scanMode": "balanced", "sources": ["social", "developer", "forums", "marketplaces"], "maxSitesPerName": 150, "maxResults": 250, "proxyConfiguration": {"useApifyProxy": True} } run = client.actor(ACTOR_ID).call(run_input=run_input) print("Current run results:") for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) ``` ### Limitations - This Actor checks public web results only. - It does not access private accounts or private data. - It does not log in to platforms. - It does not collect personal contact details. - It does not prove legal impersonation. - Some platforms may block automated checks or change their pages. - Some results may be false positives or false negatives. - Confidence scores are best-effort. - Human review is recommended for important brand decisions. ### Compliance note Users are responsible for using the data lawfully and respecting platform rules and applicable laws. This Actor is for brand monitoring, username availability checks, and public profile review for names and usernames you own or are authorized to monitor. ### FAQ #### Does this Actor check all social media platforms? No. It checks public platforms supported by the underlying username-check engines and the Actor defaults. Coverage can change as platforms change. #### Does it use Sherlock and Maigret? Yes. Sherlock is used as the fast primary engine. Maigret is used for fallback, enrichment, and cross-checking. #### Does it require login? No. The Actor does not use login credentials, cookies, or account sessions. #### Does it collect private data? No. It is designed for public profile matching only and does not collect private messages, hidden profile data, emails, phone numbers, or sensitive personal data. #### Can it monitor new profiles over time? It can be scheduled for repeated current-run checks, but it does not store cross-run state. Compare exported datasets in your own system if you need new or changed profile detection. #### Can it check username availability? Yes. It can help review public username availability signals by showing where matching public profiles are found. Availability should still be confirmed directly on the platform before business or legal decisions. #### Why are some results uncertain? Platforms may block automated requests, redirect profile URLs, return ambiguous pages, or change their public pages. Confidence scores reflect the available evidence and are best-effort. #### Can I export CSV, JSON, or Excel from Apify? Yes. Apify datasets can be exported in formats such as JSON, CSV, Excel, XML, RSS, and HTML from the dataset export options. #### Can I schedule it? Yes. You can schedule repeat runs in Apify to monitor public profile matches over time. #### Is this a legal impersonation detector? No. It identifies possible brand matches and possible impersonation signals for human review. It does not make legal conclusions. ### Start monitoring Run the Actor with your brand names or usernames to start monitoring public profile matches across platforms. # Actor input Schema ## `brandNames` (type: `array`): Enter brand names, product names, or usernames you own or are authorized to monitor. ## `scanMode` (type: `string`): Choose how thoroughly to search. Fast is quickest, balanced is the recommended default, and deep checks more broadly. ## `sources` (type: `array`): Choose broad source groups to check. Use all for the broadest scan. ## `includeVariants` (type: `boolean`): Generate common username variants such as hyphenated, underscored, and dotted forms. ## `maxVariantsPerName` (type: `integer`): Limit generated username variants per supplied brand name. ## `maxSitesPerName` (type: `integer`): Limit how many platform definitions each engine checks for each username variant. ## `maxResults` (type: `integer`): Stop after this many profile match records to control run cost and dataset size. ## `proxyConfiguration` (type: `object`): Use Apify Proxy to improve access to public platforms that block datacenter traffic. ## Actor input object example ```json { "brandNames": [ "openclaw", "openclawai", "mybrand" ], "scanMode": "balanced", "sources": [ "social", "developer", "forums", "marketplaces" ], "includeVariants": true, "maxVariantsPerName": 4, "maxSitesPerName": 150, "maxResults": 250, "proxyConfiguration": { "useApifyProxy": true } } ``` # Actor output Schema ## `results` (type: `string`): Normalized profile matches from the current run. # 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 = { "brandNames": [ "openclaw", "openclawai", "mybrand" ] }; // Run the Actor and wait for it to finish const run = await client.actor("thescrapelab/brand-username-profile-monitor").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 = { "brandNames": [ "openclaw", "openclawai", "mybrand", ] } # Run the Actor and wait for it to finish run = client.actor("thescrapelab/brand-username-profile-monitor").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 '{ "brandNames": [ "openclaw", "openclawai", "mybrand" ] }' | apify call thescrapelab/brand-username-profile-monitor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=thescrapelab/brand-username-profile-monitor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Brand Username and Impersonation Monitor", "description": "Monitor brand names, usernames, and public handles across social media, developer sites, forums, marketplaces, and other public websites. Use it as a brand username checker to find public profile matches, username availability signals, and possible impersonation risks without cross-run state.", "version": "0.1", "x-build-id": "BiHBdgShLszpiZ22o" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/thescrapelab~brand-username-profile-monitor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-thescrapelab-brand-username-profile-monitor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/thescrapelab~brand-username-profile-monitor/runs": { "post": { "operationId": "runs-sync-thescrapelab-brand-username-profile-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/thescrapelab~brand-username-profile-monitor/run-sync": { "post": { "operationId": "run-sync-thescrapelab-brand-username-profile-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "brandNames" ], "properties": { "brandNames": { "title": "Brand names or usernames", "minItems": 1, "maxItems": 25, "type": "array", "description": "Enter brand names, product names, or usernames you own or are authorized to monitor.", "items": { "type": "string", "minLength": 1, "maxLength": 80 } }, "scanMode": { "title": "Scan level", "enum": [ "fast", "balanced", "deep" ], "type": "string", "description": "Choose how thoroughly to search. Fast is quickest, balanced is the recommended default, and deep checks more broadly.", "default": "balanced" }, "sources": { "title": "Sources to check", "uniqueItems": true, "type": "array", "description": "Choose broad source groups to check. Use all for the broadest scan.", "items": { "type": "string", "enum": [ "all", "social", "video", "developer", "forums", "marketplaces", "gaming", "crypto", "music", "blogging" ], "enumTitles": [ "All", "Social", "Video", "Developer", "Forums", "Marketplaces", "Gaming", "Crypto", "Music", "Blogging" ] }, "default": [ "social", "developer", "forums", "marketplaces" ] }, "includeVariants": { "title": "Include username variants", "type": "boolean", "description": "Generate common username variants such as hyphenated, underscored, and dotted forms.", "default": true }, "maxVariantsPerName": { "title": "Max variants per name", "minimum": 1, "maximum": 10, "type": "integer", "description": "Limit generated username variants per supplied brand name.", "default": 4 }, "maxSitesPerName": { "title": "Max platforms per username", "minimum": 25, "maximum": 1000, "type": "integer", "description": "Limit how many platform definitions each engine checks for each username variant.", "default": 150 }, "maxResults": { "title": "Max output records", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Stop after this many profile match records to control run cost and dataset size.", "default": 250 }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Use Apify Proxy to improve access to public platforms that block datacenter traffic.", "default": { "useApifyProxy": true } } } }, "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 } } } } } } } } } } ```