# Social Analyzer - Find Profiles Across 900+ Sites (`anshumanatrey/social-analyzer`) Actor Cloud-hosted social-analyzer by qeeqbox. Find a username across 900+ social media and online platforms โ€” confidence scored, country/category tagged, metadata extracted. One dataset record per detected profile. $0.005 per record. - **URL**: https://apify.com/anshumanatrey/social-analyzer.md - **Developed by:** [Anshuman Atrey](https://apify.com/anshumanatrey) (community) - **Categories:** Developer tools, Automation - **Stats:** 4 total users, 2 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing $5.00 / 1,000 profile records 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 ## Social Analyzer โ€” Find Profiles Across 900+ Sites ๐Ÿ“ฆ **Open source ยท MIT:** [github.com/AnshumanAtrey/social-analyzer](https://github.com/AnshumanAtrey/social-analyzer) Cloud-hosted [qeeqbox/social-analyzer](https://github.com/qeeqbox/social-analyzer) โ€” find a username (a person, brand, or org) across **900+ social media and online platforms** in a single scan. Results stream as structured dataset records (one per detected profile) with confidence scoring, country, category, and extracted metadata. ### Quick start ```json { "username": "elonmusk", "mode": "fast", "top": 100 } ```` Scans the top 100 most popular sites in ~30-60 seconds. Each detected profile becomes a dataset record. ### Multiple usernames in one run ```json { "username": "elonmusk,jeffbezos,billgates", "mode": "fast", "top": 50 } ``` ### Comprehensive scan (top 500 sites + metadata) ```json { "username": "anshumanatrey", "mode": "slow", "top": 500, "metadata": true, "extract": true } ``` Takes a few minutes, but extracts page metadata (Open Graph, Twitter Card) and any URLs / patterns embedded in detected profile pages. ### Output structure Each scan produces multiple dataset records with a `recordType` discriminator: | recordType | Fields | When | |---|---|---| | `profile` | `username`, `platform`, `link`, `status`, `rate`, `country`, `language`, `type`, `rank`, `extracted`, `metadata`, `text` | One per detected profile | | `summary` | `username`, `mode`, `sitesChecked`, `profilesFound`, `duration`, `cmd` | Always last record | Filter by `recordType=profile` in the Apify Console table view to see only detections. ### Modes | Mode | What it does | When to use | |---|---|---| | `fast` | HTTP probing โ€” check if URL responds with a profile page | 90% of cases | | `slow` | Page analysis โ€” parse responses for OSINT signal | Deeper investigation | | `special` | Extra signal extraction (text/image/name analysis) | Most thorough | ### Confidence filter - `good` (default) โ€” only high-confidence matches (recommended for clean results) - `good,maybe` โ€” include uncertain matches - `all` โ€” every probe, including false positives ### Pricing $0.005 per dataset record. A typical fast scan of top 100 sites returns 5-15 detected profiles = $0.025-$0.075 per scan. ### Use cases - **Recruiters** โ€” verify a candidate's online presence before an interview - **Journalists** โ€” investigate subjects for stories - **Fraud teams** โ€” confirm or deny suspect identities - **HR background checks** โ€” verify professional accounts - **Dating safety** โ€” confirm someone is who they say they are - **OSINT investigators** โ€” case work on individuals or brands ### Authorization Only search usernames you're authorized to investigate. Some jurisdictions restrict OSINT collection on private individuals โ€” consult local law (GDPR in EU, CCPA in California, etc.). ### FAQ #### How accurate are the results? The `good` confidence filter (default) returns only high-signal matches โ€” these are reliable (~95%+ true positives). The `good,maybe` filter trades precision for recall โ€” useful when you want exhaustive coverage and don't mind manually checking some false positives. #### Why does a scan sometimes take 5+ minutes? `slow` mode parses each detected page for OSINT signals (emails, links, metadata). `special` mode runs OCR on profile images. If you just need a list of platforms, stick with `fast` mode โ€” usually under 60 seconds for top 100 sites. #### Can I scan a username only on platforms from certain countries? Yes โ€” pass a comma-separated list of two-letter country codes (`US,IN,JP`) and the actor will probe only sites registered to those countries. Useful for compliance investigations restricted to specific jurisdictions. #### Why does it sometimes miss LinkedIn? LinkedIn aggressively rate-limits anonymous probes โ€” confirmation of a profile often requires a logged-in scrape. For LinkedIn specifically, use a dedicated LinkedIn-scraper actor; social-analyzer's value is in the long tail of 900+ other platforms. #### How is this different from Sherlock / Maigret? Sherlock checks ~400 sites with binary "exists / doesn't exist." Social Analyzer adds: 900+ sites, confidence scoring (so you can trust the results), metadata extraction, country/category filtering, and parallel HTTP probing for speed. Maigret is the closest competitor โ€” Social Analyzer covers more sites but Maigret is faster for username-only queries. ### Pairs nicely with Bundle for full identity-resolution workflows: - **[Holehe Email OSINT](https://apify.com/anshumanatrey/holehe-email-osint)** โ€” Start with an email, find which sites it's registered on, then probe those usernames with Social Analyzer - **[theHarvester](https://apify.com/anshumanatrey/theharvester-osint)** โ€” Discover emails/people for a domain, then OSINT the names with Social Analyzer - **[NetIntel](https://apify.com/anshumanatrey/netintel)** โ€” Add WHOIS / GeoIP context to domains found in discovered profiles - **[Bug Bounty Finder](https://apify.com/anshumanatrey/bug-bounty-finder)** โ€” Audit your own brand's presence + check for active disclosure programs - **[nmap](https://apify.com/anshumanatrey/nmap-scanner)** โ€” Network recon for the technical-investigation half of OSINT - **[Zomato Restaurant Scraper](https://apify.com/anshumanatrey/zomato-restaurant-scraper)** โ€” Restaurant lead lists (separate B2B use case) ### Credits Built on [qeeqbox/social-analyzer](https://github.com/qeeqbox/social-analyzer) by Qeeqbox. AGPL-3.0 licensed per upstream. # Actor input Schema ## `username` (type: `string`): Username to investigate. Supports multiple comma-separated: johndoe,janedoe. ## `mode` (type: `string`): Fast = quick HTTP probing (~30s for top 100). Slow = page analysis. Special = extra OSINT signal extraction. ## `top` (type: `integer`): Scan the top N most popular sites. 100 = ~30s, 500 = ~3 min, 900 = full coverage. ## `websites` (type: `array`): Pick specific sites to scan. Leave empty to use 'How many sites' above. ## `countries` (type: `array`): Restrict to sites originating from these countries. Leave empty for global. ## `siteType` (type: `string`): Restrict to one site type. Leave on 'Any' for no filter. ## `method` (type: `string`): Found-only is cleanest. Use 'All' for debugging or research. ## `filter` (type: `string`): How sure should the detection be? ## `extract` (type: `boolean`): Pulls emails, links and other patterns from each detected profile. ## `metadata` (type: `boolean`): Adds page title, description, image, and language data per profile. ## `trim` (type: `boolean`): Cleaner output, recommended. ## `timeout` (type: `integer`): Hard kill the scanner after this many seconds. ## Actor input object example ```json { "username": "elonmusk", "mode": "fast", "top": 100, "siteType": "", "method": "find", "filter": "good", "extract": false, "metadata": true, "trim": true, "timeout": 1800 } ``` # Actor output Schema ## `detectedProfiles` (type: `string`): One record per discovered profile. Fields: username, platform (site name), link (full profile URL), status (good/maybe/bad), rate (% confidence), country, type (site category), language, rank, extracted (URLs/patterns if --extract), metadata (page metadata if --metadata). Final record has recordType=summary with counts and the executed command. # 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": "elonmusk" }; // Run the Actor and wait for it to finish const run = await client.actor("anshumanatrey/social-analyzer").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ 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); }); // ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ 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": "elonmusk" } # Run the Actor and wait for it to finish run = client.actor("anshumanatrey/social-analyzer").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "username": "elonmusk" }' | apify call anshumanatrey/social-analyzer --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=anshumanatrey/social-analyzer", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Social Analyzer - Find Profiles Across 900+ Sites", "description": "Cloud-hosted social-analyzer by qeeqbox. Find a username across 900+ social media and online platforms โ€” confidence scored, country/category tagged, metadata extracted. One dataset record per detected profile. $0.005 per record.", "version": "1.0", "x-build-id": "vh1FbNRTdN6jWEXju" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/anshumanatrey~social-analyzer/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-anshumanatrey-social-analyzer", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/anshumanatrey~social-analyzer/runs": { "post": { "operationId": "runs-sync-anshumanatrey-social-analyzer", "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/anshumanatrey~social-analyzer/run-sync": { "post": { "operationId": "run-sync-anshumanatrey-social-analyzer", "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(s)", "type": "string", "description": "Username to investigate. Supports multiple comma-separated: johndoe,janedoe." }, "mode": { "title": "Detection Mode", "enum": [ "fast", "slow", "special" ], "type": "string", "description": "Fast = quick HTTP probing (~30s for top 100). Slow = page analysis. Special = extra OSINT signal extraction.", "default": "fast" }, "top": { "title": "How many sites to scan", "minimum": 1, "maximum": 900, "type": "integer", "description": "Scan the top N most popular sites. 100 = ~30s, 500 = ~3 min, 900 = full coverage.", "default": 100 }, "websites": { "title": "Specific sites (optional, overrides 'How many')", "uniqueItems": true, "type": "array", "description": "Pick specific sites to scan. Leave empty to use 'How many sites' above.", "items": { "type": "string", "enum": [ "facebook", "instagram", "twitter", "tiktok", "youtube", "github", "gitlab", "linkedin", "reddit", "pinterest", "tumblr", "medium", "discord", "telegram", "snapchat", "twitch", "steam", "spotify", "soundcloud", "behance", "dribbble", "deviantart", "flickr", "vimeo", "vk", "ok", "naver", "weibo", "patreon", "wordpress", "blogger", "fandom", "etsy", "ebay", "amazon", "tradingview", "kaggle", "stackoverflow", "hackernews", "producthunt" ], "enumTitles": [ "Facebook", "Instagram", "Twitter / X", "TikTok", "YouTube", "GitHub", "GitLab", "LinkedIn", "Reddit", "Pinterest", "Tumblr", "Medium", "Discord", "Telegram", "Snapchat", "Twitch", "Steam", "Spotify", "SoundCloud", "Behance", "Dribbble", "DeviantArt", "Flickr", "Vimeo", "VK (Russia)", "OK.ru (Russia)", "Naver (Korea)", "Weibo (China)", "Patreon", "WordPress", "Blogger", "Fandom", "Etsy", "eBay", "Amazon", "TradingView", "Kaggle", "StackOverflow", "HackerNews", "ProductHunt" ] } }, "countries": { "title": "Filter by country (optional)", "uniqueItems": true, "type": "array", "description": "Restrict to sites originating from these countries. Leave empty for global.", "items": { "type": "string", "enum": [ "us", "in", "br", "ru", "cn", "jp", "kr", "de", "fr", "gb", "es", "it", "mx", "tr", "id", "vn", "th", "ph", "ae", "sa" ], "enumTitles": [ "United States", "India", "Brazil", "Russia", "China", "Japan", "Korea", "Germany", "France", "United Kingdom", "Spain", "Italy", "Mexico", "Turkey", "Indonesia", "Vietnam", "Thailand", "Philippines", "UAE", "Saudi Arabia" ] } }, "siteType": { "title": "Filter by site category (optional)", "enum": [ "", "Social Networks", "Music", "News", "Forum", "Adult", "Gaming", "Photo", "Video", "Blog", "Shopping", "Dating", "Professional" ], "type": "string", "description": "Restrict to one site type. Leave on 'Any' for no filter.", "default": "" }, "method": { "title": "What to return", "enum": [ "find", "get", "all" ], "type": "string", "description": "Found-only is cleanest. Use 'All' for debugging or research.", "default": "find" }, "filter": { "title": "Confidence threshold", "enum": [ "good", "good,maybe", "all" ], "type": "string", "description": "How sure should the detection be?", "default": "good" }, "extract": { "title": "Extract URLs & patterns from found profiles", "type": "boolean", "description": "Pulls emails, links and other patterns from each detected profile.", "default": false }, "metadata": { "title": "Extract page metadata (Open Graph, Twitter Card)", "type": "boolean", "description": "Adds page title, description, image, and language data per profile.", "default": true }, "trim": { "title": "Trim long text fields", "type": "boolean", "description": "Cleaner output, recommended.", "default": true }, "timeout": { "title": "Subprocess timeout (seconds)", "minimum": 60, "maximum": 3600, "type": "integer", "description": "Hard kill the scanner after this many seconds.", "default": 1800 } } }, "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 } } } } } } } } } } ```