# TikTok Email Scraper (`datapilot/tiktok-email-scraper`) Actor TikTok Email Finder Actor searches Google and DuckDuckGo to discover TikTok profiles with public emails. It extracts usernames, profile links, emails, titles, and descriptions from search results. Supports keyword-based searches, proxies, and outputs structured JSON datasets. - **URL**: https://apify.com/datapilot/tiktok-email-scraper.md - **Developed by:** [Data Pilot](https://apify.com/datapilot) (community) - **Categories:** Other, Developer tools - **Stats:** 9 total users, 1 monthly users, 100.0% 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 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 ## 🚀 TikTok Email Scraper (Apify Actor) A high-performance **TikTok Email Scraper** that leverages advanced search techniques (Search Dorks) on Google and DuckDuckGo to collect emails and profile data from TikTok creators based on specific keywords. It is a powerful tool for influencer marketing, talent scouting, and lead generation. --- ### 🌟 Key Features * **Multi-Engine Search:** Simultaneously utilizes Google Search and DuckDuckGo (HTML) to ensure maximum data coverage and accuracy. * **Smart Email Extraction:** Scrapes emails (e.g., `@gmail.com`, `@yahoo.com`, `@hotmail.com`, and custom domains) directly from profile bios and search snippets. * **Bot Detection Bypass:** Uses **Playwright** with **Residential Proxies** and custom **User-Agents** to avoid IP blocking and CAPTCHAs. * **Automated De-duplication:** Automatically filters out duplicate usernames to ensure your final dataset contains only unique leads. * **Apify Integration:** Run seamlessly on the Apify platform and export collected data in JSON, CSV, or Excel formats. --- ### 🛠 Tech Stack * **Language:** Python 3.11+ * **Frameworks:** [Apify SDK](https://docs.apify.com/sdk/python/), [Playwright](https://playwright.dev/python/) * **Search Methods:** Advanced Google Dorks, DuckDuckGo HTML Scraper * **Concurrency:** Asyncio (Asynchronous processing for high efficiency) --- ### 🚀 How to Use #### 1. Input Configuration Configure the following parameters in the Apify dashboard: | Field | Type | Description | Example | | :--- | :--- | :--- | :--- | | `keywords` | String | Comma-separated niches/keywords | `beauty, fitness, tech` | | `maxResults` | Integer | Maximum profiles per keyword | `50` | | `useApifyProxy` | Boolean | Enable/Disable proxy usage | `true` | #### 2. Run and Output Once the Actor completes its run, it provides data in the following format: * **Username:** The TikTok handle/creator name. * **Email:** The contact email address found. * **Profile URL:** Direct link to the TikTok profile. * **Title & Description:** Snippet data from search results providing bio context. --- # Actor input Schema ## `keywords` (type: `string`): Enter keywords to search for (comma-separated) ## `maxResults` (type: `integer`): Maximum number of profiles per keyword (default: 20) ## `useApifyProxy` (type: `boolean`): Recommended to avoid blocks ## `apifyProxyGroups` (type: `array`): Select proxy type ## Actor input object example ```json { "keywords": "fashion, beauty, fitness, tech", "maxResults": 20, "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } ```` # 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 = { "keywords": "fashion, beauty, fitness, tech" }; // Run the Actor and wait for it to finish const run = await client.actor("datapilot/tiktok-email-scraper").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 = { "keywords": "fashion, beauty, fitness, tech" } # Run the Actor and wait for it to finish run = client.actor("datapilot/tiktok-email-scraper").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 '{ "keywords": "fashion, beauty, fitness, tech" }' | apify call datapilot/tiktok-email-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=datapilot/tiktok-email-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "TikTok Email Scraper", "description": "TikTok Email Finder Actor searches Google and DuckDuckGo to discover TikTok profiles with public emails. It extracts usernames, profile links, emails, titles, and descriptions from search results. Supports keyword-based searches, proxies, and outputs structured JSON datasets.", "version": "0.0", "x-build-id": "qsoh62B66U91nvvzE" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/datapilot~tiktok-email-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-datapilot-tiktok-email-scraper", "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/datapilot~tiktok-email-scraper/runs": { "post": { "operationId": "runs-sync-datapilot-tiktok-email-scraper", "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/datapilot~tiktok-email-scraper/run-sync": { "post": { "operationId": "run-sync-datapilot-tiktok-email-scraper", "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": [ "keywords" ], "properties": { "keywords": { "title": "Search Keywords", "type": "string", "description": "Enter keywords to search for (comma-separated)" }, "maxResults": { "title": "Max Results per Keyword", "minimum": 1, "maximum": 100, "type": "integer", "description": "Maximum number of profiles per keyword (default: 20)", "default": 20 }, "useApifyProxy": { "title": "Use Apify Proxy", "type": "boolean", "description": "Recommended to avoid blocks", "default": true }, "apifyProxyGroups": { "title": "Proxy Groups", "uniqueItems": true, "type": "array", "description": "Select proxy type", "items": { "type": "string", "enum": [ "RESIDENTIAL", "DATACENTER" ] }, "default": [ "RESIDENTIAL" ] } } }, "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 } } } } } } } } } } ```