# Twitter/X Leads Scraper - Profile Email Finder (`oriented_wallpaper/twitter-leads-scraper`) Actor Turn X/Twitter searches, hashtags, or handles into contactable author leads โ€” collapse tweets to unique profiles, extract emails from bios + linked sites, follower/verified filters, 0-100 lead score, dedupe, and AI cold openers. Export CSV/JSON. - **URL**: https://apify.com/oriented\_wallpaper/twitter-leads-scraper.md - **Developed by:** [Flash Scrape](https://apify.com/oriented_wallpaper) (community) - **Categories:** Lead generation, Social media, AI - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $14.00 / 1,000 leads This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 ## Twitter/X Leads Scraper - Profile Email Finder **Turn X/Twitter into a contactable lead list.** Give it search terms, hashtags, or @handles and get back **unique author profiles** โ€” each with their **email** (from the bio *and* the linked website), follower count, verified status, a 0โ€“100 lead score, and an optional AI cold-email opener. Export to CSV, JSON, or Excel. Built for **B2B cold email, creator/founder outreach, agencies, and recruiting** โ€” clean, scored, contactable data, not just a raw tweet dump. --- ### Why this scraper - ๐Ÿงต **Tweets โ†’ unique authors** โ€” searches return tweets, but you want *people*. This actor collapses every matching tweet to **one row per author handle** (a two-pass roll-up), so you get profiles, not a noisy timeline. - ๐Ÿ“ง **Two-source email finding** โ€” scans each author's **bio** for inline emails *and* visits their **bio-link website** (home / contact / about) to pull the real email + other socials. - ๐ŸŽฏ **Quality filters** โ€” min/max followers (target nano โ†’ macro tiers), verified-only, has-website, has-email. You only pay for leads you keep. - ๐Ÿงฎ **0โ€“100 lead score** โ€” ranks by email + website + followers fit + verified + engagement (when available); best leads first. - ๐Ÿงน **Auto-dedupe** โ€” same handle / website / email collapses to one row. - โœ๏ธ **AI cold-opener (optional)** โ€” a personalized one-line icebreaker per lead, written with **your own** OpenAI/Anthropic key. --- ### Use cases - Build an outreach list of **founders / creators tweeting about your niche** (search a topic, get their emails). - Turn a **hashtag** (e.g. `#buildinpublic`) into a list of contactable builders. - Pull a competitor's or community's **handles** into emails + CSV for your CRM. --- ### Input Provide at least one of `searchTerms`, `hashtags`, or `handles`. ```json { "searchTerms": ["ai founder", "shopify agency"], "hashtags": ["buildinpublic"], "handles": ["naval"], "maxItems": 200, "minFollowers": 1000, "onlyWithEmail": true, "enrichEmails": true, "writeOpeners": false } ```` | Field | What it does | |---|---| | **searchTerms** | Keywords / phrases to search X/Twitter for. | | **hashtags** | Hashtags (with or without `#`); treated as extra search terms. | | **handles** | Specific @handles or profile URLs to pull tweets from. | | **maxItems** | How many tweets to scan before rolling up to unique authors. | | **minFollowers / maxFollowers** | Target a follower band (0 = off). | | **onlyVerified** | Keep only verified accounts. | | **onlyWithWebsite / onlyWithEmail** | Keep only authors with a website / an email. | | **enrichEmails** | Visit the bio-link site to find emails + socials (default on). | | **writeOpeners** | Add an AI cold-opener per lead โ€” needs your OpenAI/Anthropic key. | *** ### Output (one row per unique author, best leads first) ```json { "lead_score": 90, "handle": "somefounder", "name": "Some Founder", "bio": "Building an AI tool for agencies. Ex-Google. hello@somestartup.com", "followers": 24800, "email": "hello@somestartup.com", "email_status": "found", "website": "https://somestartup.com", "verified": true, "icebreaker": "Your take on AI tooling for agencies really resonatedโ€ฆ", "profile_url": "https://x.com/somefounder" } ``` Results render as a **clean sortable table** on the Output tab, and export to CSV / JSON / Excel. *** ### How it works 1. Searches X/Twitter via [`apidojo/tweet-scraper`](https://apify.com/apidojo/tweet-scraper) for your terms / hashtags / handles. 2. **Collapses the returned tweets to unique author profiles** (dedupe by handle), averaging engagement across each author's tweets when that data is present. 3. Reads each bio for inline emails and visits the bio-link website for more. 4. Dedupes, applies your filters, and scores every lead 0โ€“100. 5. *(Optional)* Writes a personalized cold-email opener per lead with your AI key. 6. Pushes a clean dataset โ†’ export CSV / JSON / Excel. *** ### FAQ **Why authors instead of tweets?** Search results are tweets, but for outreach you want the *person*. This actor rolls every matching tweet up to its author so you get a deduped list of contactable profiles. **Will every author have an email?** No. X exposes a public email for very few profiles; the rest depend on what's in the bio or on the linked site. Use `onlyWithEmail` to keep just the contactable ones. **Do I need an API key?** Only for AI openers, and it's your key (used once per run, never stored). **Is this legal?** It scrapes **public** profile data. Use it for legitimate outreach and follow X's terms and anti-spam laws (CAN-SPAM / GDPR) in your messaging. # Actor input Schema ## `searchTerms` (type: `array`): Keywords / phrases to search X/Twitter for. Tweets matching these are collapsed to their unique author profiles. e.g. 'ai founder', 'shopify agency'. ## `hashtags` (type: `array`): Hashtags to search (with or without the #). Treated as additional search terms. e.g. 'buildinpublic', 'saas'. ## `handles` (type: `array`): Specific @handles / profile URLs to pull tweets from (one per line). e.g. 'naval' or 'https://x.com/naval'. ## `maxItems` (type: `integer`): How many tweets to fetch before rolling them up to unique authors. More tweets = more unique authors discovered. ## `minFollowers` (type: `integer`): Keep only authors with at least this many followers (0 = no filter). ## `maxFollowers` (type: `integer`): Keep only authors with at most this many followers (0 = no cap). Use with min to target nano/micro/macro tiers. ## `onlyVerified` (type: `boolean`): Keep only verified profiles. ## `onlyWithWebsite` (type: `boolean`): Drop authors with no bio link (you can't enrich an email without one). ## `onlyWithEmail` (type: `boolean`): Drop authors where no email could be found. Best for cold-email campaigns. ## `enrichEmails` (type: `boolean`): Visit each author's bio-link website (home + /contact + /about) to extract emails and social links. Higher email fill-rate. No key needed. ## `writeOpeners` (type: `boolean`): Generate a personalized one-sentence outreach opener for each lead. Requires your OpenAI/Anthropic key below. ## `llmProvider` (type: `string`): Which model writes the openers. ## `llmApiKey` (type: `string`): Your own OpenAI or Anthropic API key. Used only to write the openers; never stored. ## `llmModel` (type: `string`): Override the default model (gpt-4o-mini for OpenAI, claude-haiku-4-5-20251001 for Anthropic). ## Actor input object example ```json { "searchTerms": [ "growth marketer" ], "hashtags": [ "saas" ], "handles": [ "naval" ], "maxItems": 200, "minFollowers": 0, "maxFollowers": 0, "onlyVerified": false, "onlyWithWebsite": false, "onlyWithEmail": false, "enrichEmails": true, "writeOpeners": false, "llmProvider": "openai" } ``` # 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 = { "searchTerms": [ "ai founder", "saas indie hacker" ], "hashtags": [ "buildinpublic" ] }; // Run the Actor and wait for it to finish const run = await client.actor("oriented_wallpaper/twitter-leads-scraper").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 = { "searchTerms": [ "ai founder", "saas indie hacker", ], "hashtags": ["buildinpublic"], } # Run the Actor and wait for it to finish run = client.actor("oriented_wallpaper/twitter-leads-scraper").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 '{ "searchTerms": [ "ai founder", "saas indie hacker" ], "hashtags": [ "buildinpublic" ] }' | apify call oriented_wallpaper/twitter-leads-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=oriented_wallpaper/twitter-leads-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Twitter/X Leads Scraper - Profile Email Finder", "description": "Turn X/Twitter searches, hashtags, or handles into contactable author leads โ€” collapse tweets to unique profiles, extract emails from bios + linked sites, follower/verified filters, 0-100 lead score, dedupe, and AI cold openers. Export CSV/JSON.", "version": "0.1", "x-build-id": "jRlZpWe08GUiteOOr" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/oriented_wallpaper~twitter-leads-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-oriented_wallpaper-twitter-leads-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/oriented_wallpaper~twitter-leads-scraper/runs": { "post": { "operationId": "runs-sync-oriented_wallpaper-twitter-leads-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/oriented_wallpaper~twitter-leads-scraper/run-sync": { "post": { "operationId": "run-sync-oriented_wallpaper-twitter-leads-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "searchTerms": { "title": "Search terms", "type": "array", "description": "Keywords / phrases to search X/Twitter for. Tweets matching these are collapsed to their unique author profiles. e.g. 'ai founder', 'shopify agency'.", "items": { "type": "string" } }, "hashtags": { "title": "Hashtags", "type": "array", "description": "Hashtags to search (with or without the #). Treated as additional search terms. e.g. 'buildinpublic', 'saas'.", "items": { "type": "string" } }, "handles": { "title": "Handles", "type": "array", "description": "Specific @handles / profile URLs to pull tweets from (one per line). e.g. 'naval' or 'https://x.com/naval'.", "items": { "type": "string" } }, "maxItems": { "title": "Max tweets to scan", "minimum": 1, "type": "integer", "description": "How many tweets to fetch before rolling them up to unique authors. More tweets = more unique authors discovered.", "default": 200 }, "minFollowers": { "title": "Minimum followers", "minimum": 0, "type": "integer", "description": "Keep only authors with at least this many followers (0 = no filter).", "default": 0 }, "maxFollowers": { "title": "Maximum followers", "minimum": 0, "type": "integer", "description": "Keep only authors with at most this many followers (0 = no cap). Use with min to target nano/micro/macro tiers.", "default": 0 }, "onlyVerified": { "title": "Only verified accounts", "type": "boolean", "description": "Keep only verified profiles.", "default": false }, "onlyWithWebsite": { "title": "Only authors with a website in bio", "type": "boolean", "description": "Drop authors with no bio link (you can't enrich an email without one).", "default": false }, "onlyWithEmail": { "title": "Only authors with an email", "type": "boolean", "description": "Drop authors where no email could be found. Best for cold-email campaigns.", "default": false }, "enrichEmails": { "title": "Find emails on the linked website", "type": "boolean", "description": "Visit each author's bio-link website (home + /contact + /about) to extract emails and social links. Higher email fill-rate. No key needed.", "default": true }, "writeOpeners": { "title": "Write an AI cold-opener per lead", "type": "boolean", "description": "Generate a personalized one-sentence outreach opener for each lead. Requires your OpenAI/Anthropic key below.", "default": false }, "llmProvider": { "title": "AI provider (for openers)", "enum": [ "openai", "anthropic" ], "type": "string", "description": "Which model writes the openers.", "default": "openai" }, "llmApiKey": { "title": "AI API key (for openers)", "type": "string", "description": "Your own OpenAI or Anthropic API key. Used only to write the openers; never stored." }, "llmModel": { "title": "AI model (optional)", "type": "string", "description": "Override the default model (gpt-4o-mini for OpenAI, claude-haiku-4-5-20251001 for Anthropic)." } } }, "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 } } } } } } } } } } ```