# Twitter/X Leads Scraper - Profile Email Finder (`flash_scraper/twitter-leads-scraper`) Actor Turn X/Twitter searches, hashtags, or handles into contactable author leads — 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/flash\_scraper/twitter-leads-scraper.md - **Developed by:** [Flash Scrape](https://apify.com/flash_scraper) (community) - **Categories:** Lead generation, Social media, AI - **Stats:** 2 total users, 1 monthly users, 40.0% 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 — turn searches and hashtags into contactable author profiles **Turn X/Twitter into a lead list you can actually email.** Give this actor search terms, hashtags, or @handles and get back **one row per unique author** — not a noisy tweet timeline — with their name, bio, follower count, verified status, website, an **email** found in the bio or on their linked site, an `email_status` flag, and a **0–100 lead score** that sorts the best leads first. Built for B2B cold email, creator and founder outreach, agencies, and recruiters who want contactable people, not raw tweets. **Pay-per-result** — you're charged only for the leads delivered after your filters — and **no X/Twitter login or API key** is needed. ### What it does - **Searches X/Twitter** for your terms, hashtags, and handles — tweets are fetched through the [`apidojo/tweet-scraper`](https://apify.com/apidojo/tweet-scraper) data provider, so you never supply credentials. - **Collapses tweets into unique authors**: a two-pass roll-up dedupes every matching tweet down to one row per author handle, and averages likes + retweets + replies across each author's collected tweets into an `engagement_rate` (when the underlying data carries engagement counts). - **Finds emails from two sources**: it scans each author's **bio text** for inline emails, and (with `enrichEmails` on) visits their **bio-link website** — home, `/contact`, `/contact-us`, and `/about` — to extract real emails plus LinkedIn/Facebook/Twitter links. Link-in-bio pages (Linktree, Beacons, etc.) are resolved one hop further, but only to domains matching the author's own name or handle, so a sponsor's email never contaminates your list. - **Filters before you pay**: min/max follower bands, verified-only, has-website, has-email. - **Scores every lead 0–100** — email +40, website +15, follower-band fit up to +15, verified +5, engagement up to +20 — and sorts best-first. - **Dedupes** by handle, website domain, and email, and **optionally writes an AI cold-opener** per lead using your own OpenAI or Anthropic key (used once per run, never stored). ### Use cases - **Niche founder outreach** — search *"ai founder"* or *"shopify agency"* and get the people tweeting about your space, with emails. - **Hashtag communities** — turn `#buildinpublic` into a CSV of contactable indie builders for a launch campaign. - **Creator partnerships** — filter to 5k–500k followers to find mid-tier creators with real engagement, then pitch collaborations. - **Recruiting** — find engineers or designers active in a technical hashtag and reach them off-platform by email. - **CRM enrichment** — feed a list of known @handles and get back emails, websites, and social links for your existing contacts. ### Input Provide at least one of `searchTerms`, `hashtags`, or `handles`. | Field | Type | Default | Description | |---|---|---|---| | `searchTerms` | array | `["ai founder", "saas indie hacker"]` | Keywords/phrases to search X/Twitter for. Matching tweets are collapsed to their authors. | | `hashtags` | array | `["buildinpublic"]` | Hashtags (with or without `#`); treated as extra search terms. | | `handles` | array | — | Specific @handles or profile URLs to pull tweets from, one per line. | | `maxItems` | integer | `200` | How many **tweets** to fetch before rolling up to unique authors. More tweets = more authors discovered. | | `minFollowers` | integer | `0` | Keep only authors with at least this many followers (0 = off). | | `maxFollowers` | integer | `0` | Keep only authors with at most this many followers (0 = no cap). | | `onlyVerified` | boolean | `false` | Keep only verified profiles. | | `onlyWithWebsite` | boolean | `false` | Drop authors with no bio link. | | `onlyWithEmail` | boolean | `false` | Drop authors where no email could be found. | | `enrichEmails` | boolean | `true` | Visit each author's bio-link website to extract emails and socials. | | `writeOpeners` | boolean | `false` | Generate a personalized one-sentence AI opener per lead (needs your key below). | | `llmProvider` | string | `"openai"` | `openai` or `anthropic`. | | `llmApiKey` | string | — | Your own OpenAI/Anthropic API key. Used only for openers; never stored. | | `llmModel` | string | — | Optional model override (defaults: `gpt-4o-mini` / `claude-haiku-4-5`). | ```json { "searchTerms": ["ai founder", "saas indie hacker"], "hashtags": ["buildinpublic"], "maxItems": 200, "minFollowers": 1000, "onlyWithEmail": true, "enrichEmails": true } ```` ### Output One dataset row per unique author, sorted best lead first. Export to CSV, JSON, or Excel from the Output tab. ```json { "handle": "somefounder", "name": "Some Founder", "bio": "Building an AI tool for agencies. Ex-Google. hello@somestartup.com", "email": "hello@somestartup.com", "website": "https://somestartup.com", "followers": 24800, "verified": true, "engagement_rate": 1.8, "tweets_seen": 3, "profile_url": "https://x.com/somefounder", "email_status": "found", "lead_score": 90 } ``` When enrichment finds more, rows also carry `extra_emails` (up to 5 additional addresses) and any `linkedin` / `facebook` / `twitter` links found on the author's site. With `writeOpeners` on, each lead gets an `icebreaker` field. ### Pricing This actor uses Apify **pay-per-event pricing: a fraction of a cent per result — see the Pricing tab** for the exact rate. You're charged **only for the author leads delivered after dedup and your filters** — with `onlyWithEmail` on, authors without an email cost you nothing. No subscription, no charge for empty runs, and the **free Apify plan is enough to try it** on a real search before spending anything. ### Tips / FAQ **Why authors instead of tweets?** Search results are tweets, but for outreach you need people. Every matching tweet is rolled up to its author, so the dataset is a deduped list of contactable profiles — `tweets_seen` tells you how often each author matched. **Will every author have an email?** No. X shows public emails for very few accounts; most emails come from bios and linked websites. Authors with no email show `email_status: "missing"` — use `onlyWithEmail` to keep (and pay for) only contactable leads. **Is `maxItems` the number of leads?** No — it's the number of **tweets scanned** before the author roll-up. 200 tweets might yield anywhere from a handful to ~150 unique authors depending on the query. **Why is `engagement_rate` sometimes null?** It's computed from like/retweet/reply counts averaged across the author's collected tweets; when the underlying data doesn't include those counts, the field stays null and the lead score simply skips that component. **Where does the data come from, and what if the source is down?** Tweets come via an upstream data provider (`apidojo/tweet-scraper`). On a transient upstream outage the run ends gracefully with a "temporarily unavailable — please retry" message and **you're charged nothing**. **Is this legal?** It reads **public** profile and tweet data only. Use the leads for legitimate outreach and follow X's terms plus anti-spam laws (CAN-SPAM, GDPR). ### Related actors - [LinkedIn Leads Scraper](https://apify.com/flash_scraper/linkedin-leads-scraper) — the same scored-leads approach for LinkedIn profiles - [Bulk Email Verifier](https://apify.com/flash_scraper/email-verifier) — verify every email before you launch a campaign - [Email Pattern Finder](https://apify.com/flash_scraper/email-pattern-finder) — guess-and-verify work emails when the bio has none **Support:** found a bug or need a feature? Open an Issue on this actor's **Issues tab** — typical response within 1 business day. # 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 (blue-check) profiles — usually larger, established accounts. ## `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`): AI provider used to write the cold openers — OpenAI (GPT) or Anthropic (Claude). Use the provider that matches the LLM API key you supply. ## `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" } ``` # Actor output Schema ## `results` (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 = { "searchTerms": [ "ai founder", "saas indie hacker" ], "hashtags": [ "buildinpublic" ] }; // Run the Actor and wait for it to finish const run = await client.actor("flash_scraper/twitter-leads-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 = { "searchTerms": [ "ai founder", "saas indie hacker", ], "hashtags": ["buildinpublic"], } # Run the Actor and wait for it to finish run = client.actor("flash_scraper/twitter-leads-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 '{ "searchTerms": [ "ai founder", "saas indie hacker" ], "hashtags": [ "buildinpublic" ] }' | apify call flash_scraper/twitter-leads-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=flash_scraper/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 — 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": "9cYiQ5yLg0XeHhOBG" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/flash_scraper~twitter-leads-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-flash_scraper-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/flash_scraper~twitter-leads-scraper/runs": { "post": { "operationId": "runs-sync-flash_scraper-twitter-leads-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/flash_scraper~twitter-leads-scraper/run-sync": { "post": { "operationId": "run-sync-flash_scraper-twitter-leads-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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 (blue-check) profiles — usually larger, established accounts.", "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": "AI provider used to write the cold openers — OpenAI (GPT) or Anthropic (Claude). Use the provider that matches the LLM API key you supply.", "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 } } } } } } } } } } ```