# Twitter Search Scraper (No Login Required) (`iron-crawler/twitter-search`) Actor Scrape Twitter search results at lightning speed. Zero-config API pipeline exports bulk data in JSON/CSV for instant sales intelligence and competitive research. - **URL**: https://apify.com/iron-crawler/twitter-search.md - **Developed by:** [Iron Crawler](https://apify.com/iron-crawler) (community) - **Categories:** Social media, Lead generation, Other - **Stats:** 38 total users, 5 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.50 / 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. 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 Search Scraper (No Login Required) ### What does Twitter Search Scraper (No Login Required) do? This tool extracts comprehensive data from Twitter (X.com) search results without requiring authentication or login credentials. Built with a cookieless, no-login architecture, it allows you to scrape tweets, user profiles, and engagement metrics directly from search queries—bypassing the barriers of Twitter's API restrictions and rate limits. **Key Features:** - **No authentication required**: Scrape Twitter data without logging in or managing cookies - **Search-based extraction**: Pull tweets matching any keyword, hashtag, or phrase - **Real-time data capture**: Access the latest tweets and trending conversations - **Engagement metrics included**: Extract retweet counts, likes, and reply counts - **Multiple export formats**: Download data as JSON, CSV, or Excel - **User profile data**: Capture display names, handles, and verified status - **Timestamp precision**: Get exact tweet creation dates and times - **URL and media detection**: Identify links and media content within tweets ### Why scrape Twitter (X.com)? Sales intelligence professionals need real-time access to Twitter conversations to identify prospects, monitor competitors, and track industry trends. Twitter search results contain valuable signals—product mentions, pain points, buying intent, and competitive movements—that traditional databases miss. By scraping Twitter search data, you can build comprehensive lead generation pipelines and competitive intelligence databases that update continuously with fresh market insights. **Primary Use Cases:** - **Lead Generation**: Identify potential customers discussing problems your product solves, track decision-makers mentioning competitors, and discover companies announcing budget allocations or new initiatives - **Competitive Intelligence**: Monitor competitor product launches, track customer complaints about rival solutions, analyze competitor engagement rates, and identify their key influencers and brand advocates - **Market Research & Trend Analysis**: Detect emerging industry trends before they peak, measure sentiment around specific topics or brands, identify viral content patterns, and track hashtag performance across campaigns ### How to scrape Twitter (X.com) using this tool? **Step 1: Define Your Search Query** Identify the keywords, hashtags, usernames, or phrases you want to track. You can use Twitter's advanced search operators (e.g., ""AI tools"" OR ""machine learning"", #SaaS, from:username). **Step 2: Configure Input Parameters** Enter your search query in the `query` field. The scraper will extract tweets matching your criteria. Note: **1 search query ≈ 50-100 recent tweets** depending on Twitter's current results. **Step 3: Run and Download** Start the scraper and wait for extraction to complete. Once finished, download your data in JSON, CSV, or Excel format for immediate analysis or integration into your CRM and intelligence platforms. ### What are the input parameters? | Field | Type | Description | |-------|------|-------------| | `query` | String | The search term, hashtag, or phrase you want to scrape from Twitter. Supports Twitter's search operators (e.g., ""Mr Beast"", ""#AI"", ""from:elonmusk"", ""product launch -spam""). This is the primary filter for determining which tweets to extract. | ### What data can you extract? You can download the following data in JSON, CSV, or Excel formats: ```json { ""tweet_id"": ""1738947261548392448"", ""user_handle"": ""techexpert"", ""display_name"": ""Tech Expert 🚀"", ""tweet_text"": ""Just launched our new AI feature! Check it out at https://t.co/abc123"", ""created_at"": ""2025-12-18T14:30:22Z"", ""retweet_count"": 142, ""like_count"": 567, ""reply_count"": 28 } ```` **Extracted Fields:** - `tweet_id`: Unique identifier for each tweet - `user_handle`: Twitter username (without @ symbol) - `display_name`: User's profile display name (may include emojis) - `tweet_text`: Full text content of the tweet, including URLs - `created_at`: Timestamp of when the tweet was posted (ISO 8601 format) - `retweet_count`: Number of times the tweet has been retweeted - `like_count`: Number of likes the tweet has received - `reply_count`: Number of replies to the tweet *** Whether you need a Twitter scraper for lead generation, a Twitter search scraper for competitive analysis, or an X search scraper to export tweets at scale, this tool provides reliable tweet data extraction without authentication barriers. Perfect for sales teams building Twitter lead generation workflows and marketers who need a powerful Twitter data extractor for campaign intelligence." # Actor input Schema ## `query` (type: `string`): Search query ## `section` (type: `string`): Section: top, latest, people, photos, videos ## `maxPages` (type: `integer`): Maximum number of pages to fetch (pagination handled automatically) ## Actor input object example ```json { "query": "new york", "section": "top", "maxPages": 1 } ``` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("iron-crawler/twitter-search").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 = {} # Run the Actor and wait for it to finish run = client.actor("iron-crawler/twitter-search").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 '{}' | apify call iron-crawler/twitter-search --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=iron-crawler/twitter-search", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Twitter Search Scraper (No Login Required)", "description": "Scrape Twitter search results at lightning speed. Zero-config API pipeline exports bulk data in JSON/CSV for instant sales intelligence and competitive research.", "version": "1.0", "x-build-id": "cRQu3BtgBNkywRApA" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/iron-crawler~twitter-search/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-iron-crawler-twitter-search", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/iron-crawler~twitter-search/runs": { "post": { "operationId": "runs-sync-iron-crawler-twitter-search", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/iron-crawler~twitter-search/run-sync": { "post": { "operationId": "run-sync-iron-crawler-twitter-search", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "query" ], "properties": { "query": { "title": "Search query", "type": "string", "description": "Search query", "default": "new york" }, "section": { "title": "Section: top, latest, people, photos, videos", "type": "string", "description": "Section: top, latest, people, photos, videos", "default": "top" }, "maxPages": { "title": "Maximum Pages", "minimum": 1, "maximum": 100, "type": "integer", "description": "Maximum number of pages to fetch (pagination handled automatically)", "default": 1 } } }, "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 } } } } } } } } } } ```