# ✅ X (Twitter) Retweeter Scraper (`datamagnet/x-twitter-retweeter-scraper`) Actor Get the list of users who retweeted any public Twitter/X post by entering a tweet ID. The actor returns raw retweeter data exactly as provided and can continue through pagination to collect more results up to your chosen limit. - **URL**: https://apify.com/datamagnet/x-twitter-retweeter-scraper.md - **Developed by:** [Datamagnet](https://apify.com/datamagnet) (community) - **Categories:** Social media, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $3,000.00 / 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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## Tweet Retweeter Scraper Tweet Retweeter Scraper helps you quickly find the users who retweeted a specific tweet, making it easy to analyze engagement, audience interest, and social proof on X/Twitter. It is built for marketers, researchers, creators, and growth teams who need reliable retweeter data without manual checking. Enter a tweet ID and get the retweeter list back in a clean, structured format, including the raw response data exactly as returned. If you need to track tweet engagement, study audience behavior, or collect retweeter profiles for reporting, this actor gives you a fast and practical way to do it. ### Key Features - Extract the users who retweeted a specific tweet using only the tweet ID. - Collect rich retweeter details such as name, screen name, bio, follower count, and more. - Preserve the raw returned data exactly as provided, so you keep all available fields. - Continue gathering additional retweeters automatically when more results are available. - Stop after your chosen maximum number of results to keep collections focused. - Export results in a format that is easy to review, share, and analyze. - Support repeatable workflows for monitoring tweet engagement over time. ### Use Cases #### Social Media Analytics Social media managers use this actor to understand who is amplifying a tweet and how strong the engagement is. The returned fields like `screen_name`, `name`, `followers_count`, and `statuses_count` help teams judge whether retweeters are casual users, niche creators, or accounts with meaningful reach. This makes it easier to report on campaign performance and identify which posts are resonating. #### Audience Research Researchers and brand teams can review retweeter profiles to learn more about the audience behind a tweet. Fields such as `description`, `friends_count`, `followers_count`, and `media_count` help reveal interests, account activity, and profile quality. That information can guide content strategy, audience segmentation, and messaging decisions. #### Influencer and Creator Discovery Creators and partnership teams can use retweeter data to spot accounts that consistently engage with relevant content. By looking at `screen_name`, `name`, and profile descriptions, they can identify potential collaborators, advocates, or community members worth following up with. This is especially useful when a tweet attracts attention from people in a specific niche. #### Competitive Intelligence Analysts can inspect retweeters of competitor tweets to see which audiences are responding to similar content. The profile fields returned by the actor help teams compare follower size, posting activity, and account style across different retweeter groups. This can support positioning decisions, content benchmarking, and campaign planning. #### Community Management Community managers can use the output to recognize active supporters and recurring engagers. When a tweet is shared by users with visible profiles and bios, it becomes easier to identify loyal community members and respond appropriately. This can improve relationship building and help teams prioritize outreach. #### Reporting and Internal Dashboards Operations and marketing teams can feed the returned retweeter data into reports or dashboards for ongoing tracking. Because the actor returns the raw data exactly as provided, it is easier to keep the original structure intact for downstream analysis. This helps teams maintain consistent reporting across campaigns and time periods. ### Input | Field | Type | Required | Description | Example | |---|---|---:|---|---| | id | String | Yes | The tweet ID you want to inspect. | 1738106896777699464 | | max_results | Integer | No | The maximum number of retweeters to collect before stopping. | 100 | ### Output | Field | Type | Description | |---|---|---| | user_id | String | The unique ID of the user who retweeted the tweet. Useful for identifying the account behind the profile. | | screen_name | String | The user’s public handle. Helpful for quickly recognizing or searching the account. | | description | String | The bio text from the user’s profile, which can reveal interests, role, or niche. | | profile_image | String or Null | The user’s profile image link when available, or null if none is provided. | | statuses_count | Integer | The number of posts the account has made, which can indicate how active the user is. | | followers_count | Integer | The number of followers the account has, which helps estimate reach and influence. | | friends_count | Integer | The number of accounts the user follows, which can help describe account behavior. | | media_count | Integer | The number of media posts associated with the account, useful for understanding content activity. | | name | String | The display name shown on the profile, which helps identify the person or brand. | ### Sample Output ```json { "user_id": "282835047", "screen_name": "futureSpaceXAl", "description": "Starmind", "profile_image": null, "statuses_count": 43083, "followers_count": 4351, "friends_count": 1991, "media_count": 1213, "name": "Future Innovation" } ```` ### How It Works 1. You enter the tweet ID for the post you want to inspect. 2. You choose how many retweeters you want to collect, if you want to limit the results. 3. The actor gathers the retweeter list automatically and keeps going when more results are available. 4. It stops when it reaches your chosen limit or when there are no more retweeters to collect. 5. You receive the results in a structured format that is ready to review, download, or use in your workflow. ### Getting Started Getting started is simple and does not require coding knowledge. Click **Try for free**, paste in the tweet ID, optionally set the maximum number of results, and click **Run**. The actor will collect the retweeter data for you automatically. When it finishes, you can view and download the results as JSON, CSV, or Excel. ### Frequently Asked Questions #### Do I need technical skills? No. This actor is designed for business users, marketers, researchers, and creators who want results without dealing with complicated setup. You only need to provide the tweet ID and start the run. #### How fast does it run? Speed depends on how many retweeters are available for the tweet and how many results you ask for. Smaller requests usually finish quickly, while larger collections may take longer because the actor keeps gathering additional pages of results when needed. #### What format is the output? The results are available in easy-to-use formats such as JSON, CSV, and Excel. That makes it simple to review the data manually, share it with teammates, or import it into reports and spreadsheets. #### Is this legal to use? You should always use the data in a way that follows applicable laws, platform rules, and your organization’s policies. This actor is intended for legitimate research, analytics, and business workflows, and you are responsible for using the results appropriately. #### Can I schedule it to run automatically? Yes, you can set it up to run on a schedule if you want to monitor tweet engagement regularly. This is useful for recurring reporting, campaign tracking, or watching how retweeter activity changes over time. # Actor input Schema ## `id` (type: `string`): Enter the ID of the tweet you want to inspect. ## `max_results` (type: `integer`): Maximum number of retweeters to collect before stopping. ## Actor input object example ```json { "id": "1738106896777699464", "max_results": 100 } ``` # 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 = { "id": "1738106896777699464" }; // Run the Actor and wait for it to finish const run = await client.actor("datamagnet/x-twitter-retweeter-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 = { "id": "1738106896777699464" } # Run the Actor and wait for it to finish run = client.actor("datamagnet/x-twitter-retweeter-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 '{ "id": "1738106896777699464" }' | apify call datamagnet/x-twitter-retweeter-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=datamagnet/x-twitter-retweeter-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "✅ X (Twitter) Retweeter Scraper", "description": "Get the list of users who retweeted any public Twitter/X post by entering a tweet ID. The actor returns raw retweeter data exactly as provided and can continue through pagination to collect more results up to your chosen limit.", "version": "0.0", "x-build-id": "1ftIvpzE3GFSscg1x" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/datamagnet~x-twitter-retweeter-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-datamagnet-x-twitter-retweeter-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/datamagnet~x-twitter-retweeter-scraper/runs": { "post": { "operationId": "runs-sync-datamagnet-x-twitter-retweeter-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/datamagnet~x-twitter-retweeter-scraper/run-sync": { "post": { "operationId": "run-sync-datamagnet-x-twitter-retweeter-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "id" ], "properties": { "id": { "title": "Tweet ID", "type": "string", "description": "Enter the ID of the tweet you want to inspect." }, "max_results": { "title": "Max results", "minimum": 1, "type": "integer", "description": "Maximum number of retweeters to collect before stopping.", "default": 10 } } }, "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 } } } } } } } } } } ```