# X/Twitter search scraper (`dovepppp/search-tweet-scraper`) Actor This Apify Actor collects publicly visible X (Twitter) posts matching your search query and writes them to a Dataset. Each item includes text, engagement counts, time, author profile, flags for reply/retweet/quote, and image/video URLs when available. - **URL**: https://apify.com/dovepppp/search-tweet-scraper.md - **Developed by:** [doveppp](https://apify.com/dovepppp) (community) - **Categories:** Social media, News, Other - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.30 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ### What this Actor does This **Apify Actor** collects **publicly visible X (Twitter) posts** matching your **search query** and writes them to a **Dataset**. Each item includes text, engagement counts, time, author profile, flags for reply/retweet/quote, and image/video URLs when available. Use it for monitoring, research exports, and integrations via **API**, **Zapier**, **Make**, and more. You can **schedule runs** in the Apify Console, review history, and use platform features such as **proxies** (depending on your plan). ### Why use it - Pull a batch of public posts by keywords, hashtags, time range, and other filters supported by X search. - Download results as **JSON**, **CSV**, or **Excel**, or pipe them into your own pipeline. - Run repeatable, observable jobs in the cloud instead of one-off local scripts. ### How to use it 1. Open this Actor on Apify and confirm your **maintainer has configured the required runtime** (account pool and any secrets—see deployment notes for this Actor). 2. Open **Input** and set **Search query**. You can build the same filters and operators in X’s UI, then copy the string here: **[Advanced search](https://x.com/search-advanced)** (keywords, `from:`, `since:`, `until:`, etc.). 3. Set **Maximum number of posts** (upper bound for rows written in this run). 4. Click **Start**. Inspect results under **Dataset** or fetch them via the **API**. For local runs: `apify run` (with dependencies and config in place). ### Input Configure in the **Input** tab or equivalent JSON: | Field | Description | |-------|-------------| | `query` | Search expression (keywords, `from:`, `since:`, `until:`, etc.). Compose it in [X Advanced Search](https://x.com/search-advanced) and paste the query into this field. | | `maxTweets` | Positive integer: maximum number of posts to write to the dataset in this run. | Example: ```json { "query": "apify lang:en", "maxTweets": 500 } ```` ### Output Each record is one post. You can export the default dataset as **JSON**, **CSV**, **Excel**, etc. Example (fields may evolve slightly; trust the live Dataset): ```json { "type": "tweet", "id": "1728108619189874825", "url": "https://x.com/example/status/1728108619189874825", "text": "Example text", "retweetCount": 0, "replyCount": 0, "likeCount": 0, "quoteCount": 0, "createdAt": "2023-11-24 17:49:36+00:00", "lang": "en", "quoteId": "", "bookmarkCount": 0, "isReply": false, "source": "Twitter for Android", "author": { "type": "user", "userName": "example", "url": "https://x.com/example", "id": "44196397", "name": "Example User", "isVerified": true, "isBlueVerified": true, "verifiedType": "", "profilePicture": "https://pbs.twimg.com/profile_images/…/normal.jpg", "followers": 1000, "following": 100 }, "isRetweet": false, "isQuote": false, "imgUrls": "", "videoUrls": "" } ``` ### Main fields | Field | Meaning | |-------|---------| | `id` / `url` | Post id and link | | `text` | Body text | | `retweetCount` / `replyCount` / `likeCount` / `quoteCount` / `bookmarkCount` | Engagement (as surfaced by the platform) | | `createdAt` | Post time | | `lang` | Language code | | `quoteId` | Quoted post id, if any | | `isReply` / `isRetweet` / `isQuote` | Structural flags | | `source` | Client name when available | | `author` | Display name, avatar, follower counts, etc. | | `imgUrls` / `videoUrls` | Media URLs, comma-separated when multiple | ### Pricing Cost depends on your **Apify plan**, **maxTweets**, runtime length, and network usage. See [Apify pricing](https://apify.com/pricing). Smaller time windows and lower caps usually reduce cost per run. ### Tips - Start with a small **maxTweets** to validate the query and fields, then scale up. - Comply with **X Terms of Service**, applicable laws, and platform rules; use data lawfully. - If you get fewer rows than expected, widen the window or simplify the query and retry. ### Disclaimer and support Availability of public posts, field completeness, and platform behavior can change; the Actor does **not** guarantee parity with the website UI. You are responsible for lawful collection and use of data. Report issues via this Actor’s **Issues** on Apify; contact the developer for custom work. ### Resources - [Apify Python SDK](https://docs.apify.com/sdk/python/) - [Actor documentation](https://docs.apify.com/platform/actors) # Actor input Schema ## `query` (type: `string`): Keywords and filters as in X (Twitter) search (operators, time range, etc.). ## `maxTweets` (type: `integer`): Upper bound on how many posts to write to the dataset in this run. ## Actor input object example ```json {} ``` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("dovepppp/search-tweet-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 = {} # Run the Actor and wait for it to finish run = client.actor("dovepppp/search-tweet-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 '{}' | apify call dovepppp/search-tweet-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=dovepppp/search-tweet-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "X/Twitter search scraper", "description": "This Apify Actor collects publicly visible X (Twitter) posts matching your search query and writes them to a Dataset. Each item includes text, engagement counts, time, author profile, flags for reply/retweet/quote, and image/video URLs when available.", "version": "0.2", "x-build-id": "i6DiZLBjXjm7irqVY" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/dovepppp~search-tweet-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-dovepppp-search-tweet-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/dovepppp~search-tweet-scraper/runs": { "post": { "operationId": "runs-sync-dovepppp-search-tweet-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/dovepppp~search-tweet-scraper/run-sync": { "post": { "operationId": "run-sync-dovepppp-search-tweet-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "query", "maxTweets" ], "properties": { "query": { "title": "Search query", "type": "string", "description": "Keywords and filters as in X (Twitter) search (operators, time range, etc.)." }, "maxTweets": { "title": "Maximum posts", "minimum": 100, "type": "integer", "description": "Upper bound on how many posts to write to the dataset in this run." } } }, "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 } } } } } } } } } } ```