# X (Twitter) Tweet Scraper — Search by Query, Hashtag, User (`seemuapps/x-tweet-scraper`) Actor Search and export tweets from X (Twitter) using advanced query syntax — filter by user, date, language, hashtag, and engagement. No login required. - **URL**: https://apify.com/seemuapps/x-tweet-scraper.md - **Developed by:** [Andrew](https://apify.com/seemuapps) (community) - **Categories:** Lead generation, Social media - **Stats:** 4 total users, 3 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $1.00 / 1,000 tweet results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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 ## X (Twitter) Tweet Scraper — Search by Query, Hashtag, User Search tweets on X (Twitter) by keyword, hashtag, user, date, or engagement — export them as JSON, CSV, or Excel. No login or developer account required. ### What you get - Tweet ID, full text, URL, language, and creation timestamp - Engagement metrics — likes, retweets, replies, quotes, views, bookmarks - Author details — username, display name, follower count, verification status - Reply / quote / retweet context — original tweet IDs and conversation IDs - Cursor-based pagination — resume large extractions across multiple runs ### Use cases - Brand and competitor monitoring on X - Sentiment analysis and audience research - Lead generation — find people tweeting about your niche - Trend tracking around hashtags, cashtags, or events - Influencer discovery — surface accounts driving engagement on a topic ### How to use 1. Enter a **Search Query** using Twitter's advanced search syntax (examples below) 2. Choose **Sort Order** — `Latest` for chronological, `Top` for highest engagement 3. Set **Max Tweets** (default 100; set 0 for unlimited) 4. Run the actor — one tweet per row in the **Dataset** tab 5. To fetch the next page, open the **Key-value store** tab → copy the `NEXT_PAGE_ID` value → paste it into **Page ID** on your next run ### Query syntax — examples | Goal | Query | |------|-------| | All tweets from a user | `from:elonmusk` | | Tweets mentioning a brand | `@nike` | | Exact phrase | `"black friday"` | | Hashtag in English with 100+ likes | `#ai lang:en min_faves:100` | | Tweets with media in a date range | `from:nasa has:media since_time:1704067200 until_time:1706659200` | | Replies to a user excluding RTs | `to:openai -filter:retweets` | | Combine terms with OR | `("AI" OR "ML") from:openai` | ### Output format One tweet per dataset row — perfect for direct CSV, Excel, or Google Sheets export: ```json { "tweetId": "1234567890", "url": "https://x.com/elonmusk/status/1234567890", "text": "Tweet text here…", "createdAt": "Wed Jan 01 12:00:00 +0000 2026", "lang": "en", "likeCount": 12345, "retweetCount": 678, "replyCount": 234, "quoteCount": 45, "viewCount": 1000000, "bookmarkCount": 89, "isReply": false, "isQuote": false, "isRetweet": false, "author": { "username": "elonmusk", "userId": "44196397", "name": "Elon Musk", "isBlueVerified": true, "followersCount": 200000000 } } ```` ### Pagination If your search has more results than **Max Tweets** allows, the actor saves a resume cursor to the default **Key-value store** under the key `NEXT_PAGE_ID`. 1. Open the **Key-value store** tab on the run page 2. Copy the value of `NEXT_PAGE_ID` 3. Start a new run and paste it into **Page ID** When `NEXT_PAGE_ID` is `null`, the search is fully exhausted. ### Input options | Field | Type | Description | |-------|------|-------------| | Search Query | string | Twitter advanced-search query (required) | | Sort Order | select | `Latest` (default) or `Top` | | Max Tweets | integer | Tweet cap per run — default 100, 0 for unlimited | | Page ID | string | `NEXT_PAGE_ID` from the previous run's Key-value store, to resume pagination | # Actor input Schema ## `query` (type: `string`): Twitter advanced search query. Supports operators like from:username, to:username, "exact phrase", #hashtag, $cashtag, lang:en, has:media, min\_faves:100, min\_retweets:50, since\_time:UNIX, until\_time:UNIX, OR, AND, parentheses. ## `queryType` (type: `string`): Latest returns most recent first. Top returns highest-engagement first. ## `maxItems` (type: `integer`): Maximum tweets to return. Set 0 for unlimited (use cautiously — large queries can be slow). ## `pageId` (type: `string`): Optional. Pass cursor\_next from a previous run's output to resume from where the last run stopped. ## Actor input object example ```json { "query": "from:elonmusk min_faves:1000", "queryType": "Latest", "maxItems": 100 } ``` # Actor output Schema ## `results` (type: `string`): One row per tweet: tweetId, url, text, createdAt, lang, engagement counts, author, entities, and reply/quote/retweet context. ## `nextPageId` (type: `string`): NEXT\_PAGE\_ID record in the default key-value store. Paste into Page ID on the next run to resume; null when search is exhausted. # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "query": "from:elonmusk min_faves:1000" }; // Run the Actor and wait for it to finish const run = await client.actor("seemuapps/x-tweet-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = { "query": "from:elonmusk min_faves:1000" } # Run the Actor and wait for it to finish run = client.actor("seemuapps/x-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 '{ "query": "from:elonmusk min_faves:1000" }' | apify call seemuapps/x-tweet-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=seemuapps/x-tweet-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "X (Twitter) Tweet Scraper — Search by Query, Hashtag, User", "description": "Search and export tweets from X (Twitter) using advanced query syntax — filter by user, date, language, hashtag, and engagement. No login required.", "version": "1.0", "x-build-id": "rQt89M6bJ7XgA3RFG" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/seemuapps~x-tweet-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-seemuapps-x-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/seemuapps~x-tweet-scraper/runs": { "post": { "operationId": "runs-sync-seemuapps-x-tweet-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/seemuapps~x-tweet-scraper/run-sync": { "post": { "operationId": "run-sync-seemuapps-x-tweet-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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" ], "properties": { "query": { "title": "Search Query", "type": "string", "description": "Twitter advanced search query. Supports operators like from:username, to:username, \"exact phrase\", #hashtag, $cashtag, lang:en, has:media, min_faves:100, min_retweets:50, since_time:UNIX, until_time:UNIX, OR, AND, parentheses." }, "queryType": { "title": "Sort Order", "enum": [ "Latest", "Top" ], "type": "string", "description": "Latest returns most recent first. Top returns highest-engagement first.", "default": "Latest" }, "maxItems": { "title": "Max Tweets", "minimum": 0, "type": "integer", "description": "Maximum tweets to return. Set 0 for unlimited (use cautiously — large queries can be slow).", "default": 100 }, "pageId": { "title": "Page ID (pagination)", "type": "string", "description": "Optional. Pass cursor_next from a previous run's output to resume from where the last run stopped." } } }, "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 } } } } } } } } } } ```