# Twitter Reply Scraper | $0.1/1K Tweets | Pay-Per Result ｜ 2026 (`fastcrawler/twitter-reply-scraper-0-1-1k-tweets-pay-per-result-2026`) Actor Get 1,000 Twitter replies(comments) for just $0.1! πŸš€ Instantly download Twitter replies and comments. Our tool provides precise and rapid data extraction, no coding required. Pay only for the data you need, and gain valuable insights for market analysis, sentiment monitoring, and more. - **URL**: https://apify.com/fastcrawler/twitter-reply-scraper-0-1-1k-tweets-pay-per-result-2026.md - **Developed by:** [fastcrawler](https://apify.com/fastcrawler) (community) - **Categories:** AI, Social media, Automation - **Stats:** 613 total users, 27 monthly users, 99.4% runs succeeded, 20 bookmarks - **User rating**: 3.87 out of 5 stars ## Pricing Pay per event 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 Reply Scraper | $0.1/1K Tweets | Pay-Per Result | 2026 Effortlessly scrape Twitter replies with precision and speed β€” pay only for the results you get! This tool is designed to streamline data collection for market research, sentiment analysis, and audience insights, giving you access to valuable conversational data. ### πŸ’‘ Related scrapers - **[Free Twitter API](https://twexapi.io) ｜ [Track X Influencers Tweets in Real-time](https://xmonitor.online)** - [Tweet(X) Fast&Cheapest scraper|$0.35/1K| Pay-Per Result |2025](https://apify.com/fastcrawler/tweet-x-fast-cheapest-scraper-0-35-1k-pay-per-result-2025) - [Twitter Reply Scraper](https://apify.com/fastcrawler/twitter-x-comment-scraper-support-sentiment-tone-analyzer) - [Twitter KOL Discovery: Find Influencers Fast | $0.5/1k | 2025](https://apify.com/fastcrawler/twitter-kol-discovery-find-influencers-fast-0-5-1k-2025) - [Other useful scrapers](https://apify.com/fastcrawler) ### 🌟 Key Features - βœ… High-Accuracy Scraping: Capture replies from any public tweet, even in large threads. - πŸ€– Keyword & User Filtering: Focus on relevant replies by specifying keywords or user handles. - 🌍 Multi-Language Support: Scrape and process tweets in various languages for global analysis. - ✨ Clean, Structured Output: Get organized data ready for analysis, without extra cleaning steps. **Supercharge your research and content strategy with a reliable, result-driven reply scraper! πŸš€ ### Contact and Feature Requests - If you need customized features or have specific requirements - support for second-level tweet monitoring - Real-time username change monitoring - real-time follow activity monitoring, feel free to contact me - If you need any sort of support, please send an email to fastcrawler9@gmail.com | [tg](https://t.me/+MUMR3PKOoVVjNzE9) | [discord](https://discord.gg/s4jyupzaWu) ### Input Parameters | Field | Type | Description | |----|----------|---------------| | conversationId | string | The id of tweet you want to get the replies from. eg: 1895950135865327920| | queryType | string | queryType: Latest,Top| | maxItems | string | maxItems| ### Output Example ```` ``` { "type": "tweet", "id": "1896710161546506732", "url": "https://x.com/themainsquid1/status/1896710161546506732", "twitterUrl": "https://twitter.com/themainsquid1/status/1896710161546506732", "text": "@SOLAXYTOKEN We shall see", "source": "Twitter for iPhone", "retweetCount": 0, "replyCount": 1, "likeCount": 0, "quoteCount": 2, "viewCount": 35, "createdAt": "Mon Mar 03 23:51:56 +0000 2025", "lang": "en", "bookmarkCount": 0, "isReply": true, "inReplyToId": "1895950135865327920", "conversationId": "1895950135865327920", "inReplyToUserId": "1863672222734323712", "inReplyToUsername": "SOLAXYTOKEN", "author": { "type": "user", "userName": "themainsquid1", "url": "https://x.com/themainsquid1", "twitterUrl": "https://twitter.com/themainsquid1", "id": "1885048650016989184", "name": "lTyler", "isVerified": false, "isBlueVerified": false, "profilePicture": "https://pbs.twimg.com/profile_images/1892261696506368000/SY_mBevU_normal.jpg", "coverPicture": "https://pbs.twimg.com/profile_banners/1885048650016989184/1739985331", "description": "", "location": "", "followers": 79, "following": 54, "status": "", "canDm": false, "canMediaTag": true, "createdAt": "Thu Jan 30 19:33:32 +0000 2025", "entities": { "description": { "urls": [] }, "url": {} }, "fastFollowersCount": 0, "favouritesCount": 355, "hasCustomTimelines": true, "isTranslator": false, "mediaCount": 4, "statusesCount": 228, "withheldInCountries": [], "affiliatesHighlightedLabel": {}, "possiblySensitive": false, "pinnedTweetIds": [ "1892021531368165565" ], "profile_bio": { "description": "πŸ‡ΊπŸ‡Έ 🌍 ✌️", "entities": { "description": {} } }, "isAutomated": false, "automatedBy": null }, "extendedEntities": {}, "card": null, "place": {}, "entities": { "user_mentions": [ { "id_str": "1863672222734323712", "indices": [ 0, 12 ], "name": "SOLAXY", "screen_name": "SOLAXYTOKEN" } ] }, "quoted_tweet": null, "retweeted_tweet": null } ``` ```` # Actor input Schema ## `conversationIds` (type: `array`): The id of tweet you want to get the replies from. ## `queryType` (type: `string`): queryType ## `maxItems` (type: `integer`): Maximum number of items that you want as output. ## Actor input object example ```json { "conversationIds": [ "1895950135865327920" ], "queryType": "Top", "maxItems": 30 } ```` # Actor output Schema ## `overview` (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 = { "conversationIds": [ "1895950135865327920" ], "queryType": "Top" }; // Run the Actor and wait for it to finish const run = await client.actor("fastcrawler/twitter-reply-scraper-0-1-1k-tweets-pay-per-result-2026").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ 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); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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 = { "conversationIds": ["1895950135865327920"], "queryType": "Top", } # Run the Actor and wait for it to finish run = client.actor("fastcrawler/twitter-reply-scraper-0-1-1k-tweets-pay-per-result-2026").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "conversationIds": [ "1895950135865327920" ], "queryType": "Top" }' | apify call fastcrawler/twitter-reply-scraper-0-1-1k-tweets-pay-per-result-2026 --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=fastcrawler/twitter-reply-scraper-0-1-1k-tweets-pay-per-result-2026", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Twitter Reply Scraper | $0.1/1K Tweets | Pay-Per Result ｜ 2026", "description": "Get 1,000 Twitter replies(comments) for just $0.1! πŸš€ Instantly download Twitter replies and comments. Our tool provides precise and rapid data extraction, no coding required. Pay only for the data you need, and gain valuable insights for market analysis, sentiment monitoring, and more.", "version": "0.0", "x-build-id": "nkoXafvIUs8RHFf0v" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/fastcrawler~twitter-reply-scraper-0-1-1k-tweets-pay-per-result-2026/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-fastcrawler-twitter-reply-scraper-0-1-1k-tweets-pay-per-result-2026", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/fastcrawler~twitter-reply-scraper-0-1-1k-tweets-pay-per-result-2026/runs": { "post": { "operationId": "runs-sync-fastcrawler-twitter-reply-scraper-0-1-1k-tweets-pay-per-result-2026", "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/fastcrawler~twitter-reply-scraper-0-1-1k-tweets-pay-per-result-2026/run-sync": { "post": { "operationId": "run-sync-fastcrawler-twitter-reply-scraper-0-1-1k-tweets-pay-per-result-2026", "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": [ "conversationIds" ], "properties": { "conversationIds": { "title": "conversation id", "type": "array", "description": "The id of tweet you want to get the replies from.", "items": { "type": "string" } }, "queryType": { "title": "queryType", "enum": [ "Latest", "Top" ], "type": "string", "description": "queryType", "default": "Top" }, "maxItems": { "title": "Maximum number of items on output", "minimum": 30, "maximum": 5000, "type": "integer", "description": "Maximum number of items that you want as output.", "default": 30 } } }, "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 } } } } } } } } } } ```