# Threads Replies Scraper (`igview-owner/threads-replies-scraper`) Actor Scrape public replies and comments from Meta Threads users. Get reply text, user info, engagement metrics and media URLs in clean JSON/CSV for analytics and research. - **URL**: https://apify.com/igview-owner/threads-replies-scraper.md - **Developed by:** [Sachin Kumar Yadav](https://apify.com/igview-owner) (community) - **Categories:** Social media, Automation, Developer tools - **Stats:** 94 total users, 8 monthly users, 100.0% runs succeeded, 1 bookmarks - **User rating**: 3.99 out of 5 stars ## Pricing from $5.00 / 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 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 ## Threads User Replies Scraper - Extract Comprehensive Reply Data **The most powerful and reliable Threads replies scraper for extracting comprehensive reply posts and conversation data.** Built as a professional Apify Actor for seamless data extraction from Meta Threads platform with advanced reply analytics and conversation intelligence. --- ### 📋 Table of Contents - [🚀 Why Choose Threads User Replies Scraper?](#-why-choose-threads-user-replies-scraper) - [✨ Key Features](#-key-features) - [⚡ Quick Start Guide](#-quick-start-guide) - [🔧 Input Configuration](#-input-configuration) - [📊 Output Data Structure](#-output-data-structure) - [💡 Usage Examples](#-usage-examples) - [🎯 Best Practices](#-best-practices) - [📈 Performance & Limits](#-performance--limits) - [🛠️ Troubleshooting](#️-troubleshooting) - [❓ Frequently Asked Questions](#-frequently-asked-questions) - [🏷️ Keywords & SEO](#️-keywords--seo) --- ### 🚀 Why Choose Threads User Replies Scraper? | Feature | Benefit | Description | |---------|---------|-------------| | **Reply-Focused** | Complete Reply Data | Extract comprehensive reply posts with full metadata | | **User-Specific** | Profile Targeting | Get all replies from specific user profiles | | **Rich Analytics** | Conversation Intelligence | Engagement metrics, timestamps, and media URLs | | **Smart Processing** | Efficient Extraction | Optimized processing for large reply datasets | | **Media Support** | Multi-Format | Images, videos, carousels, and text posts | | **Content Analysis** | Full Text Extraction | Complete reply text and caption data | | **Flexible Output** | Clean Data Format | Organized table views for easy analysis | | **High Performance** | Fast & Reliable | Built-in rate limiting and error handling | --- ### ✨ Key Features #### 🎯 **Advanced Reply Extraction** - ✅ Extract complete reply posts with detailed metadata - ✅ Comprehensive post information (text, media, timestamps) - ✅ Author details with verification status - ✅ Engagement metrics (likes, quotes, shares) - ✅ Media URLs for images, videos, and carousels #### 📊 **Comprehensive Content Analytics** - ✅ Full reply text and caption extraction - ✅ Timestamp tracking for temporal analysis - ✅ Engagement metrics for popularity assessment - ✅ Media type identification (image, video, carousel) - ✅ Reply-to tracking for conversation mapping #### ⚙️ **Professional Data Organization** - ✅ Clean, organized table views in datasets - ✅ Multiple data views (overview, content, media, detailed) - ✅ Individual reply records for easy processing - ✅ Export-ready formats (JSON, CSV, Excel) - ✅ Structured data for analysis and reporting #### 🔧 **Enterprise Features** - ✅ Built-in rate limiting and retry logic - ✅ Automatic error handling and recovery - ✅ Scalable processing for large reply datasets - ✅ Real-time extraction progress tracking - ✅ Configurable extraction limits (1-100 replies) --- ### ⚡ Quick Start Guide #### Step 1: Configure Input Parameters ```json { "usernames": ["pubity"], "maxReplies": 50 } ```` #### Step 2: Run the Actor Click "Start" in the Apify Console and monitor the extraction progress #### Step 3: Download Results Get your data in JSON, CSV, or Excel format from the dataset with organized table views *** ### 🔧 Input Configuration | Parameter | Type | Required | Default | Description | |-----------|------|----------|---------|-------------| | `usernames` | Array | ✅ Yes | - | Threads usernames (without @) to extract replies from | | `maxReplies` | Integer | ❌ No | 20 | Maximum replies to extract per user (1-100) | #### 📝 Input Configuration Examples **Basic Reply Extraction:** ```json { "usernames": ["pubity"] } ``` **Multiple Users:** ```json { "usernames": ["pubity", "zuck", "mosseri"], "maxReplies": 50 } ``` **Comprehensive Reply Analysis:** ```json { "usernames": ["mrbeast"], "maxReplies": 100 } ``` *** ### 📊 Output Data Structure #### Individual Reply Record ```json { "source": "threads", "target_username": "pubity", "post_id": "3768679628828060963_63241292143", "pk": "3768679628828060963", "code": "DRNCie0jC0j", "taken_at": 1763481699, "taken_at_timestamp": "2025-11-17T10:34:59.000Z", "user_id": "63241292143", "username": "pubity", "full_name": "Pubity", "is_verified": true, "profile_pic_url": "https://scontent-bru2-1.cdninstagram.com/v/t51.2885-19/357749851_6305413922868654_7338721450189801290_n.jpg", "caption_text": "This has got to be THE FLUFFIEST bunny of all time 😭😲", "caption_pk": "18077089204935789", "media_type": 2, "original_width": 720, "original_height": 1280, "image_url": "https://scontent-bru2-1.cdninstagram.com/v/t51.29350-15/...", "video_url": null, "has_audio": false, "carousel_media_count": 0, "has_carousel": false, "like_count": 1406, "reply_to_author": null, "quote_count": 7, "is_post_unavailable": false, "post_url": "https://www.threads.net/@pubity/post/DRNCie0jC0j", "profile_url": "https://www.threads.net/@pubity" } ``` #### 📱 Dataset Views | View | Purpose | Data Included | |------|---------|---------------| | **Overview** | Quick Summary | Essential reply data with engagement metrics | | **Content** | Text Focus | Reply text, author info, and engagement data | | **Media** | Media Focus | Images, videos, and media metadata | | **Detailed** | Complete Data | All reply information with full metadata | *** ### 💡 Usage Examples #### 📊 Business Intelligence Use Cases | Use Case | Configuration | Expected Results | |----------|---------------|------------------| | **Content Analysis** | 50-100 replies | Analyze user's reply patterns and engagement | | **Conversation Tracking** | 20-50 replies | Monitor user interactions and discussions | | **Engagement Research** | 50-100 replies | Study reply engagement and content performance | | **Brand Monitoring** | 30-80 replies | Track brand mentions and conversations | #### 🎯 Real-World Scenarios **1. Social Media Manager - Engagement Analysis:** ```json { "usernames": ["nike", "adidas"], "maxReplies": 50 } ``` *Analyze brand's reply patterns and engagement strategies* **2. Content Creator - Conversation Research:** ```json { "usernames": ["mrbeast"], "maxReplies": 100 } ``` *Study creator's reply engagement and audience interaction* **3. Market Researcher - Competitor Analysis:** ```json { "usernames": ["competitor_brand"], "maxReplies": 75 } ``` *Analyze competitor's conversation strategy and engagement* *** ### 🎯 Best Practices #### ✅ **Optimization Strategies** | Strategy | Benefit | Implementation | |----------|---------|----------------| | **Start Small** | Cost Control | Begin with 20-50 replies for testing | | **Batch Processing** | Multiple Users | Process multiple users in single run | | **Regular Monitoring** | Data Quality | Track extraction success rates and data quality | | **Export Management** | Efficiency | Use appropriate export formats for your analysis needs | #### 🔧 **Performance Tips** - ✅ **Smart Limits**: Set reasonable reply limits based on your analysis needs - ✅ **Data Quality**: Monitor engagement metrics for content insights - ✅ **Export Formats**: Choose JSON for detailed analysis, CSV for spreadsheet tools - ✅ **Processing Time**: Allow adequate time for large reply extractions #### ❌ **Common Pitfalls to Avoid** - ❌ Don't extract more replies than necessary for your analysis - ❌ Don't ignore rate limits and platform guidelines - ❌ Don't run multiple extractions simultaneously on the same username *** ### 📈 Performance & Limits #### ⚡ **Performance Metrics** | Metric | Value | Description | |--------|-------|-------------| | **Processing Speed** | ~20 replies/minute | Average replies processed per minute | | **Extraction Time** | 2-3 seconds/reply | Time per reply processing | | **Success Rate** | 99.5%+ | Reliable data extraction | | **Rate Limiting** | Built-in | Automatic throttling | #### 📊 **Usage Limits** | Limit Type | Value | Recommendation | |------------|-------|----------------| | **Max Replies** | 100 replies/user | Optimal for most use cases | | **Optimal Range** | 20-50 replies/user | Best performance for quick analysis | | **Timeout** | 30 seconds/request | Automatic retry on timeout | *** ### 🛠️ Troubleshooting #### 🔍 **Common Issues & Solutions** | Issue | Cause | Solution | |-------|-------|----------| | **No Data Returned** | Invalid username | Verify username exists and is public | | **Partial Data** | Rate limiting | Wait and retry, or reduce reply count | | **Missing Replies** | Private account | Only public replies are accessible | | **Timeout Errors** | Network issues | Actor automatically retries failed requests | #### 📋 **Error Messages Guide** **"Usernames are required"** - Ensure usernames array is provided and not empty **"No valid usernames provided"** - Check username format (no @ symbol, valid characters only) **"Rate limit exceeded"** - Built-in rate limiting activated - extraction will continue automatically *** ### ❓ Frequently Asked Questions #### 🔐 **Authentication & Access** **Q: Do I need a Threads account to use this scraper?** A: No, this actor works with publicly available data and doesn't require any login credentials. **Q: Can I scrape private account replies?** A: No, only public accounts and their public replies can be extracted. Private accounts are not accessible. #### 📊 **Data & Extraction** **Q: How many replies can I extract per run?** A: You can extract up to 100 replies per user. The optimal range is 20-50 replies for best performance. **Q: What data is included for each reply?** A: Complete reply data including text, media URLs, timestamps, author information, verification status, and engagement metrics. **Q: How is the data organized in the output?** A: Each reply is saved as a separate record with complete metadata in multiple organized table views. #### 🛠️ **Technical Questions** **Q: What output formats are available?** A: Multiple dataset views: Overview, content view, media view, and detailed view with all metadata. **Q: How long does extraction take?** A: Typically 2-3 seconds per reply, depending on data size and network conditions. **Q: Can I schedule regular extractions?** A: Yes, use Apify's scheduling feature to run the actor automatically at set intervals. *** ### 🏷️ Tags #### 🎯 **Primary Keywords** - Threads User Replies Scraper - Threads Replies Extractor - Meta Threads Reply Data - Threads Reply Analytics - Threads Conversation Scraper #### 📊 **Secondary Keywords** - Threads replies extraction - Threads conversation analytics - Meta Threads replies - Threads reply mining - Social media conversation data - Threads user replies - Reply engagement tracking - Threads conversation research #### 🔍 **Long-tail Keywords** - How to scrape Threads user replies - Extract reply data from Meta Threads - Threads reply data extraction tool - Automated Threads reply scraping - Threads conversation analysis tool - Meta Threads reply mining software #### 📈 **Industry Terms** - Social media intelligence - Conversation analytics - Content monitoring tools - Reply engagement tracking - Conversation quality analysis - Social listening platform *** ### 🚀 **Get Started Now** **Ready to extract Threads replies and download media content?** 1. Click ["Try for free"](https://apify.com?fpr=ykgg9c) to test the actor 2. Enter your username and configure filters 3. Get comprehensive reply data. **⭐ If this actor helps your workflow, please give it a star!** *** ### Related Actors 🤝 - **[Threads Media Downloader](https://apify.com/igview-owner/threads-media-downloader)** - **[Threads Profile Following Scraper](https://apify.com/igview-owner/threads-user-following-scraper)** - **[Threads Profile Followers Scraper](https://apify.com/igview-owner/threads-user-followers-scraper)** - **[Threads Profile Media Scraper](https://apify.com/igview-owner/threads-user-media-scraper)** - **[Threads Profile Scraper](https://apify.com/igview-owner/meta-threads-profile-scraper)** - **[Threads Search Scraper](https://apify.com/igview-owner/threads-search-scraper)** *** ### 🚀 **Ready to Extract Threads Replies?** **Start your first extraction now and get comprehensive reply data in minutes!** 🎯 **Perfect for**: Social media managers, content creators, researchers, data analysts, and brand monitoring professionals\ 📊 **Use cases**: Conversation analysis, engagement tracking, content monitoring, market research, brand sentiment analysis\ ⚡ **Benefits**: Complete reply data, organized output, conversation insights, reliable extraction *** *Built with ❤️ for the Apify community. Extract smarter, analyze deeper, grow faster.* # Actor input Schema ## `usernames` (type: `array`): 📝 Enter Threads usernames (without @) to extract their replies. Only public accounts are supported. You can add multiple usernames to process them in batch. ## `maxReplies` (type: `integer`): 🔢 Maximum number of replies to extract per username. Set higher for comprehensive extraction or lower for quick sampling. Default is 28 replies. ## Actor input object example ```json { "usernames": [ "pubity" ], "maxReplies": 15 } ``` # Actor output Schema ## `overview` (type: `string`): Open the dataset view with an overview of threads replies. ## `content` (type: `string`): Open the dataset view with detailed content information of replies. ## `media` (type: `string`): Open the dataset view with media information of replies. # 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 = { "usernames": [ "pubity" ], "maxReplies": 15 }; // Run the Actor and wait for it to finish const run = await client.actor("igview-owner/threads-replies-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 = { "usernames": ["pubity"], "maxReplies": 15, } # Run the Actor and wait for it to finish run = client.actor("igview-owner/threads-replies-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 '{ "usernames": [ "pubity" ], "maxReplies": 15 }' | apify call igview-owner/threads-replies-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=igview-owner/threads-replies-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Threads Replies Scraper", "description": "Scrape public replies and comments from Meta Threads users. Get reply text, user info, engagement metrics and media URLs in clean JSON/CSV for analytics and research.", "version": "1.0", "x-build-id": "xGb7figtUbeoN76BW" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/igview-owner~threads-replies-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-igview-owner-threads-replies-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/igview-owner~threads-replies-scraper/runs": { "post": { "operationId": "runs-sync-igview-owner-threads-replies-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/igview-owner~threads-replies-scraper/run-sync": { "post": { "operationId": "run-sync-igview-owner-threads-replies-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "usernames" ], "properties": { "usernames": { "title": "👤 Threads Usernames", "minItems": 1, "maxItems": 1, "uniqueItems": true, "type": "array", "description": "📝 Enter Threads usernames (without @) to extract their replies. Only public accounts are supported. You can add multiple usernames to process them in batch.", "default": [ "pubity" ], "items": { "type": "string" } }, "maxReplies": { "title": "💬 Maximum Replies Per User", "minimum": 10, "maximum": 1000, "type": "integer", "description": "🔢 Maximum number of replies to extract per username. Set higher for comprehensive extraction or lower for quick sampling. Default is 28 replies.", "default": 15 } } }, "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 } } } } } } } } } } ```