# Instagram Post Comments Scraper (`shahidirfan/instagram-post-comments-scraper`) Actor Extract Instagram post comments at scale. Scrape comments, likes, replies, user profiles & timestamps instantly. Perfect for sentiment analysis, social listening, influencer research, competitor tracking & brand monitoring. Production-ready JSON/CSV output. - **URL**: https://apify.com/shahidirfan/instagram-post-comments-scraper.md - **Developed by:** [Shahid Irfan](https://apify.com/shahidirfan) (community) - **Categories:** Social media, Automation, Videos - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## Instagram Comments Scraper Extract comments from any Instagram post or reel with ease. Collect valuable user feedback, author usernames, reply count, and likes at scale. Perfect for sentiment analysis, social listening, and competitor research. --- ### Features - **Multi-URL scraping** — Scrape comments from multiple Instagram posts in a single run - **Engagement metrics** — Retrieve comment likes and author details - **Reply tracking** — Capture parent-child comment structures, including replies and reply counts - **Verification status** — Identify verified authors and influencer profiles --- ### Use Cases #### Brand Sentiment Analysis Understand how users feel about your brand or products. Analyze comment content to track positive, negative, or neutral sentiment over time. #### Influencer Tracking Audit influencer posts by reviewing engagement and verifying the quality of comments. Ensure comment threads are authentic and relevant. #### Market Intelligence Monitor competitor posts to see how their audience reacts. Identify trending questions, customer issues, and feature requests. --- ### Input Parameters | Parameter | Type | Required | Default | Description | |-----------|------|----------|---------|-------------| | `urls` | Array | Yes | — | List of Instagram post or reel URLs to scrape | | `results_wanted` | Integer | No | `20` | Maximum comments to collect per URL | | `maxPages` | Integer | No | `1` | Maximum number of pagination pages to load | --- ### Output Data Each item in the dataset contains the following fields: | Field | Type | Description | |-------|------|-------------| | `Comment Text` | String | The content of the comment | | `Comment Media` | String | URL to media attached to the comment | | `Comment Date` | String | ISO timestamp of the comment creation | | `Comment Likes` | String | Number of likes on the comment | | `Author Username` | String | Username of the commenter | | `Author ID` | String | Unique Instagram ID of the author | | `Author URL` | String | Link to the author profile | | `Author Is Verified` | String | Verification status of the commenter | | `Is Reply` | String | Indicates if the comment is a reply | | `Parent Comment ID` | String | ID of the parent comment | | `Has Replies` | String | Indicates if the comment has child replies | | `Reply Count` | String | Number of replies to this comment | --- ### Usage Examples #### Basic Extraction Scrape the first few comments of an Instagram post: ```json { "urls": [ "https://www.instagram.com/p/C8e6f-xOB4i/" ], "results_wanted": 20 } ```` #### Deep Pagination Collect a larger volume of comments with deeper pagination: ```json { "urls": [ "https://www.instagram.com/p/C8e6f-xOB4i/" ], "results_wanted": 100, "maxPages": 5 } ``` *** ### Sample Output ```json { "Comment Text": "This is an amazing post!", "Comment Media": null, "Comment Date": "2024-06-21T12:30:45Z", "Comment Likes": "15", "Author Username": "travel_fanatic", "Author ID": "5544332211", "Author URL": "https://www.instagram.com/travel_fanatic/", "Author Is Verified": "false", "Is Reply": "false", "Parent Comment ID": null, "Has Replies": "true", "Reply Count": "2" } ``` *** ### Tips for Best Results #### Limit Test Scrapes - Start with `results_wanted` set to 20 for initial verification - Keep runs fast to save resources and check output format #### Verify URLs - Ensure URLs are public and point directly to a post or reel - Example: `https://www.instagram.com/p/[code]/` or `https://www.instagram.com/reel/[code]/` *** ### Integrations Connect your data with: - **Google Sheets** — Export directly for spreadsheet analysis - **Airtable** — Build comment databases - **Make / Zapier** — Automate notification and analysis workflows #### Export Formats - **JSON** — For developers and custom APIs - **CSV** — For easy data analysis and dashboard importing *** ### Frequently Asked Questions #### Can I collect replies? Yes, the scraper identifies replies and associates them with their parent comments. #### How many comments can I extract? There is no hard limit. You can configure `results_wanted` to match your requirements. #### Do I need to log in? No, the scraper handles requests publicly without requiring Instagram accounts or cookies. *** ### Support For issues or feature requests, contact support through the Apify Console. #### Resources - [Apify Documentation](https://docs.apify.com/) - [API Reference](https://docs.apify.com/api/v2) *** ### Legal Notice This actor is designed for legitimate data collection. Users are responsible for ensuring compliance with website terms of service and applicable local laws. # Actor input Schema ## `urls` (type: `array`): URLs to scrape. Must match: instagram.com ## `results_wanted` (type: `integer`): Maximum number of comments to extract per URL ## `maxPages` (type: `integer`): Maximum number of pagination actions (pages, scrolls, or loads) ## Actor input object example ```json { "urls": [ "https://www.instagram.com/p/DZz9FpYMK6d/" ], "results_wanted": 20, "maxPages": 1 } ``` # Actor output Schema ## `results` (type: `string`): A link to the dataset containing the scraped comments. # 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 = { "urls": [ "https://www.instagram.com/p/DZz9FpYMK6d/" ], "results_wanted": 20 }; // Run the Actor and wait for it to finish const run = await client.actor("shahidirfan/instagram-post-comments-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 = { "urls": ["https://www.instagram.com/p/DZz9FpYMK6d/"], "results_wanted": 20, } # Run the Actor and wait for it to finish run = client.actor("shahidirfan/instagram-post-comments-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 '{ "urls": [ "https://www.instagram.com/p/DZz9FpYMK6d/" ], "results_wanted": 20 }' | apify call shahidirfan/instagram-post-comments-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=shahidirfan/instagram-post-comments-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Instagram Post Comments Scraper", "description": "Extract Instagram post comments at scale. Scrape comments, likes, replies, user profiles & timestamps instantly. Perfect for sentiment analysis, social listening, influencer research, competitor tracking & brand monitoring. Production-ready JSON/CSV output.", "version": "0.0", "x-build-id": "QfahaRA1mUfHQUmiY" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/shahidirfan~instagram-post-comments-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-shahidirfan-instagram-post-comments-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/shahidirfan~instagram-post-comments-scraper/runs": { "post": { "operationId": "runs-sync-shahidirfan-instagram-post-comments-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/shahidirfan~instagram-post-comments-scraper/run-sync": { "post": { "operationId": "run-sync-shahidirfan-instagram-post-comments-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": [ "urls" ], "properties": { "urls": { "title": "URLs", "type": "array", "description": "URLs to scrape. Must match: instagram.com", "items": { "type": "string" }, "default": [ "https://www.instagram.com/p/DZz9FpYMK6d/" ] }, "results_wanted": { "title": "Results Wanted", "minimum": 1, "type": "integer", "description": "Maximum number of comments to extract per URL", "default": 20 }, "maxPages": { "title": "Max pagination steps", "minimum": 1, "type": "integer", "description": "Maximum number of pagination actions (pages, scrolls, or loads)", "default": 1 } } }, "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 } } } } } } } } } } ```