# Facebook Comments Scraper (`solid-scraper/facebook-comments-scraper`) Actor 📣 Facebook Comments Scraper extracts public comments fast for insights, competitor research, and audience analysis. Capture keywords, sentiment cues, and engagement data instantly—ideal for marketers, agencies, and researchers. 🚀🔎 - **URL**: https://apify.com/solid-scraper/facebook-comments-scraper.md - **Developed by:** [SolidScraper](https://apify.com/solid-scraper) (community) - **Categories:** Social media, Lead generation, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.99 / 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 ### Facebook Comments Scraper 🔍 **Facebook Comments Scraper** extracts comment threads from Facebook post URLs so you can collect, review, and analyze public discussion at scale. It helps teams that need a **facebook comments scraper** for **scrape facebook comments** workflows—whether you’re building datasets for **facebook comments export**, researching community sentiment, or powering downstream reporting. Whether you’re a marketer, data analyst, or researcher, this actor simplifies **facebook post comments extractor** tasks by turning public comment data into a structured dataset you can use immediately—saving you hours of manual work. --- ### Why choose Facebook Comments Scraper? | Feature | Benefit | |---|---| | ✅ **All-in-one comments extraction** | Extracts comment text, dates, and engagement metrics from each provided Facebook post URL | | ✅ **Configurable comment ranking** | Lets you choose between **Most relevant**, **Newest**, and **All** comments mode for your use case | | ✅ **Optional inclusion of replies** | Supports scraping replies to comments when you enable `includeReplies` | | ✅ **Reliability-focused execution** | Includes retries and fallbacks for resilient collection across runs | | ✅ **Structured dataset output** | Saves results in a consistent dataset schema ready for analysis | | ✅ **Scale-friendly bulk runs** | Process multiple post URLs in a single actor run using `startUrls` | --- ### Key features - 🔎 **Facebook post comments extraction**: Scrapes comment threads from the Facebook post URLs you provide in `startUrls`. - 🧾 **Structured output for analysis**: Produces fields like `text`, `date`, `likesCount`, `commentsCount`, and `facebookUrl` for each comment. - 🗂️ **Comments mode control**: Choose **Most relevant**, **Newest**, or **All** to match how you want comments prioritized. - 💬 **Optional reply scraping**: Enable `includeReplies` to also pull replies in your collected thread data. - 🧠 **Thread-aware parsing**: Captures comment URLs/IDs and author metadata (e.g., `profileName`, `profileId`, `profilePicture`) alongside the comment text. - 🛡️ **Retry and resilience handling**: Uses retries with exponential backoff during requests to improve completion reliability. - 💾 **Dataset-ready saving**: Pushes results to an Apify dataset continuously during pagination. - 🌐 **URL list input**: Accepts a list of Facebook URLs so you can run **bulk facebook comments scraper** workflows. --- ### Input Provide input via an `input.json` file. Example structure: ```json { "startUrls": [ { "url": "https://www.facebook.com/humansofnewyork/posts/pfbid0BbKbkisExKGSKuhee9a7i86RwRuMKFC8NSkKStB7CsM3uXJuAAfZLrkcJMXxhH4Yl" } ], "resultsAmount": 50, "includeReplies": false, "commentsMode": "All" } ```` #### Input Fields | Field | Required | Description | |---|---:|---| | `startUrls` | ✅ | List of Facebook post URLs to scrape (each item can be an object with `url`, or a plain string URL depending on how you build your input). | | `resultsAmount` | ❌ | Maximum number of comments to scrape per URL. Defaults to `50`. | | `includeReplies` | ❌ | If enabled, replies to comments will also be scraped. Defaults to `false`. | | `commentsMode` | ❌ | Ranking mode for comments: `Most relevant`, `Newest`, or `All`. Defaults to `All`. | *** ### Output The actor saves each collected comment record to the default Apify dataset as structured JSON objects. ```json [ { "facebookUrl": "https://www.facebook.com/example", "commentUrl": "https://www.facebook.com/some/path?comment_id=123", "commentId": "123", "id": "4567890", "feedbackId": null, "date": "2024-01-01T00:00:00.000Z", "text": "This is the comment text", "profilePicture": "https://example.com/avatar.jpg", "profileId": "111222333", "profileName": "Author Name", "likesCount": "12", "commentsCount": 3, "comments": [], "threadingDepth": 0, "pageAdLibrary": { "is_business_page_active": false, "id": "999999999" }, "facebookId": "987654321", "postTitle": "Optional post title if available", "inputUrl": "https://www.facebook.com/example" } ] ``` #### Output Fields | Field | Type | Description | |---|---|---| | `facebookUrl` | string | Link to the Facebook post the comment belongs to. | | `commentUrl` | string | Direct URL to the comment (when available). | | `commentId` | string | null | Comment identifier extracted from the comment URL or legacy identifiers (when available). | | `id` | string | null | Internal relay ID for the comment node (labeled as relay ID in code). | | `feedbackId` | string | null | Feedback identifier associated with the comment (when available). | | `date` | string | null | Comment timestamp formatted in Zulu/ISO style (e.g. `...Z`). | | `text` | string | null | The comment text content. | | `profilePicture` | string | null | URL to the author’s profile picture URI (when available). | | `profileId` | string | null | Author profile ID. | | `profileName` | string | null | Author display name for the commenter. | | `likesCount` | string | Like/reaction count stored as a string in the extracted record. | | `commentsCount` | number | Reply count (stored as `replies_fields.total_count` when available). | | `comments` | array | Placeholder for nested comments (initialized as an empty array in the extracted records). | | `threadingDepth` | number | Initialized to `0` in extracted records. | | `facebookId` | string | null | Post-level Facebook legacy ID (when available). | | `postTitle` | string | null | Post title/message text if it can be found in the page data. | | `inputUrl` | string | The input URL used as the scraping target (defaults to the target URL). | | `pageAdLibrary` | object | null | Page metadata attached per item (includes `is_business_page_active` and `id`). | > Export formats: results are stored in an Apify dataset and can be exported from the Apify Console (e.g., JSON and CSV depending on your workflow). *** ### How to use Facebook Comments Scraper (via Apify Console) 1. **Open Apify Console**\ Log in at [console.apify.com](https://console.apify.com) and go to the **Actors** tab. 2. **Find Facebook Comments Scraper**\ Search for **Facebook Comments Scraper** and open the actor details page. 3. **Go to the INPUT section**\ Use the built-in input form to add your Facebook post URLs in `startUrls`. 4. **Set your limits and behavior**\ Configure `resultsAmount` (max comments per URL), decide whether `includeReplies` should be enabled, and choose `commentsMode` (`Most relevant`, `Newest`, or `All`). 5. **(Optional) Adjust proxy settings**\ If you’re running into access issues, you can set proxy configuration in the run input. This actor includes built-in proxy support and resilience features for more reliable scraping. 6. **Click Run**\ Start the actor run and monitor logs. You should see progress as it fetches and paginates through comment data. 7. **Open the OUTPUT dataset**\ After completion, check the **Results** dataset where each comment record is saved. 8. **Export your data**\ Export the dataset for reporting, analysis, or a **facebook comments export** pipeline (commonly to JSON/CSV depending on your needs). No coding required—get accurate results in minutes with this **facebook comments scraper**. *** ### Advanced features & SEO optimization - 🔧 **Engineered for Facebook comments analysis scraper workflows**: Designed to collect key engagement signals like `likesCount` and `commentsCount` alongside the comment `text` for easier downstream analysis of public discussion. - 🎛️ **Precise control with comments mode**: Use `commentsMode` to switch between **Most relevant**, **Newest**, and **All** comment ranking. - 📌 **Bulk-ready dataset building**: Feed multiple URLs through `startUrls` to power **facebook comments collection tool** use cases and **bulk facebook comments scraper** runs. - 🔄 **Resilient pagination & retries**: Includes retries and fallbacks to improve completion success across different targets. - 💾 **Structured output schema**: Consistent fields like `profileName`, `date`, `facebookUrl`, and `commentUrl` make results easy to import into your pipelines. *** ### Best use cases - 📈 **Marketing teams building community insights**: Extract public discussion from multiple posts and assemble a clean dataset for sentiment or topic analysis. - 🧑‍🔬 **Researchers analyzing engagement patterns**: Compare `likesCount` and reply volume (`commentsCount`) across posts and time. - 🗞️ **Content strategists validating audience response**: Quickly review comment text and metadata after launching content, then export for reporting. - 🧑‍💼 **Community managers monitoring feedback**: Collect comments (and optionally replies) into a single place for review and moderation workflows. - 🧱 **Data analysts preparing structured datasets**: Use the consistent output fields to join comment data with other sources in a data warehouse. - 💻 **Developers automating export pipelines**: Integrate dataset outputs into scheduled workflows for a **facebook comment downloader** style automation. - 🧠 **Lead research & outreach preparation**: When you use **publicly available data** ethically, you can aggregate commenter profiles to inform outreach research (always follow relevant policies and laws). *** ### Technical specifications **Supported Input Formats** - ✅ `startUrls`: List of Facebook post URLs (as objects with `url`, or plain URL strings depending on your input). - ✅ `resultsAmount` (integer): Maximum comments per URL (default `50`). - ✅ `includeReplies` (boolean): Whether replies are included (default `false`). - ✅ `commentsMode` (string): `Most relevant`, `Newest`, or `All` (default `All`). **Proxy Support** - ✅ Built-in proxy support for more reliable scraping runs. - ❗ Proxy configuration can be provided via input (the actor uses proxy settings when available). **Retry Mechanism** - ✅ Retries are used during requests with exponential backoff to improve robustness. **Dataset Structure** - ✅ Default dataset view titled **Results** with columns for: `profileName`, `text`, `date`, `likesCount`, `commentsCount`, and `facebookUrl`. **Rate Limits & Performance** - ⚠️ Performance varies based on target availability and access conditions. - ✅ The actor respects `resultsAmount` as a cap per URL during pagination. **Limitations** - ❌ Private/restricted content is not guaranteed to be accessible. - ❌ Some fields (e.g. `commentUrl`, `commentId`, or `postTitle`) may be null when not available in the extracted data. *** ### FAQ #### What does the Facebook Comments Scraper actually scrape? ✅ It scrapes public comment data from the Facebook post URLs you provide in `startUrls`, returning fields such as comment `text`, `date`, `likesCount`, and author information like `profileName` and `profileId`. #### Can I scrape replies, not just top-level comments? ✅ Yes. Enable `includeReplies` to also scrape replies to comments (default is `false`). #### How do commentsMode options affect the results? ✅ `commentsMode` controls the ranking behavior for comments using one of these values: `Most relevant`, `Newest`, or `All` (default: `All`). #### How many comments do I get per URL? ✅ The actor uses `resultsAmount` as the maximum number of comments to scrape per URL (default: `50`). #### Where do the results go? ✅ Results are saved to the actor’s default Apify dataset under the **Results** view, with table columns like `profileName`, `text`, `date`, `likesCount`, `commentsCount`, and `facebookUrl`. #### Is there a way to use this in an automated workflow? ✅ Yes. You can run the actor with input JSON and then export the dataset output to your downstream systems as part of a pipeline—perfect for a **facebook comments collection tool** or **facebook comments export** process. #### Do I need coding to use it? ✅ No. You can use Apify Console’s INPUT form to provide `startUrls`, configure `resultsAmount`, `includeReplies`, and `commentsMode`, then export your dataset. #### Is it legal to scrape public Facebook comments? ✅ The actor accesses **publicly available sources** only, but whether scraping is permitted depends on applicable laws and platform policies. You’re responsible for compliance (including GDPR/CCPA where relevant) and for using the data ethically. *** ### Support & feature requests Have questions or want to improve this Facebook Comments Scraper? We’d love your feedback. 💬 - 💡 **Feature Requests**: Examples include exporting directly to CSV, adding more comment/thread fields, or supporting additional output formats for **facebook comments analysis scraper** workflows. - 📧 **Contact**: Send support requests to . User feedback directly shapes the roadmap for this **facebook comments scraper**. *** ### Closing CTA / Final thoughts *If you need the most comprehensive and SEO-optimized way to scrape and export Facebook comment data, **Facebook Comments Scraper** is built for exactly that.*\ Run it against your post URLs, set your `resultsAmount`, and start building your **facebook comments export** dataset today. *** ### Disclaimer **This tool only accesses publicly accessible sources**. It does not access private profiles, authenticated data, or password-protected pages. You are responsible for ensuring your use complies with applicable laws and regulations (including GDPR/CCPA where relevant), spam and privacy rules, and the platform’s Terms of Service. For data removal requests, contact . Please use this actor responsibly, ethically, and for legitimate purposes only. # Actor input Schema ## `startUrls` (type: `array`): List of Facebook post URLs to scrape. ## `resultsAmount` (type: `integer`): Maximum comments to scrape per URL. ## `includeReplies` (type: `boolean`): If enabled, replies to comments will also be scraped. ## `commentsMode` (type: `string`): Ranking mode for comments. ## Actor input object example ```json { "startUrls": [ { "url": "https://www.facebook.com/humansofnewyork/posts/pfbid0BbKbkisExKGSKuhee9a7i86RwRuMKFC8NSkKStB7CsM3uXJuAAfZLrkcJMXxhH4Yl" } ], "resultsAmount": 50, "includeReplies": false, "commentsMode": "All" } ``` # 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 = { "startUrls": [ { "url": "https://www.facebook.com/humansofnewyork/posts/pfbid0BbKbkisExKGSKuhee9a7i86RwRuMKFC8NSkKStB7CsM3uXJuAAfZLrkcJMXxhH4Yl" } ] }; // Run the Actor and wait for it to finish const run = await client.actor("solid-scraper/facebook-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 = { "startUrls": [{ "url": "https://www.facebook.com/humansofnewyork/posts/pfbid0BbKbkisExKGSKuhee9a7i86RwRuMKFC8NSkKStB7CsM3uXJuAAfZLrkcJMXxhH4Yl" }] } # Run the Actor and wait for it to finish run = client.actor("solid-scraper/facebook-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 '{ "startUrls": [ { "url": "https://www.facebook.com/humansofnewyork/posts/pfbid0BbKbkisExKGSKuhee9a7i86RwRuMKFC8NSkKStB7CsM3uXJuAAfZLrkcJMXxhH4Yl" } ] }' | apify call solid-scraper/facebook-comments-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=solid-scraper/facebook-comments-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Facebook Comments Scraper", "description": "📣 Facebook Comments Scraper extracts public comments fast for insights, competitor research, and audience analysis. Capture keywords, sentiment cues, and engagement data instantly—ideal for marketers, agencies, and researchers. 🚀🔎", "version": "0.1", "x-build-id": "CKSNk2RgnOgVRVc8j" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/solid-scraper~facebook-comments-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-solid-scraper-facebook-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/solid-scraper~facebook-comments-scraper/runs": { "post": { "operationId": "runs-sync-solid-scraper-facebook-comments-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/solid-scraper~facebook-comments-scraper/run-sync": { "post": { "operationId": "run-sync-solid-scraper-facebook-comments-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "startUrls" ], "properties": { "startUrls": { "title": "Facebook URLs", "type": "array", "description": "List of Facebook post URLs to scrape.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "resultsAmount": { "title": "Results amount", "type": "integer", "description": "Maximum comments to scrape per URL.", "default": 50 }, "includeReplies": { "title": "Include comment replies", "type": "boolean", "description": "If enabled, replies to comments will also be scraped.", "default": false }, "commentsMode": { "title": "Comments mode", "enum": [ "Most relevant", "Newest", "All" ], "type": "string", "description": "Ranking mode for comments.", "default": "All" } } }, "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 } } } } } } } } } } ```