# Facebook Posts Search Scraper (`scraper-engine/facebook-posts-search-scraper`) Actor Find public Facebook posts from search results using keywords, hashtags, or topics. This Apify actor extracts post text, authors, timestamps, links, and engagement signals. Great for trend monitoring, audience research, brand tracking, and competitor analysis in structured data. - **URL**: https://apify.com/scraper-engine/facebook-posts-search-scraper.md - **Developed by:** [Scraper Engine](https://apify.com/scraper-engine) (community) - **Categories:** Social media, Lead generation, Automation - **Stats:** 1 total users, 0 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $4.99 / 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 Posts Search Scraper Scrape public Facebook posts by search query. Extracts post content, engagement metrics, and metadata. ### Key Features - **Bulk search** – Multiple queries or URLs in one run - **Proxy fallback** – No proxy by default; auto-fallback to datacenter → residential on block - **Live saving** – Results saved as they’re scraped - **Retry logic** – 3 retries with exponential backoff ### Input | Field | Type | Description | |-------|------|-------------| | `startUrls` | array | Facebook search URLs (e.g. `https://www.facebook.com/search/top/?q=football`) | | `searchQueries` | array | Search keywords (used if `startUrls` is empty) | | `maxPosts` | integer | Max posts per query (1–500, default: 50) | | `postTimeRange` | string | Filter: `24h`, `7d`, `30d`, `90d` | | `proxyConfiguration` | object | Proxy settings (default: no proxy) | **Example input:** ```json { "searchQueries": ["football", "cricket"], "maxPosts": 50, "postTimeRange": "", "proxyConfiguration": { "useApifyProxy": false } } ```` ### Output Each post includes: - `facebookUrl`, `pageId`, `pageName` – Page info - `postId`, `url`, `topLevelUrl` – Post identifiers - `time`, `timestamp` – Post date - `likes`, `comments`, `shares` – Engagement - `text`, `link`, `thumb` – Content - `searchQuery` – Query that matched ### How to Use 1. Open [Apify Console](https://console.apify.com) → **Actors** 2. Select **facebook-posts-search-scraper** 3. Set inputs (queries, max posts, proxy) 4. Run the actor 5. View results in the **OUTPUT** tab 6. Export as JSON or CSV ### Proxy Behavior - **Default:** No proxy - **On block:** Datacenter proxy (SHADER) - **If still blocked:** Residential proxy with 3 retries - **After fallback:** Remaining requests use the new proxy ### Cautions - Data is from **public** sources only - You are responsible for legal compliance (privacy, ToS, etc.) # Actor input Schema ## `searchQueries` (type: `array`): ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 💡 Enter topics you want to explore! Examples: football • cricket • tech news • recipes • travel 🎯 Each keyword = a separate search → more posts discovered ## `maxPosts` (type: `integer`): ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📈 How many posts to collect per keyword? 🎚️ Range: 1 → 5,000 posts 💯 Default: 50 (quick runs) 🚀 Need more? Crank it up to 5000! ## `postTimeRange` (type: `string`): ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📅 Filter by recency—get only fresh content! ⏳ All time (default) • 24h • 7d • 30d • 90d ## `proxyConfiguration` (type: `object`): ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🛡️ Proxy configuration 💫 Works great out of the box—Apify Proxy enabled by default! ⚙️ Adjust only when you need custom setup. ## Actor input object example ```json { "searchQueries": [ "football" ], "maxPosts": 50, "postTimeRange": "", "proxyConfiguration": { "useApifyProxy": true } } ``` # 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 = { "searchQueries": [ "football" ], "proxyConfiguration": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("scraper-engine/facebook-posts-search-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 = { "searchQueries": ["football"], "proxyConfiguration": { "useApifyProxy": True }, } # Run the Actor and wait for it to finish run = client.actor("scraper-engine/facebook-posts-search-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 '{ "searchQueries": [ "football" ], "proxyConfiguration": { "useApifyProxy": true } }' | apify call scraper-engine/facebook-posts-search-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scraper-engine/facebook-posts-search-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Facebook Posts Search Scraper", "description": "Find public Facebook posts from search results using keywords, hashtags, or topics. This Apify actor extracts post text, authors, timestamps, links, and engagement signals. Great for trend monitoring, audience research, brand tracking, and competitor analysis in structured data.", "version": "0.1", "x-build-id": "wkaSr17QC3OxgVm2r" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scraper-engine~facebook-posts-search-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scraper-engine-facebook-posts-search-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scraper-engine~facebook-posts-search-scraper/runs": { "post": { "operationId": "runs-sync-scraper-engine-facebook-posts-search-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scraper-engine~facebook-posts-search-scraper/run-sync": { "post": { "operationId": "run-sync-scraper-engine-facebook-posts-search-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "searchQueries": { "title": "🔑 Search Keywords", "type": "array", "description": "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n💡 Enter topics you want to explore!\n\nExamples: football • cricket • tech news • recipes • travel\n\n🎯 Each keyword = a separate search → more posts discovered", "default": [ "" ], "items": { "type": "string" } }, "maxPosts": { "title": "📊 Max Posts per Query", "minimum": 1, "maximum": 5000, "type": "integer", "description": "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n📈 How many posts to collect per keyword?\n\n🎚️ Range: 1 → 5,000 posts\n💯 Default: 50 (quick runs)\n🚀 Need more? Crank it up to 5000!", "default": 50 }, "postTimeRange": { "title": "⏱️ Time Filter", "enum": [ "", "24h", "7d", "30d", "90d" ], "type": "string", "description": "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n📅 Filter by recency—get only fresh content!\n\n⏳ All time (default) • 24h • 7d • 30d • 90d", "default": "" }, "proxyConfiguration": { "title": "🌐 Proxy Settings", "type": "object", "description": "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n🛡️ Proxy configuration\n\n💫 Works great out of the box—Apify Proxy enabled by default!\n⚙️ Adjust only when you need custom setup." } } }, "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 } } } } } } } } } } ```