# Search & Scrape Social Media Posts by Keyword (`mrscrap/search-scrape-social-media-posts-by-keyword`) Actor Search any keyword and instantly scrape social media posts from TikTok, YouTube, Reddit, Instagram, Facebook, Twitter/X, and LinkedIn. Extracts likes, comments, and shares — no API keys needed. Filter by country, language, and date range. - **URL**: https://apify.com/mrscrap/search-scrape-social-media-posts-by-keyword.md - **Developed by:** [Mr Scrap](https://apify.com/mrscrap) (community) - **Categories:** Social media, Automation, Integrations - **Stats:** 2 total users, 1 monthly users, 66.7% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $1.50 / 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. 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 ## Social Media Post Scraper **Instantly find out what social media is saying about any topic — across 7 major platforms.** Want to know how people are reacting to a brand, a trend, or a keyword on TikTok, YouTube, Reddit, and more? This actor searches Google for the most relevant social media posts on your topic and extracts their engagement metrics (likes, comments, shares) — giving you a clear picture of the public conversation, without needing API keys or platform credentials. --- ### What You Get - **Live engagement data** — likes, comments, and shares pulled directly from post pages - **7 platforms in one run** — YouTube, Reddit, TikTok, Instagram, Facebook, Twitter/X, LinkedIn - **Keyword-driven discovery** — find trending content for any topic, in any country or language - **Structured output** — clean JSON dataset ready for analysis, reporting, or integration - **Markdown report** — human-readable summary exported alongside your dataset --- ### Supported Platforms | Platform | Likes | Comments | Shares | Reliability | |----------|-------|----------|--------|-------------| | YouTube | ✅ | ✅ | — | High (~90%) | | Reddit | ✅ | ✅ | — | High (~90%) | | TikTok | ⚠️ | ⚠️ | ⚠️ | Medium (~50%) — anti-bot risk | | Instagram | ⚠️ | — | — | Low (<20%) — login wall | | Facebook | ⚠️ | ⚠️ | ⚠️ | Low (<20%) — login wall | | Twitter / X | ⚠️ | ⚠️ | ⚠️ | Low (<30%) — soft login wall | | LinkedIn | — | — | — | Very low — login required | > On login-protected platforms, engagement data is extracted from Google's search snippets when direct access is blocked. > The `source` field in the output indicates whether data came from `"post_page"` (live) or `"google"` (snippet fallback). --- ### Input | Field | Type | Required | Default | Description | |-------|------|----------|---------|-------------| | `keyword` | string | **YES** | — | Topic to search (e.g. `"AI tools USA"`) | | `platforms` | array | no | all 7 | Platforms to include | | `country` | string | no | `"us"` | Google country code (e.g. `"fr"`, `"ma"`) | | `language` | string | no | `""` | Google language code (e.g. `"fr"`, `"ar"`) | | `date_range` | enum | no | `"week"` | `hour` / `day` / `week` / `month` / `year` | | `max_results_per_platform` | integer | no | `5` | Posts per platform (1–20) | | `scrape_post_pages` | boolean | no | `true` | Fetch live metrics from post pages | | `concurrency` | integer | no | `10` | Parallel requests (1–10) | --- ### Output One item per post in your Apify dataset: ```json { "keyword": "AI tools USA", "platform": "youtube", "title": "Best AI Tools For Developers In USA | 2024", "url": "https://youtube.com/watch?v=abc123", "likes": "14231", "comments": "847", "shares": "—", "source": "post_page" } ```` A local Markdown report is also saved to `results/report_*.md` at the end of each run. *** ### Use Cases - **Social media monitoring** — track what content performs best on your topic - **Competitor research** — see which posts drive the most engagement in your niche - **Content strategy** — identify trending formats and topics before creating content - **Market research** — analyze public sentiment and content trends by country/language # Actor input Schema ## `keyword` (type: `string`): Topic or phrase to search for (e.g. 'AI tools Morocco 2024') ## `platforms` (type: `array`): Which platforms to search. Leave empty to search all 7 platforms. ## `country` (type: `string`): Google country for geotargeted results. ## `language` (type: `string`): Language of Google search results. Select 'Any language' to get results in all languages. ## `date_range` (type: `string`): How recent the Google results must be ## `max_results_per_platform` (type: `integer`): Maximum number of posts to extract per platform (1–20). Each post = one dataset item. ## `scrape_post_pages` (type: `boolean`): Visit each post URL to extract live engagement counts. If false, only Google snippet data is used (faster but less accurate for some platforms). ## `concurrency` (type: `integer`): Number of parallel post-page crawls (1–10). Lower values are safer on anti-bot platforms. ## Actor input object example ```json { "platforms": [ "tiktok", "instagram", "youtube", "facebook", "twitter", "linkedin", "reddit" ], "country": "us", "language": "", "date_range": "week", "max_results_per_platform": 5, "scrape_post_pages": true, "concurrency": 10 } ``` # Actor output Schema ## `results` (type: `string`): Dataset of social media posts with likes, comments, shares, and source platform. # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("mrscrap/search-scrape-social-media-posts-by-keyword").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 = {} # Run the Actor and wait for it to finish run = client.actor("mrscrap/search-scrape-social-media-posts-by-keyword").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 '{}' | apify call mrscrap/search-scrape-social-media-posts-by-keyword --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=mrscrap/search-scrape-social-media-posts-by-keyword", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Search & Scrape Social Media Posts by Keyword", "description": "Search any keyword and instantly scrape social media posts from TikTok, YouTube, Reddit, Instagram, Facebook, Twitter/X, and LinkedIn. Extracts likes, comments, and shares — no API keys needed. Filter by country, language, and date range.", "version": "0.0", "x-build-id": "VWTb1pam7ljXo1Aha" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/mrscrap~search-scrape-social-media-posts-by-keyword/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-mrscrap-search-scrape-social-media-posts-by-keyword", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/mrscrap~search-scrape-social-media-posts-by-keyword/runs": { "post": { "operationId": "runs-sync-mrscrap-search-scrape-social-media-posts-by-keyword", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/mrscrap~search-scrape-social-media-posts-by-keyword/run-sync": { "post": { "operationId": "run-sync-mrscrap-search-scrape-social-media-posts-by-keyword", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "keyword" ], "properties": { "keyword": { "title": "Search keyword", "type": "string", "description": "Topic or phrase to search for (e.g. 'AI tools Morocco 2024')" }, "platforms": { "title": "Social media platforms", "type": "array", "description": "Which platforms to search. Leave empty to search all 7 platforms.", "items": { "type": "string", "enum": [ "tiktok", "instagram", "youtube", "facebook", "twitter", "linkedin", "reddit" ], "enumTitles": [ "TikTok", "Instagram", "YouTube", "Facebook", "Twitter / X", "LinkedIn", "Reddit" ] }, "default": [ "tiktok", "instagram", "youtube", "facebook", "twitter", "linkedin", "reddit" ] }, "country": { "title": "Country", "enum": [ "us", "gb", "fr", "de", "es", "it", "pt", "br", "ca", "au", "nz", "ma", "dz", "tn", "ly", "eg", "sa", "ae", "qa", "kw", "bh", "om", "jo", "lb", "iq", "sy", "ye", "tr", "ir", "pk", "in", "bd", "lk", "np", "ng", "gh", "ke", "za", "sn", "ci", "cm", "et", "tz", "ug", "ao", "mz", "ru", "pl", "ua", "cz", "hu", "ro", "bg", "rs", "hr", "sk", "si", "at", "ch", "be", "nl", "se", "no", "dk", "fi", "gr", "il", "jp", "kr", "cn", "tw", "hk", "sg", "my", "id", "th", "vn", "ph", "mm", "mx", "ar", "co", "cl", "pe", "ve", "ec", "bo", "py", "uy" ], "type": "string", "description": "Google country for geotargeted results.", "default": "us" }, "language": { "title": "Language", "enum": [ "", "en", "fr", "ar", "es", "de", "it", "pt", "nl", "ru", "pl", "uk", "cs", "hu", "ro", "bg", "hr", "sk", "sl", "el", "sv", "no", "da", "fi", "tr", "he", "fa", "hi", "ur", "bn", "ta", "te", "mr", "gu", "kn", "ml", "pa", "id", "ms", "th", "vi", "tl", "ja", "ko", "zh-CN", "zh-TW", "my", "km", "lo", "si", "sw", "am", "ha", "yo", "ig", "zu", "af", "so" ], "type": "string", "description": "Language of Google search results. Select 'Any language' to get results in all languages.", "default": "" }, "date_range": { "title": "Date range", "enum": [ "", "hour", "day", "week", "month", "year" ], "type": "string", "description": "How recent the Google results must be", "default": "week" }, "max_results_per_platform": { "title": "Max results per platform", "minimum": 1, "maximum": 20, "type": "integer", "description": "Maximum number of posts to extract per platform (1–20). Each post = one dataset item.", "default": 5 }, "scrape_post_pages": { "title": "Scrape post pages for live metrics", "type": "boolean", "description": "Visit each post URL to extract live engagement counts. If false, only Google snippet data is used (faster but less accurate for some platforms).", "default": true }, "concurrency": { "title": "Concurrency", "minimum": 1, "maximum": 10, "type": "integer", "description": "Number of parallel post-page crawls (1–10). Lower values are safer on anti-bot platforms.", "default": 10 } } }, "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 } } } } } } } } } } ```