# Viral Video Finder Scraper & Topic Finder (`intelscrape/youtube-viral-video-finder-scraper`) Actor Viral Video Finder Scraper Scrape any YouTube channel to find viral videos. This tool automatically calculates baseline averages, extracts views and URLs, and uses AI to generate actionable content strategies based on outlier multipliers. Perfect for competitor analysis and YouTube topic research. - **URL**: https://apify.com/intelscrape/youtube-viral-video-finder-scraper.md - **Developed by:** [IntelScrape](https://apify.com/intelscrape) (community) - **Categories:** AI, Videos, Social media - **Stats:** 4 total users, 2 monthly users, 100.0% runs succeeded, 1 bookmarks - **User rating**: No ratings yet ## Pricing from $30.00 / 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 ## YouTube Viral Video Scraper & Topic Finder 🚀 Stop guessing what content works. Start mathematically proving it. The **YouTube Viral Video Scraper & Topic Finder** is an advanced competitor analysis tool designed for YouTubers, content strategists, and marketing agencies. It scrapes the latest videos from any YouTube channel, calculates the channel's exact baseline performance, and uses an AI strategy engine to instantly surface their most viral "Outlier" topics. Instead of manually scrolling through hundreds of videos, this tool gives you a perfectly sorted content roadmap in seconds. ### ✨ Key Features * **📈 Automatic Baseline Math:** It calculates the channel's 30-video moving average to establish a true benchmark. * **🌟 Outlier Multiplier Engine:** It divides every video's views by the channel baseline to calculate exact performance multipliers (e.g., 2.54x, 4.71x). * **🛡️ Virtual DOM Proof:** Custom iterative scrolling logic bypasses YouTube's lazy-loading and virtual memory destruction to guarantee 100% accurate data extraction. * **🤖 AI Strategy Generation:** It analyzes the multiplier and generates a direct, actionable strategy telling you exactly what to do with the data. * **🥇 Auto-Sorting:** The final dataset is automatically sorted from highest multiplier to lowest, so the most viral goldmines are always at the absolute top of your spreadsheet. --- ### 🛠️ How to Use 1. Click the **Input** tab. 2. Paste the URL of the YouTube channel you want to analyze (e.g., `https://www.youtube.com/@AlexHormozi/videos`). 3. Click **Start**. *Note: The scraper will automatically bypass YouTube's cookie-consent popups and extract the data headless.* --- ### 📊 The "Star System" Output The `ai_content_strategy` column uses a visual Star System so you can instantly identify what content to copy and what content to avoid: * ⭐⭐⭐ **VIRAL OUTLIER (3.0x+):** The video performed 300%+ better than the channel's average. *Action: Double down on this exact topic, thumbnail style, and title structure immediately.* * ⭐⭐ **HIGH PERFORMER (1.5x+):** Strong momentum detected. *Action: Create a follow-up or "Part 2" video.* * ⭐ **AVERAGE (0.8x+):** Standard channel performance. Satisfies the core audience but will not rapidly grow the channel. * 🚫 **UNDERPERFORMER (<0.8x):** Below average engagement. *Action: Pivot away from this specific format.* --- ### 📄 Example Output Data Your downloaded CSV/Excel file will be perfectly formatted and ready to hand off to your production team: ```json { "video_title": "If I Wanted to Make My First $100K in 2026, I'd Do This", "url": "https://www.youtube.com/watch?v=jfW6gL6hKhk", "video_views": 1400000, "channel_baseline_average": 297167, "outlier_multiplier": "4.71x", "ai_content_strategy": "⭐⭐⭐ VIRAL OUTLIER: This video performed 3x+ above channel baseline. ACTION: Double down on this exact topic, thumbnail style, and title structure immediately.", "publish_date": "3 months ago" } ```` *** ### 💰 Pay Per Event (Zero Wasted Money) This Actor is strictly monetized on a **Pay Per Event** model. You only pay for the exact rows of data successfully extracted and analyzed. If a video fails to scrape, you don't pay for it. # Actor input Schema ## `startUrls` (type: `array`): List of YouTube channel URLs to scrape. ## `maxVideos` (type: `integer`): Number of recent videos to analyze to calculate the channel baseline average ## Actor input object example ```json { "startUrls": [ { "url": "https://www.youtube.com/@AlexHormozi" } ], "maxVideos": 30 } ``` # Actor output Schema ## `video_title` (type: `string`): Title ## `url` (type: `string`): Video Link ## `views` (type: `string`): Views ## `average_views` (type: `string`): Channel Avg ## `outlier_multiplier` (type: `string`): X Times Avg ## `ai_content_strategy` (type: `string`): Advice ## `publish_date` (type: `string`): Time # 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.youtube.com/@AlexHormozi" } ] }; // Run the Actor and wait for it to finish const run = await client.actor("intelscrape/youtube-viral-video-finder-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.youtube.com/@AlexHormozi" }] } # Run the Actor and wait for it to finish run = client.actor("intelscrape/youtube-viral-video-finder-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.youtube.com/@AlexHormozi" } ] }' | apify call intelscrape/youtube-viral-video-finder-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=intelscrape/youtube-viral-video-finder-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Viral Video Finder Scraper & Topic Finder", "description": "Viral Video Finder Scraper \nScrape any YouTube channel to find viral videos. This tool automatically calculates baseline averages, extracts views and URLs, and uses AI to generate actionable content strategies based on outlier multipliers. Perfect for competitor analysis and YouTube topic research.", "version": "1.0", "x-build-id": "LjdcwR8SKrf5Ic6df" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/intelscrape~youtube-viral-video-finder-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-intelscrape-youtube-viral-video-finder-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/intelscrape~youtube-viral-video-finder-scraper/runs": { "post": { "operationId": "runs-sync-intelscrape-youtube-viral-video-finder-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/intelscrape~youtube-viral-video-finder-scraper/run-sync": { "post": { "operationId": "run-sync-intelscrape-youtube-viral-video-finder-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": "YouTube Channel URLs", "type": "array", "description": "List of YouTube channel URLs to scrape.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "maxVideos": { "title": "Max Videos to Analyze", "minimum": 5, "maximum": 500, "type": "integer", "description": "Number of recent videos to analyze to calculate the channel baseline average", "default": 30 } } }, "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 } } } } } } } } } } ```