# X (Twitter) Influencer Analytics - Engagement Rate Report (`seemuapps/x-influencer-analytics-scraper`) Actor Get a full engagement report for any X (Twitter) account - engagement rate, average likes and views, posting frequency, top tweets and hashtags. - **URL**: https://apify.com/seemuapps/x-influencer-analytics-scraper.md - **Developed by:** [Andrew](https://apify.com/seemuapps) (community) - **Categories:** Social media, Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $100.00 / 1,000 influencer analytics reports 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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## X (Twitter) Influencer Analytics - Engagement Rate Report Get a complete engagement report for any X (Twitter) account — real engagement rate, average likes and views, posting frequency, top tweets, and top hashtags — computed from their actual recent tweets. No login, no cookies. ### What you get One report per account: - **Profile snapshot** — followers, following, total tweets, bio, location, blue-verified status, account age - **Engagement rate** — average engagement per tweet as a percentage of followers (the number brands actually ask for) - **Averages over recent original tweets** — likes, retweets, replies, quotes, bookmarks, and views, plus median likes to spot outlier-skewed accounts - **Reach metrics** — average views and views-to-followers percentage - **Posting behavior** — tweets per week, share of replies, retweets, and quote tweets - **Top content** — the account's 3 highest-engagement recent tweets (with URLs) and top 5 hashtags - Export to JSON, CSV, Excel, or Google Sheets directly from the Apify console ### Use cases - **Influencer vetting** — verify an influencer's real engagement rate before paying for a campaign - **Creator pricing** — benchmark accounts against each other with identical, comparable metrics - **Competitor analysis** — track rivals' posting frequency, engagement, and best-performing content - **Lead scoring** — prioritize outreach to accounts with an active, engaged audience - **Self-audit** — measure your own account's performance over time with scheduled runs ### How to use 1. Enter one or more **Usernames** (with or without @) or profile URLs 2. Set **Tweets to Analyze** per account (default 100 recent tweets) 3. Run the actor — one report per account appears in the **Dataset** tab ### Output format Each dataset record: ```json { "username": "mkbhd", "fullName": "Marques Brownlee", "isBlueVerified": true, "followersCount": 6800000, "followingCount": 512, "totalTweetsCount": 72000, "accountAgeDays": 5840, "tweetsAnalyzed": 100, "originalTweetsAnalyzed": 74, "analysisWindowDays": 21.5, "avgLikes": 4820.5, "avgRetweets": 310.2, "avgReplies": 240.8, "avgViews": 512000, "medianLikes": 2100, "avgEngagementPerTweet": 5560.1, "engagementRatePct": 0.08, "viewsToFollowersPct": 7.53, "tweetsPerWeek": 32.6, "replyPct": 41.9, "retweetPct": 26.0, "topHashtags": ["#tech", "#review"], "topTweets": [ { "tweetId": "1866332309399781537", "url": "https://x.com/mkbhd/status/1866332309399781537", "text": "The new phone is wild…", "likeCount": 45200, "viewCount": 3400000, "totalEngagement": 51230 } ], "checkedAt": "2026-07-25T04:12:00.000Z" } ```` ### FAQ **Do I need X login credentials?** No. Enter usernames and run. **How is the engagement rate calculated?** Average engagement (likes + retweets + replies + quotes + bookmarks) per original tweet, divided by follower count. Retweets the account made are excluded so its own content is what's measured. **Why both average and median likes?** One viral tweet can inflate an average. A median far below the average signals inconsistent performance. # Actor input Schema ## `usernames` (type: `array`): One or more X (Twitter) usernames (with or without @) or profile URLs. One analytics report is produced per account. ## `tweetsToAnalyze` (type: `integer`): How many recent tweets to analyze per account (20-1000). More tweets = more accurate averages but a longer, costlier run. ## Actor input object example ```json { "usernames": [ "mkbhd" ], "tweetsToAnalyze": 100 } ``` # Actor output Schema ## `results` (type: `string`): One row per account: profile info, engagementRatePct, avg likes/retweets/replies/views, medianLikes, tweetsPerWeek, reply/retweet percentages, topHashtags, and top 3 tweets. # 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 = { "usernames": [ "mkbhd" ] }; // Run the Actor and wait for it to finish const run = await client.actor("seemuapps/x-influencer-analytics-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 = { "usernames": ["mkbhd"] } # Run the Actor and wait for it to finish run = client.actor("seemuapps/x-influencer-analytics-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 '{ "usernames": [ "mkbhd" ] }' | apify call seemuapps/x-influencer-analytics-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=seemuapps/x-influencer-analytics-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "X (Twitter) Influencer Analytics - Engagement Rate Report", "description": "Get a full engagement report for any X (Twitter) account - engagement rate, average likes and views, posting frequency, top tweets and hashtags.", "version": "1.0", "x-build-id": "BWLxgYEVwzmXnf78C" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/seemuapps~x-influencer-analytics-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-seemuapps-x-influencer-analytics-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/seemuapps~x-influencer-analytics-scraper/runs": { "post": { "operationId": "runs-sync-seemuapps-x-influencer-analytics-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/seemuapps~x-influencer-analytics-scraper/run-sync": { "post": { "operationId": "run-sync-seemuapps-x-influencer-analytics-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "usernames" ], "properties": { "usernames": { "title": "Usernames", "type": "array", "description": "One or more X (Twitter) usernames (with or without @) or profile URLs. One analytics report is produced per account.", "items": { "type": "string" } }, "tweetsToAnalyze": { "title": "Tweets to Analyze", "minimum": 20, "maximum": 1000, "type": "integer", "description": "How many recent tweets to analyze per account (20-1000). More tweets = more accurate averages but a longer, costlier run.", "default": 100 } } }, "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 } } } } } } } } } } ```