# X (Twitter) Audience Overlap Analyzer - Shared Followers (`seemuapps/x-audience-overlap-analyzer`) Actor Compare the followers of 2-5 X (Twitter) accounts - shared follower counts, overlap percentages, and enriched profiles of the common audience. - **URL**: https://apify.com/seemuapps/x-audience-overlap-analyzer.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 $500.00 / 1,000 audience overlap 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) Audience Overlap Analyzer - Shared Followers Compare the follower bases of 2–5 X (Twitter) accounts and find out exactly how much audience they share — shared follower counts, overlap percentages, and full profiles of the people who follow both. No login, no cookies. ### What you get **One `overlapSummary` row per account pair:** - **Shared follower count** — how many people follow both accounts - **Overlap percentages** — what share of account A's audience also follows account B (and vice versa), plus the Jaccard similarity of the two audiences - True follower totals for each account and how many followers were analyzed - A partial-analysis flag when large accounts were capped **One `sharedFollower` row per enriched shared follower (optional):** - Username, display name, blue-verified status, bio, and location - Follower/following counts, total tweets, account creation date - Profile picture and direct profile URL Export everything to JSON, CSV, Excel, or Google Sheets directly from the Apify console. ### Use cases - **Influencer vetting** — check how much an influencer's audience overlaps yours before paying for a collab; low overlap = more new reach - **Competitor analysis** — measure how much audience you share with rivals and who those shared followers are - **Partnership scouting** — find accounts whose audiences complement (not duplicate) yours - **Ad targeting research** — identify the engaged accounts that follow multiple players in your niche - **M&A / creator due diligence** — verify claimed audience uniqueness with real data ### How to use 1. Enter **2 to 5 usernames** (with or without @) or profile URLs 2. Optionally adjust **Max Followers Analyzed per Account** (default 25,000 most recent) and **Shared Profiles to Enrich** (default 50 per pair) 3. Run the actor — every pair of accounts is compared and results appear in the **Dataset** tab ### Output format Overlap summary row: ```json { "recordType": "overlapSummary", "accountA": "nasa", "accountB": "spacex", "accountAFollowerTotal": 79500000, "accountBFollowerTotal": 35800000, "accountAFollowersAnalyzed": 25000, "accountBFollowersAnalyzed": 25000, "sharedFollowerCount": 4180, "overlapPctOfA": 16.72, "overlapPctOfB": 16.72, "jaccardPct": 9.13, "analysisIsPartial": true, "checkedAt": "2026-07-25T04:12:00.000Z" } ```` Shared follower row: ```json { "recordType": "sharedFollower", "accountA": "nasa", "accountB": "spacex", "username": "spacefan", "fullName": "Space Fan", "isBlueVerified": false, "followersCount": 1520, "bio": "Rocket launches and astronomy…", "profileUrl": "https://x.com/spacefan" } ``` ### FAQ **Do I need X login credentials?** No. **How are large accounts handled?** By default the 25,000 most recent followers of each account are analyzed — a strong sample of the *active* audience. Raise the cap (up to 250,000 per account; 0 = maximum) for tighter numbers; the `analysisIsPartial` flag tells you when a cap was hit. **What does `jaccardPct` mean?** Shared followers divided by the combined unique audience of both accounts — a symmetric 0–100 similarity score. # Actor input Schema ## `usernames` (type: `array`): 2 to 5 X (Twitter) usernames (with or without @) or profile URLs. Every pair is compared. ## `maxFollowersPerAccount` (type: `integer`): How many of each account's most recent followers to analyze. Set 0 for the maximum (up to 250,000 most recent followers per account). ## `sampleSharedProfiles` (type: `integer`): For each pair, fetch full profile details for up to this many shared followers. Set 0 to output only the overlap summary. ## Actor input object example ```json { "usernames": [ "nasa", "spacex" ], "maxFollowersPerAccount": 25000, "sampleSharedProfiles": 50 } ``` # Actor output Schema ## `results` (type: `string`): Rows with recordType 'overlapSummary' (sharedFollowerCount, overlapPctOfA/B, jaccardPct, followers analyzed) and recordType 'sharedFollower' (username, bio, follower counts, profile URL). # 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": [ "nasa", "spacex" ] }; // Run the Actor and wait for it to finish const run = await client.actor("seemuapps/x-audience-overlap-analyzer").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": [ "nasa", "spacex", ] } # Run the Actor and wait for it to finish run = client.actor("seemuapps/x-audience-overlap-analyzer").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": [ "nasa", "spacex" ] }' | apify call seemuapps/x-audience-overlap-analyzer --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=seemuapps/x-audience-overlap-analyzer", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "X (Twitter) Audience Overlap Analyzer - Shared Followers", "description": "Compare the followers of 2-5 X (Twitter) accounts - shared follower counts, overlap percentages, and enriched profiles of the common audience.", "version": "1.0", "x-build-id": "rJvAYCZlIGRt7T0ZO" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/seemuapps~x-audience-overlap-analyzer/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-seemuapps-x-audience-overlap-analyzer", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/seemuapps~x-audience-overlap-analyzer/runs": { "post": { "operationId": "runs-sync-seemuapps-x-audience-overlap-analyzer", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/seemuapps~x-audience-overlap-analyzer/run-sync": { "post": { "operationId": "run-sync-seemuapps-x-audience-overlap-analyzer", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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 to Compare", "type": "array", "description": "2 to 5 X (Twitter) usernames (with or without @) or profile URLs. Every pair is compared.", "items": { "type": "string" } }, "maxFollowersPerAccount": { "title": "Max Followers Analyzed per Account", "minimum": 0, "type": "integer", "description": "How many of each account's most recent followers to analyze. Set 0 for the maximum (up to 250,000 most recent followers per account).", "default": 25000 }, "sampleSharedProfiles": { "title": "Shared Profiles to Enrich", "minimum": 0, "type": "integer", "description": "For each pair, fetch full profile details for up to this many shared followers. Set 0 to output only the overlap summary.", "default": 50 } } }, "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 } } } } } } } } } } ```