# XavvyNess AI Social Scout (`xavvyness/xavvyness-social-monitor`) Actor Monitor any social profile and get AI intelligence: sentiment, trending topics, audience insights, and strategic recommendations. Works across Instagram, TikTok, Twitter/X, LinkedIn. Live web search — no credentials needed. - **URL**: https://apify.com/xavvyness/xavvyness-social-monitor.md - **Developed by:** [XavvyNess](https://apify.com/xavvyness) (community) - **Categories:** AI, Social media, Marketing - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $50.00 / 1,000 social intelligence 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 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 ## 📊 XavvyNess Social Monitor — Social Media Intelligence + Sentiment Monitor any social media profile or keyword across Instagram, TikTok, Twitter/X, and LinkedIn. Get AI-powered sentiment scores, trending topics, engagement analysis, competitor insights, and strategic recommendations — not just raw comment dumps. > **The #1 complaint about Social Media Sentiment actors is "it only does comments, not strategy."** We built the strategic layer on top. ### Demo > 🎬 **Video demo coming soon.** Upload `social-monitor.mp4` to YouTube, then run `python3 scripts/actor-video-gen.py --embed-readmes` to embed it here automatically. --- ### 🚀 What It Does 1. Finds recent posts and engagement data for any social profile or keyword 2. Analyzes sentiment, trending topics, and audience patterns with AI 3. Generates strategic recommendations and growth opportunities **Perfect for:** brand monitoring, influencer research, competitor tracking, content strategy, crisis detection --- ### 📥 Input | Field | Required | Default | Description | |---|---|---|---| | `profiles` | ✅ | — | Profile URLs or @handles (e.g. `@openai` or `https://instagram.com/openai`) | | `keywords` | — | `[]` | Additional keywords to focus analysis on | | `platform` | — | `auto` | `auto` / `instagram` / `tiktok` / `twitter` / `linkedin` | | `postLimit` | — | `20` | How many recent posts to analyze (5-100) | | `depth` | — | `standard` | `quick` / `standard` / `deep` | **Example input:** ```json { "profiles": ["@anthropic", "https://twitter.com/openai"], "keywords": ["Claude", "GPT", "pricing"], "platform": "auto", "postLimit": 30, "depth": "deep" } ```` *** ### 📤 Output Real output from a live run on @openai: ```json { "profile": "openai", "handle": "openai", "platform": "general", "overallSentiment": "Positive", "sentimentScore": "0.7", "followerCount": "3.2 million", "engagementRate": "2-3%", "postingFrequency": "Daily", "topTopics": ["API Usage", "GPT-5.2", "Codex", "ChatGPT", "Model Optimization"], "contentThemes": ["AI Development", "Product Releases", "Customer Support", "Enterprise Solutions"], "audienceType": "Developers and Tech Enthusiasts", "summary": "OpenAI has a strong social presence with a large and engaged audience. The company focuses on advancing AI technology, releasing new products and features, and providing support to its users. The audience is primarily composed of developers and tech enthusiasts interested in AI development, product releases, and customer support.", "recommendations": [ "Increase engagement by responding to user feedback and concerns", "Expand content themes to include more tutorials and guides for new users", "Utilize social media platforms to promote new product releases and features" ], "analyzedAt": "2026-04-08T22:26:44.759Z", "agent": "XavvyNess Social Monitor" } ``` *** ### 💡 Analysis Depths | Depth | Output | Best For | |---|---|---| | `quick` | Sentiment + top 3 topics | Fast pulse check | | `standard` | + engagement rate + recommendations | Weekly monitoring | | `deep` | + competitor comparison + crisis signals + 30-day trend | Strategy planning | *** ### ⚙️ Setup — API Keys | Variable | Required | Where to Get | |---|---|---| | `TAVILY_API_KEY` | ✅ Required | [app.tavily.com](https://app.tavily.com) (free tier: 1,000 searches/month) | | `GROQ_API_KEY` | Recommended (free) | [console.groq.com](https://console.groq.com) | | `GOOGLE_API_KEY` | Optional fallback | [aistudio.google.com](https://aistudio.google.com) | *** ### ❓ FAQ **Q: Does it scrape social platforms directly?**\ A: No — we use Tavily's live web search to find and analyze public social content. This means no account bans, no platform blocks. **Q: What platforms are supported?**\ A: Instagram, TikTok, Twitter/X, LinkedIn, and Facebook. Use `platform: "auto"` to auto-detect from the URL. **Q: How current is the data?**\ A: Tavily returns results from the last 24-48 hours. For real-time streaming data, schedule runs every few hours. *** ### 🔗 Use Cases 1. **Brand monitoring** — Track sentiment around your brand name 24/7 2. **Influencer research** — Evaluate engagement quality before a partnership 3. **Competitor tracking** — Monitor what's working for competitors and why 4. **Content strategy** — Discover which topics and formats drive the most engagement 5. **Crisis detection** — Catch negative sentiment spikes before they escalate 6. **Campaign measurement** — Before/after sentiment analysis for marketing campaigns *** ### 📊 Performance - ✅ Analysis per profile: ~15-30s - ✅ Works across all major platforms without account credentials - ✅ Retry logic with categorized error messages - ✅ Groq → Gemini fallback - ✅ **Failed runs are not charged** — you only pay for successfully analyzed profiles *** ### Integration #### Via Apify JavaScript client ```javascript import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' }); const run = await client.actor('W0vNaaiXZdeicI7gF').call({ profiles: ['https://www.instagram.com/openai/', '@anthropic'], keywords: ['Claude', 'GPT'], platform: 'auto', depth: 'standard', }); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach(profile => { console.log(profile.handle); // @handle console.log(profile.overallSentiment); // positive | neutral | negative console.log(profile.topTopics); // trending topics array console.log(profile.recommendations); // strategic action items }); ``` #### Via HTTP API ```bash curl -X POST \ "https://api.apify.com/v2/acts/W0vNaaiXZdeicI7gF/runs?token=YOUR_TOKEN" \ -H "Content-Type: application/json" \ -d '{ "profiles": ["https://www.instagram.com/openai/"], "depth": "standard" }' ``` #### Via Make.com / Zapier Use the **Apify** module → **Run Actor** action. Actor ID: `W0vNaaiXZdeicI7gF`. Map `{{overallSentiment}}`, `{{topTopics}}`, and `{{recommendations}}` from the output into your automation. *** *Built by XavvyNess — AI agent services that do real work.* # Actor input Schema ## `profiles` (type: `array`): Social media profile URLs or @handles to monitor. ## `keywords` (type: `array`): Optional: specific keywords or topics to focus the analysis on. ## `platform` (type: `string`): Target social platform. Use 'auto' to detect from the URL. ## `postLimit` (type: `integer`): How many recent posts to analyze. ## `depth` (type: `string`): How deeply to analyze each social profile. ## Actor input object example ```json { "profiles": [ "https://www.instagram.com/openai/" ], "keywords": [], "platform": "auto", "postLimit": 20, "depth": "standard" } ``` # 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 = { "profiles": [ "https://www.instagram.com/openai/" ] }; // Run the Actor and wait for it to finish const run = await client.actor("xavvyness/xavvyness-social-monitor").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 = { "profiles": ["https://www.instagram.com/openai/"] } # Run the Actor and wait for it to finish run = client.actor("xavvyness/xavvyness-social-monitor").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 '{ "profiles": [ "https://www.instagram.com/openai/" ] }' | apify call xavvyness/xavvyness-social-monitor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=xavvyness/xavvyness-social-monitor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "XavvyNess AI Social Scout", "description": "Monitor any social profile and get AI intelligence: sentiment, trending topics, audience insights, and strategic recommendations. Works across Instagram, TikTok, Twitter/X, LinkedIn. Live web search — no credentials needed.", "version": "1.0", "x-build-id": "ZVe2i37iVoaWcBg3c" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/xavvyness~xavvyness-social-monitor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-xavvyness-xavvyness-social-monitor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/xavvyness~xavvyness-social-monitor/runs": { "post": { "operationId": "runs-sync-xavvyness-xavvyness-social-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/xavvyness~xavvyness-social-monitor/run-sync": { "post": { "operationId": "run-sync-xavvyness-xavvyness-social-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "profiles" ], "properties": { "profiles": { "title": "Profile URLs or Handles", "type": "array", "description": "Social media profile URLs or @handles to monitor.", "default": [ "https://www.instagram.com/openai/" ], "items": { "type": "string" } }, "keywords": { "title": "Keywords to Track", "type": "array", "description": "Optional: specific keywords or topics to focus the analysis on.", "default": [], "items": { "type": "string" } }, "platform": { "title": "Platform", "enum": [ "auto", "instagram", "tiktok", "twitter", "linkedin" ], "type": "string", "description": "Target social platform. Use 'auto' to detect from the URL.", "default": "auto" }, "postLimit": { "title": "Number of Recent Posts", "minimum": 5, "maximum": 100, "type": "integer", "description": "How many recent posts to analyze.", "default": 20 }, "depth": { "title": "Analysis Depth", "enum": [ "quick", "standard", "deep" ], "type": "string", "description": "How deeply to analyze each social profile.", "default": "standard" } } }, "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 } } } } } } } } } } ```