# Brand & Keyword Mention Aggregator (`gabrielaxy/brand-mention-aggregator`) Actor Find every Hacker News and Reddit mention of your brand, product, or keyword from the last N days. Exact-phrase matching (no false positives). No API key required. - **URL**: https://apify.com/gabrielaxy/brand-mention-aggregator.md - **Developed by:** [Gabriel Antony Xaviour](https://apify.com/gabrielaxy) (community) - **Categories:** Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## Brand & Keyword Mention Aggregator **Find every Hacker News mention of your brand, product, or keyword in the last N days. No API key required. Free.** Exact-phrase matching — no false positives from substring matches. > **⚠️ Reddit is currently HN-only on Apify cloud.** Reddit blocks Apify's datacenter IPs. To include Reddit results, run this actor with residential proxies enabled in the run settings (adds ~$0.0004 per run). HN works without proxies. ### What it does For each keyword you provide: 1. Queries Hacker News (via Algolia's official search API) for **exact-phrase** matches in the last N days 2. Queries Reddit's public search.json for the same exact phrase (requires residential proxies on Apify cloud) 3. Dedupes by permalink 4. Returns a flat dataset of mentions with author, score, snippet, and link ### Use cases - **Founders** monitoring brand mentions (you launched on HN — who's talking about you?) - **PR / marketing teams** tracking competitor or category mentions - **Devs** monitoring their open-source project's name across communities - **Hiring** spotting people mentioning your stack ("we just shipped X in next.js") - **Customer research** surfacing real user feedback for a product ### Input ```json { "keywords": ["next.js", "supabase"], "days": 7, "maxPerKeyword": 30 } ```` | Field | Type | Description | |---|---|---| | `keywords` | string\[] | One or more exact phrases to track | | `days` | integer | Look back this many days (default 7, max 90) | | `maxPerKeyword` | integer | Cap per keyword per source (default 30, max 100) | ### Output (example, query="next.js") ```json { "source": "hackernews", "keyword": "next.js", "title": "Arrow Flight vs. JSON in Next.js: Benchmarking Python and Go", "url": "https://news.ycombinator.com/item?id=48077301", "permalink": "https://news.ycombinator.com/item?id=48077301", "snippet": "...", "author": "keynha", "ts": 1778414403393, "score": 12, "numComments": 4 } ``` ### Performance - **2 keywords × 7 days**: 5.5s, $0.0012, ~26 unique mentions ### Pricing **Free.** You pay only Apify's compute (~$0.001 per run). ### FAQ **Why exact-phrase matching?** Without it, searching "cosmiclan" matches user "cosmicgadget" (substring). We wrap your keyword in quotes before passing it to the underlying search APIs. Zero false positives. **Why does Reddit return 0 results on Apify cloud?** Reddit's anti-bot detection blocks Apify's datacenter IPs. Two options: 1. Enable residential proxies in the run settings (adds ~$0.0004 per run) 2. Run this actor locally where Reddit doesn't block you (the source is on GitHub) **Can I add Twitter / X / LinkedIn?** Those platforms aggressively block scraping and require paid API access. Open a feature request and we'll plug in the appropriate paid actor. **How often should I run this?** Daily for active monitoring. Apify's free tier covers ~3,000 daily runs/month. ### Author Built by [gabrielaxy](https://apify.com/gabrielaxy). # Actor input Schema ## `keywords` (type: `array`): Brand names, product names, or phrases to track. Each will be matched as an EXACT phrase across HN and Reddit. ## `days` (type: `integer`): Look back this many days from now. ## `maxPerKeyword` (type: `integer`): Cap items per keyword per source (HN or Reddit). Total max ≈ keywords × 2 × maxPerKeyword. ## Actor input object example ```json { "keywords": [ "next.js" ], "days": 7, "maxPerKeyword": 30 } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "keywords": [ "next.js" ] }; // Run the Actor and wait for it to finish const run = await client.actor("gabrielaxy/brand-mention-aggregator").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = { "keywords": ["next.js"] } # Run the Actor and wait for it to finish run = client.actor("gabrielaxy/brand-mention-aggregator").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 '{ "keywords": [ "next.js" ] }' | apify call gabrielaxy/brand-mention-aggregator --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=gabrielaxy/brand-mention-aggregator", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Brand & Keyword Mention Aggregator", "description": "Find every Hacker News and Reddit mention of your brand, product, or keyword from the last N days. Exact-phrase matching (no false positives). No API key required.", "version": "0.1", "x-build-id": "Ukwv1cqLwKH7dk9N9" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/gabrielaxy~brand-mention-aggregator/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-gabrielaxy-brand-mention-aggregator", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/gabrielaxy~brand-mention-aggregator/runs": { "post": { "operationId": "runs-sync-gabrielaxy-brand-mention-aggregator", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/gabrielaxy~brand-mention-aggregator/run-sync": { "post": { "operationId": "run-sync-gabrielaxy-brand-mention-aggregator", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "keywords" ], "properties": { "keywords": { "title": "Keywords", "minItems": 1, "uniqueItems": true, "type": "array", "description": "Brand names, product names, or phrases to track. Each will be matched as an EXACT phrase across HN and Reddit.", "items": { "type": "string" } }, "days": { "title": "Days to look back", "minimum": 1, "maximum": 90, "type": "integer", "description": "Look back this many days from now.", "default": 7 }, "maxPerKeyword": { "title": "Max results per keyword per source", "minimum": 1, "maximum": 100, "type": "integer", "description": "Cap items per keyword per source (HN or Reddit). Total max ≈ keywords × 2 × maxPerKeyword.", "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 } } } } } } } } } } ```