# News Trending Detector (`0xgollum/news-trending-detector`) Actor Detect topics accelerating in real time across Hacker News, Google News, Bloomberg, CNBC, BBC and The Verge — velocity, not just volume. - **URL**: https://apify.com/0xgollum/news-trending-detector.md - **Developed by:** [0xGollum](https://apify.com/0xgollum) (community) - **Categories:** News, Social media - **Stats:** 2 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $2.00 / 1,000 results 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 ## News Trending Detector 📈 **Find out what's *accelerating* in the news — not just what's being published.** Most "news scrapers" hand you a firehose of articles and leave you to figure out what matters. This actor is different: it tracks the **velocity** of every topic across multiple sources and surfaces the ones that are *spiking right now* versus their own recent baseline. --- ### Why velocity, not volume? A topic with 50 mentions isn't news if it always has 50 mentions. A topic that jumped from 4 → 20 in the last hour is. This actor keeps a rolling baseline for every topic (persisted between runs) and flags: | Status | Meaning | |--------|---------| | `emerging` | Brand-new topic, never seen in previous runs | | `accelerating` | Mention volume is significantly above its own baseline | | `baseline` | First run only — establishing the reference (no velocity yet) | | `steady` / `cooling` | Flat or fading — only returned as a fallback (see below) | Accelerating and emerging topics come first, so you get **signal, not noise**. When nothing is accelerating in a given run, the actor returns the current top topics (labelled `steady`/`cooling`) instead of an empty result — so a scheduled run always gives you a readable snapshot of the landscape. > ⏱️ **Important:** velocity is measured *across runs*. The **first run just builds the baseline**. Schedule the actor to run on a recurring basis (e.g. every hour) — from the second run onward you get real `accelerating` / `emerging` signals. Apify Schedules make this one click. --- ### Two modes **1. Watchlist mode** — pass `keywords_filter` (e.g. `openai, nvidia, bitcoin`). Each topic is queried directly against Google News for a strong, focused volume signal, then tracked for acceleration. Perfect for *"tell me when MY topics surge."* **2. Discovery mode** — leave `keywords_filter` empty. The actor aggregates all sources and surfaces whatever is trending globally. --- ### Sources (all public, no API keys) - **Hacker News** — tech / startup signal - **Google News** — broad + per-keyword search - **Bloomberg** — markets & business - **CNBC** — business & tech - **BBC** — general / world - **The Verge** — tech / consumer - ➕ your own **custom RSS feeds** --- ### Use cases - **Traders / hedge funds** — catch market-moving stories early - **Brand & PR monitoring** — know the moment your brand or a competitor spikes - **Growth / VC scouts** — spot emerging themes before they're obvious - **Newsletter & content creators** — auto-sourced "what's hot" sections - **Researchers** — study how stories propagate and accelerate --- ### Input | Field | Type | Default | Description | |-------|------|---------|-------------| | `keywords_filter` | string | — | Comma-separated watchlist. Empty = global discovery. | | `include_hackernews` | boolean | `true` | Include Hacker News. | | `include_rss_feeds` | boolean | `true` | Include Google News, Bloomberg, CNBC, BBC, The Verge. | | `custom_rss_feeds` | array | `[]` | Your own RSS feed URLs. | | `min_frequency` | integer | `3` | Minimum mentions to qualify as a topic. | | `max_results` | integer | `100` | Cap on trends returned per run. | #### Example ```json { "keywords_filter": "openai, nvidia, bitcoin", "include_hackernews": true, "include_rss_feeds": true, "min_frequency": 3 } ```` *** ### Output Each dataset item is one trending topic: ```json { "topic": "OpenAI", "status": "accelerating", "current_mentions": 18, "baseline_mentions": 6.2, "velocity_score": 12.4, "acceleration": 11.8, "sources": ["gnews", "hackernews", "theverge"], "sample_articles": [ { "title": "OpenAI raises mega round", "source": "gnews", "url": "https://…" } ] } ``` - **velocity\_score** — single ranking number (higher = accelerating harder / fresher) - **acceleration** — current mentions minus baseline - **sample\_articles** — up to 5 example headlines behind the trend *** ### Pricing **Pay per result:** **$0.002 per topic** returned. A typical run returns a handful to a few dozen items, so most runs cost a few cents. No subscription, no rental — you pay only per item in your dataset. *** ### Tips - **Run on a schedule** (hourly is a good start) — that's what powers the velocity signal. - Tighten `min_frequency` if you want only strong trends; loosen it to catch weak/early ones. - In watchlist mode, fewer, well-chosen keywords give cleaner signal than a huge list. *** *Built for the international market. All data comes from public, freely available sources.* # Actor input Schema ## `keywords_filter` (type: `string`): Comma-separated topics to track (e.g. 'openai, nvidia, bitcoin'). Each is queried against Google News for a strong per-topic volume signal. Leave empty for open discovery of whatever is trending across all sources. ## `include_hackernews` (type: `boolean`): Include Hacker News top stories (tech/startup signal). ## `include_rss_feeds` (type: `boolean`): Include Google News, Bloomberg, CNBC, BBC and The Verge. ## `custom_rss_feeds` (type: `array`): Add your own RSS feed URLs to fold into the trend analysis. ## `min_frequency` (type: `integer`): A topic must appear in at least this many articles to qualify. ## `max_results` (type: `integer`): Cap on the number of trending topics returned per run. ## Actor input object example ```json { "keywords_filter": "openai, nvidia, bitcoin", "include_hackernews": true, "include_rss_feeds": true, "custom_rss_feeds": [], "min_frequency": 3, "max_results": 100 } ``` # 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_filter": "openai, nvidia, bitcoin" }; // Run the Actor and wait for it to finish const run = await client.actor("0xgollum/news-trending-detector").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_filter": "openai, nvidia, bitcoin" } # Run the Actor and wait for it to finish run = client.actor("0xgollum/news-trending-detector").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_filter": "openai, nvidia, bitcoin" }' | apify call 0xgollum/news-trending-detector --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=0xgollum/news-trending-detector", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "News Trending Detector", "description": "Detect topics accelerating in real time across Hacker News, Google News, Bloomberg, CNBC, BBC and The Verge — velocity, not just volume.", "version": "0.1", "x-build-id": "ZdSMOCAnkCZlBkPO3" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/0xgollum~news-trending-detector/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-0xgollum-news-trending-detector", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/0xgollum~news-trending-detector/runs": { "post": { "operationId": "runs-sync-0xgollum-news-trending-detector", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/0xgollum~news-trending-detector/run-sync": { "post": { "operationId": "run-sync-0xgollum-news-trending-detector", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "keywords_filter": { "title": "Keywords / watchlist", "type": "string", "description": "Comma-separated topics to track (e.g. 'openai, nvidia, bitcoin'). Each is queried against Google News for a strong per-topic volume signal. Leave empty for open discovery of whatever is trending across all sources." }, "include_hackernews": { "title": "Include Hacker News", "type": "boolean", "description": "Include Hacker News top stories (tech/startup signal).", "default": true }, "include_rss_feeds": { "title": "Include news RSS feeds", "type": "boolean", "description": "Include Google News, Bloomberg, CNBC, BBC and The Verge.", "default": true }, "custom_rss_feeds": { "title": "Custom RSS feeds (optional)", "type": "array", "description": "Add your own RSS feed URLs to fold into the trend analysis.", "default": [], "items": { "type": "string" } }, "min_frequency": { "title": "Minimum mentions", "minimum": 1, "maximum": 100, "type": "integer", "description": "A topic must appear in at least this many articles to qualify.", "default": 3 }, "max_results": { "title": "Max trends returned", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Cap on the number of trending topics returned per 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 } } } } } } } } } } ```