# Wikipedia Trending Topic Pageview Tracker (`saint_person/wikipedia-trending-pageviews-tracker`) Actor Compare Wikipedia articles by pageview momentum - which topics are gaining reader attention this week versus last, ranked side by side. - **URL**: https://apify.com/saint\_person/wikipedia-trending-pageviews-tracker.md - **Developed by:** [saint person](https://apify.com/saint_person) (community) - **Categories:** Business - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event 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 ## ๐Ÿ“Š Wikipedia Trending Topic Pageview Tracker > ๐Ÿš€ **See which topics are gaining reader attention, not just which are popular.** Compare pageview momentum across articles in one run. > ๐Ÿ•’ **Last updated:** 2026-07-07 ยท ๐Ÿ“Š **8 fields** per article ยท Built on Wikimedia's official Pageviews API (no key) > ๐ŸŽ **Try it free:** the free Apify plan returns up to **10 results per run** - enough to see the output quality. Any paid Apify plan unlocks full-size runs. Give it a list of Wikipedia articles and it pulls daily pageview history for each, then compares this week's readership to last week's - so you know Taylor_Swift's attention is up 5.7x this week while Artificial_intelligence has cooled slightly, instead of just knowing both get a lot of traffic. | ๐ŸŽฏ Target Audience | ๐Ÿ’ก Primary Use Cases | | --- | --- | | Journalists, content strategists, PR/comms teams, researchers, trend-watchers | Breaking-interest detection, content-calendar planning, PR spike monitoring, trend research | --- ### ๐Ÿ“‹ What this Actor does - Fetches daily pageview history per article from Wikimedia's official REST API - Splits the window into last-7-days vs prior-7-days - Computes a momentum ratio and labels it SURGING / RISING / STEADY / DECLINING - Reports peak single-day views and average daily views for context > ๐Ÿ’ก **Why it matters:** total traffic tells you a topic is known. Momentum tells you it's having a moment right now. --- ### ๐ŸŽฌ Full demo ๐Ÿšง Video walkthrough coming soon - the Quick Start below takes under 3 minutes end to end. --- ### โš™๏ธ Input | Input | Type | Default | Behavior | | --- | --- | --- | --- | | `articles` | string | (prefilled) | Comma-separated English Wikipedia article titles (underscores for spaces), up to 10 | | `daysBack` | integer | 14 | Days of history to analyze (14-60) | **Example: Compare 5 trending topics** ```json { "articles": "Artificial_intelligence, Bitcoin, World_War_II, Olympic_Games, Taylor_Swift", "daysBack": 14 } ```` **Example: Two-way comparison over a month** ```json { "articles": "ChatGPT, Generative_artificial_intelligence", "daysBack": 30 } ``` > โš ๏ธ **Good to know:** use exact Wikipedia URL-style titles with underscores (e.g. "World\_War\_II" not "World War 2"). Very recently created or renamed articles may not have pageview history yet. *** ### ๐Ÿ“Š Output Each article returns **8 fields**. Download as CSV, Excel, JSON, or XML, or consume via the Apify API. #### ๐Ÿงพ Schema | Field | Type | Example | | --- | --- | --- | | article | string | Taylor\_Swift | | totalViews | integer | 1156272 | | avgDailyViews | number | 82590.9 | | last7DaysViews / prior7DaysViews | integer | 984174 / 172098 | | momentumRatio | number | 5.72 | | trendLevel | string | SURGING | | peakDayViews | integer | 406328 | | generatedAt | string | 2026-07-07T10:34:25Z | #### ๐Ÿ“ฆ Sample record ```json {"article":"Taylor_Swift","totalViews":1156272,"avgDailyViews":82590.9,"last7DaysViews":984174,"prior7DaysViews":172098,"momentumRatio":5.72,"trendLevel":"SURGING","peakDayViews":406328,"generatedAt":"2026-07-07T10:34:25Z"} ``` *** ### โœจ Why choose this Actor | | Capability | | --- | --- | | ๐Ÿ† | **Momentum, not just traffic.** Ranks by week-over-week acceleration, not raw view count. | | ๐Ÿ“š | **Multi-article in one run.** Compare up to 10 topics side by side. | | โšก | **Built on Wikimedia's official API.** Free, no key - stable and durable. | | ๐Ÿ” | **Automatic retries.** Transient failures retry with backoff. | | ๐Ÿ’ณ | **Pay per result.** No subscription. A typical 5-article run costs about $0.035. | > ๐Ÿ“Š Turns 5 manual pageview lookups into 1 ranked momentum table. *** ### ๐Ÿ“ˆ How it compares to alternatives | Approach | Cost | Coverage | Refresh | Setup | | --- | --- | --- | --- | --- | | **โญ Wikipedia Trending Topic Pageview Tracker** *(this Actor)* | $5 free credit, then pay-per-use | Any article, up to 10 per run | **Live per run** | โšก 2 min | | Wikipedia's own pageviews tool | Free | One article at a time | Live | ๐Ÿข Manual, one by one | | Google Trends | Free | Search interest, not pageviews | Live | ๐Ÿข Different metric entirely | | Manual copy-paste | Free | Current session only | One-shot | ๐Ÿข Hours of clicking | Pick this Actor when you want a ranked, multi-article momentum view without writing scoring logic yourself. *** ### ๐Ÿš€ How to use 1. ๐Ÿ“ **Sign up.** Create a free Apify account with $5 credit (takes 2 minutes). 2. ๐ŸŒ **Open the Actor.** Go to the Wikipedia Trending Topic Pageview Tracker page on the Apify Store. 3. โœ๏ธ **Set your input.** List your articles - or just press Start, the prefilled example works. 4. ๐Ÿš€ **Run it.** Watch results stream into the Dataset tab. 5. ๐Ÿ“ฅ **Download.** Grab results as CSV, Excel, JSON, or XML - or pipe them anywhere via the Apify API. > โฑ๏ธ Total time from signup to downloaded dataset: **3-5 minutes.** No coding required. *** ### ๐Ÿ’ผ Business use cases - **Journalists/newsletter writers:** find which topic is genuinely spiking in reader interest this week. - **PR/comms teams:** quantify a spike in public attention after a campaign or news event. - **Content strategists:** time a piece of content to a topic's real momentum, not a guess. - **Researchers:** build reproducible public-attention datasets for media studies. *** ### ๐ŸŒŸ Beyond business use cases - ๐ŸŽ“ **Research & academia** - media attention studies, information-diffusion research. - ๐ŸŽจ **Personal & creative** - track how a topic you write about is trending. - ๐Ÿค **Non-profit & civic** - monitor public awareness of a cause or issue over time. - ๐Ÿงช **Experimentation** - prototype a public-interest dashboard on real data. *** ### ๐Ÿ”Œ Automating Wikipedia Trending Topic Pageview Tracker **Node.js** (`apify-client` on NPM): ```javascript const { ApifyClient } = require('apify-client'); const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' }); const run = await client.actor('saint_person/wikipedia-trending-pageviews-tracker').call({ articles: "Artificial_intelligence, Bitcoin", daysBack: 14 }); const { items } = await client.dataset(run.defaultDatasetId).listItems(); ``` **Python** (`apify-client` on PyPI): ```python from apify_client import ApifyClient client = ApifyClient('YOUR_APIFY_TOKEN') run = client.actor('saint_person/wikipedia-trending-pageviews-tracker').call(run_input={"articles": "Artificial_intelligence, Bitcoin", "daysBack": 14}) items = client.dataset(run['defaultDatasetId']).list_items().items ``` ๐Ÿ“š [Full Apify API documentation](https://docs.apify.com/api/v2) [Apify Schedules](https://docs.apify.com/platform/schedules) triggers this Actor on any cron interval - weekly for a standing trend watch. *** ### ๐Ÿง  Use via MCP (for AI agents) Wikipedia Trending Topic Pageview Tracker is directly callable by any MCP-compatible AI agent (Claude, ChatGPT, Cursor, and others) through Apify's hosted MCP server - no custom integration code needed. ```json { "mcpServers": { "apify": { "url": "https://mcp.apify.com/sse?actors=saint_person/wikipedia-trending-pageviews-tracker", "headers": { "Authorization": "Bearer YOUR_APIFY_TOKEN" } } } } ``` Once connected, just ask your agent something like: *"Use the Wikipedia Trending Pageview Tracker Actor to tell me which of these 5 topics is gaining the most reader attention this week."* ๐Ÿ“š [Apify MCP server docs](https://docs.apify.com/platform/integrations/mcp) *** ### ๐Ÿค– Ask an AI assistant about this Actor - ๐Ÿ’ฌ [**ChatGPT**](https://chat.openai.com/?q=How%20do%20I%20use%20the%20Wikipedia%20Trending%20Topic%20Pageview%20Tracker%20on%20Apify%3F) - ๐Ÿง  [**Claude**](https://claude.ai/new?q=How%20do%20I%20use%20the%20Wikipedia%20Trending%20Topic%20Pageview%20Tracker%20on%20Apify%3F) - ๐Ÿ” [**Perplexity**](https://perplexity.ai/search?q=How%20do%20I%20use%20the%20Wikipedia%20Trending%20Topic%20Pageview%20Tracker%20on%20Apify%3F) *** ### โ“ Frequently Asked Questions #### ๐Ÿงฉ How does it work? It calls Wikimedia's official Pageviews REST API per article, then splits the daily view history into last-7-days vs prior-7-days to compute a momentum ratio. #### Why not just show total pageviews? Total views conflate old, evergreen popularity with new spikes. Momentum tells you what's happening right now. #### What counts as "SURGING"? A momentum ratio of 1.5+ (this week's views are 50%+ higher than the prior week's). #### โฐ Can I schedule regular runs? Yes. Use Apify Schedules to run this Actor on any cron interval. #### โš–๏ธ Is this data legal to collect? This Actor only collects data that is publicly accessible via Wikimedia's free API. No login, no bypass of access controls. #### ๐Ÿ’ณ Do I need a paid Apify plan? No. The free Apify plan is enough for testing and small runs. #### ๐Ÿ” What happens if a run fails? Transient errors retry automatically with backoff. If one article has no data (very new/renamed pages sometimes lack history), the rest of the run still completes and the log explains exactly why. #### ๐Ÿ†˜ What if I need help? Open an issue on this Actor's Issues tab. *** ### ๐Ÿ”Œ Integrate with any app - [**Zapier**](https://docs.apify.com/platform/integrations/zapier) - [**Make**](https://docs.apify.com/platform/integrations/make) - [**n8n**](https://docs.apify.com/platform/integrations) - [**Google Sheets**](https://docs.apify.com/platform/integrations/drive) - [**Slack**](https://docs.apify.com/platform/integrations/slack) - [**Gmail**](https://docs.apify.com/platform/integrations/gmail) - [**Webhooks**](https://docs.apify.com/platform/integrations/webhooks) - [**MCP**](https://docs.apify.com/platform/integrations/mcp) *** ### ๐Ÿ”— Recommended Actors Pairs well with [HN Topic Momentum Tracker](https://apify.com/saint_person/hn-topic-momentum-tracker) for comparing mainstream reader attention against developer-community buzz. > ๐Ÿ’ก **Pro tip:** browse the complete [saint\_person collection](https://apify.com/saint_person) for more tools like this. *** ### ๐Ÿ†˜ Help & custom requests - ๐Ÿ› **Something not working?** Open an issue on this Actor's **Issues tab**. - ๐Ÿ› ๏ธ **Need a custom feature or a different data source?** Open an issue describing what you need. *** > **โš ๏ธ Disclaimer:** this Actor is an independent tool and is not affiliated with, endorsed by, or sponsored by the Wikimedia Foundation. All trademarks are the property of their respective owners. Only publicly available data is collected. # Actor input Schema ## `articles` (type: `string`): Comma-separated list of English Wikipedia article titles to compare (up to 10), using underscores for spaces exactly as in the Wikipedia URL, e.g. 'Artificial\_intelligence, Bitcoin'. Works out of the box with the prefilled example. ## `daysBack` (type: `integer`): How many days of pageview history to analyze (minimum 14, so a last-7-days-vs-prior-7-days comparison is possible). ## Actor input object example ```json { "articles": "Artificial_intelligence, Bitcoin, World_War_II, Olympic_Games, Taylor_Swift", "daysBack": 14 } ``` # 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 = { "articles": "Artificial_intelligence, Bitcoin, World_War_II, Olympic_Games, Taylor_Swift" }; // Run the Actor and wait for it to finish const run = await client.actor("saint_person/wikipedia-trending-pageviews-tracker").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ 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); }); // ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ 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 = { "articles": "Artificial_intelligence, Bitcoin, World_War_II, Olympic_Games, Taylor_Swift" } # Run the Actor and wait for it to finish run = client.actor("saint_person/wikipedia-trending-pageviews-tracker").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "articles": "Artificial_intelligence, Bitcoin, World_War_II, Olympic_Games, Taylor_Swift" }' | apify call saint_person/wikipedia-trending-pageviews-tracker --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=saint_person/wikipedia-trending-pageviews-tracker", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Wikipedia Trending Topic Pageview Tracker", "description": "Compare Wikipedia articles by pageview momentum - which topics are gaining reader attention this week versus last, ranked side by side.", "version": "1.0", "x-build-id": "nbw0Zfxob3R9W3nJy" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/saint_person~wikipedia-trending-pageviews-tracker/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-saint_person-wikipedia-trending-pageviews-tracker", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/saint_person~wikipedia-trending-pageviews-tracker/runs": { "post": { "operationId": "runs-sync-saint_person-wikipedia-trending-pageviews-tracker", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/saint_person~wikipedia-trending-pageviews-tracker/run-sync": { "post": { "operationId": "run-sync-saint_person-wikipedia-trending-pageviews-tracker", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "articles" ], "properties": { "articles": { "title": "Wikipedia article titles", "type": "string", "description": "Comma-separated list of English Wikipedia article titles to compare (up to 10), using underscores for spaces exactly as in the Wikipedia URL, e.g. 'Artificial_intelligence, Bitcoin'. Works out of the box with the prefilled example." }, "daysBack": { "title": "Days of history", "minimum": 14, "maximum": 60, "type": "integer", "description": "How many days of pageview history to analyze (minimum 14, so a last-7-days-vs-prior-7-days comparison is possible).", "default": 14 } } }, "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 } } } } } } } } } } ```