# Wikipedia Most Viewed Articles and Pageviews Radar (`adaptive_nightmare/wikipedia-trending-radar`) Actor

Scrape daily top 1,000 most-viewed Wikipedia articles across all languages with view counts, ranks, summary abstracts, and thumbnail pictures.

- **URL**: https://apify.com/adaptive_nightmare/wikipedia-trending-radar.md
- **Developed by:** [Darim Quraishi](https://apify.com/adaptive_nightmare) (community)
- **Categories:** News, AI, Education
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$1.00 / 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/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Wikipedia Most Viewed Articles Scraper

Retrieve daily top-viewed Wikipedia articles across any language edition with view counts, rankings, article descriptions, and thumbnail images.

### Features

- Fetches the daily top 1,000 most-viewed articles for any calendar date.
- Supports all language editions, including English, Spanish, German, French, and Japanese.
- Extracts exact daily view counts and ranking positions.
- Enriches entries with summary abstracts and thumbnail image URLs.
- Automatically filters out administrative pages like Main Page and Special pages.
- Queries the official Wikimedia REST API proxy-free.

### Input parameters

| Parameter | Type | Default | Description |
|---|---|---|---|
| `language` | String | `"en"` | Wikipedia language edition (`"en"`, `"es"`, `"de"`, `"fr"`, `"ja"`, etc.). |
| `date` | String | `""` | Target date in `YYYY-MM-DD` (e.g. `2026-10-18`). Default is the most recent available day (yesterday). |
| `maxItems` | Number | `50` | Maximum number of trending topics to return (up to 1,000). |
| `enrichDetails` | Boolean | `true` | Fetch opening summary extract, subtitle, and thumbnail image for each topic. |
| `filterSpecialPages` | Boolean | `true` | Remove internal pages like `Main_Page` or `Special:Search`. |

### Output format

`json
{
  "rank": 4,
  "article": "Jailer 2",
  "views": 321853,
  "date": "2026-10-19",
  "language": "en",
  "url": "https://en.wikipedia.org/wiki/Jailer_2",
  "description": "2026 Indian film by Nelson Dilipkumar",
  "summary": "Jailer 2 is a 2026 Indian Tamil-language action comedy film written and directed by Nelson Dilipkumar...",
  "thumbnail": "https://upload.wikimedia.org/wikipedia/en/d/dc/Jailer_2_poster.jpg",
  "scrapedAt": "2026-10-20T12:00:00.000Z"
}
`

### Use cases

- Trend analysis: Measure public attention spikes around breaking news and cultural events.
- Content planning: Identify topics with high search interest for publishing ideas.
- Academic research: Analyze historical pageview distributions across subjects.
- Media intelligence: Detect viral stories before they saturate secondary channels.

### Pricing and efficiency

- Typical runtime: 2 to 4 seconds per execution.
- Zero proxy costs: Runs over public REST and RSS endpoints without residential proxy pools.
- Suitable for scheduled runs and continuous monitoring under standard platform limits.

# Actor input Schema

## `language` (type: `string`):

Language edition of Wikipedia to scrape.

## `date` (type: `string`):

Specific date in 'YYYY-MM-DD' format (e.g. '2026-10-18'). Leave empty to automatically fetch the most recent aggregated day (yesterday).

## `maxItems` (type: `integer`):

Number of top trending articles to return (up to 1,000).

## `enrichDetails` (type: `boolean`):

Fetch high-quality opening paragraph summary, subtitle description, and thumbnail image for each article.

## `filterSpecialPages` (type: `boolean`):

Automatically remove internal Wikipedia system pages (Main_Page, Special:Search, File:, etc.) so only genuine real-world topics appear.

## Actor input object example

```json
{
  "language": "en",
  "maxItems": 50,
  "enrichDetails": true,
  "filterSpecialPages": true
}
```

# Actor output Schema

## `results` (type: `string`):

No description

# 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {};

// Run the Actor and wait for it to finish
const run = await client.actor("adaptive_nightmare/wikipedia-trending-radar").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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {}

# Run the Actor and wait for it to finish
run = client.actor("adaptive_nightmare/wikipedia-trending-radar").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{}' |
apify call adaptive_nightmare/wikipedia-trending-radar --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,adaptive_nightmare/wikipedia-trending-radar"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/bAtFvN1APXPMDtePs/builds/mcN2Hz0JuDMfIx8Mu/openapi.json