Wikipedia Article Data & Pageviews Scraper

Look up any Wikipedia article and get its summary and metadata paired with the official daily pageviews trend, so you see not just what a topic is but how much attention it is getting right now. Search by keyword or pull the day's trending articles too. Here is one real result, with every base field the actor returns:

{
  "title": "OpenAI",
  "description": "American artificial intelligence company",
  "extract": "OpenAI is an American artificial intelligence (AI) research organization headquartered in San Francisco, consisting of OpenAI Group PBC, a for-profit public benefit corporation (PBC), partially controlled by OpenAI Foundation, a nonprofit. OpenAI develops generative AI models, particularly the GPT series of large language models. Its release of ChatGPT in November 2022 has been credited with catalyzing the AI boom, and widespread interest in generative AI.",
  "url": "https://en.wikipedia.org/wiki/OpenAI",
  "lang": "en",
  "pageId": 48795986,
  "lastEdit": "2026-08-17T09:50:57Z",
  "thumbnailUrl": null,
  "type": "standard",
  "pageviewsWindowDays": 30,
  "pageviewsTotalWindow": 213442,
  "pageviewsAvgDaily": 7360,
  "pageviewsLatestDay": 7685,
  "pageviewsTrendPct": -4.7,
  "observedAt": "2026-08-17T11:25:36.861Z",
  "error": null
}

Wikipedia article intelligence in one actor. It returns the article summary, description, canonical URL, page ID, lead image and last-edit timestamp, and pairs each one with the official Wikimedia pageviews time series distilled into a window total, a daily average, the latest day, and a 7-day-over-7-day trend percentage. That is what turns a bare summary into a signal you can track.

📥 Input · 📤 Output · 💰 Pricing · ▶️ Examples

Table of contents

• What it does
• Use cases
• Quickstart
• Input reference
• Output reference
• Run via API and CLI
• Fetch results
• Billing and limits
• FAQ and troubleshooting

What it does

The actor works in three modes and writes one clean record per article to the run's dataset. In article mode you pass a list of titles and get each one back with its summary and pageviews trend. In search mode you pass a keyword and it finds the matching articles, each enriched with the same summary and pageviews fields plus a matched snippet and word count. In top mode it returns the day's trending articles ranked by real pageviews, with non-article entries such as the main page and search pages filtered out. Because official pageviews data lags a day or two, top mode defaults to the most recent day with data and steps back automatically until it finds a populated ranking.

Every record carries the pageviews aggregates computed over your chosen window (30, 60 or 90 days): pageviewsTotalWindow, pageviewsAvgDaily, pageviewsLatestDay and a pageviewsTrendPct comparing the last seven days with the seven before. Two optional add-ons (paid Apify plans only) go further. includePageviewSeries attaches the full daily time series of date and views for seasonality and charting. withAiSummary distills the extract into a TL;DR, key facts, a category and named entities. Missing values are always returned as null, never invented, and articles that do not exist come back as an unbilled error row.

Use cases

• Content and topic research. Pull summaries and metadata for a list of topics and rank them by how much attention each is getting, so your content calendar follows real demand.
• SEO and topic trend monitoring. Track the pageviews trend of the Wikipedia articles behind your target keywords to spot rising and cooling topics before they show up in other tools.
• Trending topics discovery. Use top mode to surface the day's most-viewed articles in any language edition for newsrooms, social teams and market research.
• Knowledge panels and enrichment. Fetch clean descriptions, extracts, lead images and canonical URLs to power knowledge panels, chat answers and internal wikis.
• ML and NLP datasets. Assemble multilingual article summaries with attention signals and, optionally, the full pageviews time series for training and evaluation datasets.
• Reputation and PR tracking. Watch the daily pageviews of the articles for your brand, people or products to quantify spikes in public interest.

Quickstart

Open the actor, paste this into the input, and press Run. It returns two articles with their 30-day pageviews trend.

{
  "mode": "article",
  "titles": ["OpenAI", "Artificial intelligence"],
  "pageviewDays": "30",
  "maxResults": 25
}

Switch mode to search and set a query to find articles by keyword, or set mode to top to pull the day's trending articles. Change lang to any Wikipedia edition code such as es, de or ja.

Input reference

Field	Type	Required	Default	Description
mode	string	no	article	What to fetch: article, search or top.
titles	string[]	for article mode	["OpenAI","Artificial intelligence"]	Article titles to look up in article mode.
query	string	for search mode	quantum computing	Keyword(s) to find matching articles in search mode.
date	string	no	(most recent)	Day to rank in top mode, YYYY-MM-DD. Leave empty to use the latest day with data.
lang	string	no	en	Wikipedia language edition code, for example en, es, de, fr, pt, ja.
pageviewDays	string	no	30	Pageviews window to aggregate: 30, 60 or 90 days.
maxResults	integer	no	25	Maximum number of article rows to return. Free plans are capped at 10.
includePageviewSeries	boolean	no	false	Paid add-on. Attach the full daily pageviews time series to each article. Billed per article on a non-empty series.
withAiSummary	boolean	no	false	Paid AI add-on. Add a TL;DR, key facts, category and entities from the extract. Billed per article on usable output.

Output reference

One dataset item per article. Types: string, integer, number, array, or null when the source value is absent.

Field	Type	Description
title	string	Article title.
description	string	Short one-line description of the article.
extract	string	Plain-text lead summary.
url	string	Canonical desktop URL of the article.
lang	string	Wikipedia language edition code.
pageId	integer	Wikipedia numeric page identifier.
lastEdit	string	ISO 8601 timestamp of the last edit.
thumbnailUrl	string	Lead image thumbnail URL, or null.
type	string	Article type, standard or disambiguation.
pageviewsWindowDays	integer	Number of days aggregated for the pageviews figures.
pageviewsTotalWindow	integer	Total pageviews over the window.
pageviewsAvgDaily	integer	Average daily pageviews over the window.
pageviewsLatestDay	integer	Pageviews on the most recent available day.
pageviewsTrendPct	number	Percent change of the last 7 days versus the prior 7 days.
snippet	string	Search mode only: matched text snippet.
wordcount	integer	Search mode only: article word count.
articleSize	integer	Search mode only: article size in bytes.
rank	integer	Top mode only: trending rank for the day.
views	integer	Top mode only: views on the ranked day.
topDate	string	Top mode only: the day the ranking is for.
pageviewSeries	array	Add-on: full daily time series of {date, views}, or null.
aiTldr	string	AI add-on: one to two sentence TL;DR, or null.
aiKeyFacts	array	AI add-on: key facts, or null.
aiCategory	string	AI add-on: broad topic category, or null.
aiEntities	array	AI add-on: named entities, or null.
observedAt	string	ISO 8601 timestamp of when the record was collected.
error	string	Present only when an article could not be fetched; the row is not billed.

Run via API and CLI

Start a run and wait for it to finish, then read the dataset. Replace <TOKEN> with your Apify API token.

Run synchronously and get dataset items in one call:

curl -X POST "https://api.apify.com/v2/acts/scrapers_lat~wikipedia-articles-pageviews-scraper/run-sync-get-dataset-items?token=<TOKEN>" \
  -H "Content-Type: application/json" \
  -d '{"mode":"article","titles":["OpenAI","Artificial intelligence"],"pageviewDays":"30"}'

Start a run asynchronously:

curl -X POST "https://api.apify.com/v2/acts/scrapers_lat~wikipedia-articles-pageviews-scraper/runs?token=<TOKEN>" \
  -H "Content-Type: application/json" \
  -d '{"mode":"top","lang":"en","maxResults":25}'

Apify CLI:

apify call scrapers_lat/wikipedia-articles-pageviews-scraper \
  --input '{"mode":"search","query":"quantum computing","maxResults":25}'

Fetch results

Every run writes to a dataset. Fetch items as JSON, CSV, or Excel by changing format:

# JSON
curl "https://api.apify.com/v2/datasets/<DATASET_ID>/items?token=<TOKEN>&clean=true&format=json"

# CSV
curl "https://api.apify.com/v2/datasets/<DATASET_ID>/items?token=<TOKEN>&clean=true&format=csv"

# Paginate large datasets
curl "https://api.apify.com/v2/datasets/<DATASET_ID>/items?token=<TOKEN>&offset=1000&limit=1000"

<DATASET_ID> is returned as defaultDatasetId in the run object. Use offset and limit to page through large result sets. clean=true drops empty and internal fields.

Billing and limits

• Pay per article. You are charged per article record returned (result event). See the pricing tab for the current per-result price.
• Add-ons billed separately. pageviews_series is charged per article only when a non-empty daily series is attached, and ai_summary is charged per article only when the model returns usable output. Both are opt-in and available on paid Apify plans only.
• No charge on failure. Articles that do not exist and failed runs are written as an error row and are never charged. Empty and zero-result runs cost nothing.
• Spend cap respected. Set maxTotalChargeUsd on the run; once reached, the actor stops emitting and charging further billable results.
• Free Apify plans are capped at 10 rows per run and cannot use the paid add-ons. Upgrade for higher maxResults.

FAQ and troubleshooting

How fresh are the pageviews? Official Wikimedia pageviews data lags about one to two days, so the window ends two days before today and top mode uses the most recent day that has data. The pageviewsLatestDay value reflects that most recent complete day.

What does pageviewsTrendPct mean? It is the percentage change between the total views of the last seven days in the window and the seven days before that. A positive value means rising attention, a negative value means cooling. It is null when there are fewer than fourteen days of data.

Which languages are supported? Any Wikipedia language edition. Set lang to the edition code, for example en, es, de, fr, pt, ru, ja or zh. Titles, search and pageviews are all resolved within that edition.

How do I get the full daily pageviews history? Enable includePageviewSeries on a paid plan. Each article then includes a pageviewSeries array of {date, views} covering your chosen window, ready for charting and seasonality analysis.

A run returned an error row for a title. Why? The title did not resolve to an existing article in that language edition. Check spelling and capitalization, or use search mode to find the exact title. Error rows are not billed.

Is this an official Wikipedia or Wikimedia tool? No. This actor is independent and has no affiliation with Wikipedia or the Wikimedia Foundation. It reads only publicly available article metadata and pageviews.

Related scrapers

• arXiv Research Papers & Abstracts Scraper: Preprint metadata, authors and abstracts.
• Wikidata Entities Scraper: Structured entities, statements and identifiers.

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Independent tool, not affiliated with Wikipedia or the Wikimedia Foundation. Accesses only publicly available article metadata and pageviews.