Wikipedia Pageviews: Trends, Top Articles & Languages

What does Wikipedia Pageviews do?

Wikipedia Pageviews gives you real page-view numbers for any Wikipedia articles, in any language, straight from the Wikimedia Foundation's official statistics. Unlike Google Trends, which only shows relative interest from 0 to 100, these are actual counts of people reading, every day since July 2015.

It does the analysis for you:

• Compare any number of articles over time: total views, daily average, share of attention, peak day, 30-day momentum, year-over-year change, trend direction, seasonal peak month, spikes, and a 3-month forecast for each.
• True totals with redirects: people reach "Taylor Swift" through "Taylor swift", "T. Swift" and dozens of other titles. Wikipedia counts each separately; this Actor adds them back in.
• One topic in every language: how much is "Coffee" read in Japanese, German, Spanish or Persian? See where in the world a topic is followed.
• Most-read and surging articles for any recent days: what the world is reading right now, and what is spiking.

Every run saves a visual report: an interactive chart with a log-scale toggle, article cards with forecasts, language bars and a top list.

It runs on the Apify platform, so you get API access, scheduling, webhooks, integrations (Make, Zapier, n8n, Google Sheets) and monitoring with no setup.

Live example reports

Real reports from real runs:

Example	What it shows	Report
Pop stars compared	Taylor Swift, Beyoncé, Bad Bunny, Sabrina Carpenter, Chappell Roan and Billie Eilish over 3 years	Open report
Coffee in 25 languages	Where in the world people read about coffee	Open report
Most read this week	The top 100 articles and what is surging	Open report

Why use it?

• Attention research: measure public interest in people, brands, products, events or ideas with real numbers rather than an index.
• Media and entertainment: track artists, films, shows and athletes; spot what is trending before it peaks.
• Market and investment signals: page views of companies and products are a well-known proxy for public attention.
• SEO and content: find seasonal topics and rising subjects, and see which language markets care about a topic.
• Academic and data journalism: clean daily series since 2015, with bots and crawlers excluded by default.

How to get Wikipedia page views

1. Click Try for free.
2. Enter articles: titles, wikipedia.org links in any language, or de:Berlin style for a language edition.
3. Pick a time range and a mode: Compare articles, One topic in every language, or Most-read and surging articles.
4. Click Start. Open Visual report in the Output tab, or download the dataset.

Input

Field	What it does
mode	compare, languages or top
articles	Titles, links or lang:Title. Spelling and capitals are fixed and redirects followed automatically
language	Language edition for plain titles and for the top list (en, de, ja and so on)
timeRange	Last 30 days, 90 days, 12 months, 3 years, 5 years, since July 2015, or custom dates
includeRedirects, maxRedirectsPerArticle	Add views that arrive through alternative titles
agent	People only (default) or people, bots and crawlers
includeForecast	3-month forecast per article (needs about 2.5 years of data)
languagesLimit	Languages mode: how many language editions to measure
topDays, topEndDate, topLimit	Top mode: which days and how many articles

Example: five pop stars over three years.

{
    "articles": ["Taylor Swift", "Beyoncé", "Bad Bunny", "Sabrina Carpenter", "Chappell Roan"],
    "timeRange": "last-3-years"
}

Example: where in the world people read about coffee.

{ "mode": "languages", "articles": ["Coffee"], "timeRange": "last-12-months", "languagesLimit": 20 }

Output

One row per article (compare), per language edition (languages) or per top article (top), plus the visual report. You can download the dataset in various formats such as JSON, HTML, CSV, or Excel.

A real result (trimmed):

{
    "article": "Taylor Swift",
    "language": "en",
    "summary": "Taylor Swift: falling (-57.8% a year), up 46.1% over the last 30 days, -58.4% year over year, seasonal peak in February.",
    "totalViews": 38336297,
    "averageDailyViews": 34978,
    "peakDay": "2024-02-12",
    "peakViews": 702450,
    "momentum30dPct": 46.1,
    "yoyChangePct": -58.4,
    "seasonalPeakMonth": "February",
    "viewsViaRedirects": 289343,
    "redirectsCounted": 25,
    "forecastNext3MonthsChangePct": -22.2,
    "forecastExpectedErrorPct": 26.1,
    "wikidataId": "Q26876",
    "url": "https://en.wikipedia.org/wiki/Taylor_Swift"
}

Data fields

Field	Meaning
totalViews, averageDailyViews, latestViews	Views in the range (people only by default)
shareOfAttentionPct	This article's share of all views across your articles
peakDay, peakViews, spikes	Biggest day and other unusual spikes
momentum30dPct, yoyChangePct	Last 30 days vs the 30 before, and vs the same period a year earlier
trendDirection, trendSlopePctPerYear	Rising, stable or falling, from a fitted trend line
seasonalPeakMonth, seasonalLowMonth	Seasonal pattern (needs 2+ years)
viewsViaRedirects, redirectSharePct	Views that arrived through alternative titles
forecastNext3MonthsChangePct, forecastExpectedErrorPct, forecast	3-month forecast, its typical error, and the daily forecast with a range
timeline	Every day's views
shareOfGlobalViewsPct	Languages mode: each edition's share of the topic's views
daysInDailyTop, bestDailyRank, lastDayVsEarlierPct	Top mode: staying power and surge

How much does it cost?

You pay per article analysed (or per language edition, or per top article), plus an optional small charge per daily timeline row in long format. The Wikimedia API is fast, so runs are quick: three years of daily views for five articles, with redirects, take well under a minute.

Tips

• Leave redirects on for true totals; for a quick look, lower maxRedirectsPerArticle.
• Use Last 3 years or longer to get year-over-year, seasonality and the forecast.
• Schedule the top mode daily with a webhook to catch what is surging every morning.
• Compare an article across languages before entering a new market.

How does the forecast work?

Each article gets the method that predicted it best in the past. Three simple methods compete (the recent level, the same period last year, and the seasonal pattern adjusted to the recent level) on four past cut-off points, and the winner's average error is reported as forecastExpectedErrorPct.

FAQ, disclaimers, and support

Is this official data? Yes. It comes from the Wikimedia Foundation's public Pageviews API, the same numbers as wikimedia.org statistics tools. Daily figures arrive about a day late, so ranges end two days ago.

Why do some top articles look odd? Wikimedia filters known bots, but occasional automated traffic still reaches the daily top lists. An article with millions of views and little news coverage may be one of those.

Is it allowed? The data is published openly by the Wikimedia Foundation for reuse. The Actor identifies itself to Wikimedia as their API policy asks.

Something not working? Open an issue on the Issues tab with the run link. Need a custom version? Get in touch through the Issues tab.