Auto-generated structured data of Wikipedia Pageviews from table Fields

This file contains a count of pageviews to the English-language Wikipedia from 2015-03-16T00:00:00 to 2015-04-25T15:59:59, grouped by timestamp (down to a one-second resolution level) and site (mobile or desktop). The smallest number of events in a group is 645; because of this, we are confident there should not be privacy implications of releasing this data.

In November 2023, the English version of Wikipedia received over 3 billion page views originating from the United States across all platforms. The United Kingdom was the country to generate the second-most page views for the subdomain, with 809.9 million views, followed by India, with 773.2 million visualizations.

The most viewed English-language article on Wikipedia in 2023 was Deaths in 2024, with a total of 44.4 million views. Political topics also dominated the list, with articles related to the 2024 U.S. presidential election and key political figures like Kamala Harris and Donald Trump ranking among the top ten most viewed pages. Wikipedia's language diversity As of December 2024, the English Wikipedia subdomain contained approximately 6.91 million articles, making it the largest in terms of content and registered active users. Interestingly, the Cebuano language ranked second with around 6.11 million entries, although many of these articles are reportedly generated by bots. German and French followed as the next most populous European language subdomains, each with over 18,000 active users. Compared to the rest of the internet, as of January 2024, English was the primary language for over 52 percent of websites worldwide, far outpacing Spanish at 5.5 percent and German at 4.8 percent. Global traffic to Wikipedia.org Hosted by the Wikimedia Foundation, Wikipedia.org saw around 4.4 billion unique global visits in March 2024, a slight decrease from 4.6 billion visitors in January. In addition, as of January 2024, Wikipedia ranked amongst the top ten websites with the most referring subnets worldwide.

Abstract (our paper)

The frequency of a web search keyword generally reflects the degree of public interest in a particular subject matter. Search logs are therefore useful resources for trend analysis. However, access to search logs is typically restricted to search engine providers. In this paper, we investigate whether search frequency can be estimated from a different resource such as Wikipedia page views of open data. We found frequently searched keywords to have remarkably high correlations with Wikipedia page views. This suggests that Wikipedia page views can be an effective tool for determining popular global web search trends.

Data

personal-name.txt.gz:
The first column is the Wikipedia article id, the second column is the search keyword, the third column is the Wikipedia article title, and the fourth column is the total of page views from 2008 to 2014.

personal-name_data_google-trends.txt.gz, personal-name_data_wikipedia.txt.gz:
The first column is the period to be collected, the second column is the source (Google or Wikipedia), the third column is the Wikipedia article id, the fourth column is the search keyword, the fifth column is the date, and the sixth column is the value of search trend or page view.

Publication

This data set was created for our study. If you make use of this data set, please cite:
Mitsuo Yoshida, Yuki Arase, Takaaki Tsunoda, Mikio Yamamoto. Wikipedia Page View Reflects Web Search Trend. Proceedings of the 2015 ACM Web Science Conference (WebSci '15). no.65, pp.1-2, 2015.
http://dx.doi.org/10.1145/2786451.2786495
http://arxiv.org/abs/1509.02218 (author-created version)

Note

The raw data of Wikipedia page views is available in the following page.
http://dumps.wikimedia.org/other/pagecounts-raw/

Number of visits in 2019 for a sample of 23462 English Wikipedia articles which contain references to academic sources which have a green open access copy available but not yet used. The consultation statistics were retrieved from the Wikimedia pageviews API using the Python client (script also included). The sample was selected among articles which in April 2020 had at least one citation of an academic paper (using the "cite journal" template) for which OAbot (through Unpaywall data) had found a green open access URL to add (gratis open access, not necessarily libre open access). Data shows that the top 1 % most visited articles received 30 % of the visits: over 500 million in the year, corresponding to 1 million potential citation link clicks to distribute across all references assuming a 0.2 % click-through rate per Piccardi et al. (2020).

Abstract (our paper)

This paper investigates the page view and interlanguage link at Wikipedia for Japanese comic analysis. This paper is based on a preliminary investigation, and obtained three results, but the analysis is insufficient to use the results for a market research immediately. I am looking for research collaborators in order to conduct a more detailed analysis.

Data

Publication

This data set was created for our study. If you make use of this data set, please cite:
Mitsuo Yoshida. Preliminary Investigation for Japanese Comic Analysis using Wikipedia. Proceedings of the Fifth Asian Conference on Information Systems (ACIS 2016). pp.229-230, 2016.

As of March 2020, the most visited Wikipedia page in the United States was "2020 Democratic party presidential primaries" with * million visits during the month. The second-most visited page was "2019-20 coronavirus pandemic" with *** million visits. A significant portion of the top visited Wikipedia pages in March are related to the global coronavirus pandemic.

Context

• Time Series: Time series is a set of observations recorded over regular interval of time, Time series can be beneficial in many fields like stock market prediction, weather forecasting. - Accounts for the fact that data points taken over time may have an internal structure (such as auto correlation, trend or seasonal variation) that should be accounted for.

• Web traffic: Amount of data sent and received by visitors to a website. - Sites monitor the incoming and outgoing traffic to see which parts or pages of their site are popular and if there are any apparent trends, such as one specific page being viewed mostly by people in a particular country

Content

Contains Page Views for 60k Wikipedia articles in 8 different languages taken on a daily basis for 2 years.

https://i.ibb.co/h1JCgpY/DSLC.png" alt="DSLC">

A Data Science Life Cycle can be used to create a project. Forecasting can be done for any interval provided sufficient dataset is available. Refer the Github link in the tasks to view the forecast done using ARIMA and Prophet. Further feel free to contribute. Several other models can be used including a neural network to improve the results by many folds.

Acknowledgements

Credits :
1. Wikipedia 2. Google

In March 2024, close to 4.4 billion unique global visitors had visited Wikipedia.org, slightly down from 4.4 billion visitors since August of the same year. Wikipedia is a free online encyclopedia with articles generated by volunteers worldwide. The platform is hosted by the Wikimedia Foundation.

Pearson and Spearman correlation between Wikipedia pageviews and news volume.

Code and data to recreate analyses and Figures from the paper 'Using Wikipedia Pageview Data to Investigate Public Interest in Climate Change at a Global Scale' https://doi.org/10.1145/3614419.3644007

The analysis is based on the following data and code files:

(0) The database wiki_climate.duckdb contains the pageview data. It is stored for convenience in the database but is the same data that is described and can be downloaded from this Wikimedia blogpost . The main analysis is performed in wiki_climate_analysis.R.

(1) wikidata_cc_results.csv: Contains the wikidata QIDs for all concepts that are part in WikiProject climate change, including whether this concept exists in the top 25 most visited language editions.

(2) wiki_cc_article_size.csv: is the article size in bytes for all articles of WPCC in these LE (to create Figure 1) For this the function in getWikiPageSize.R was used.

(3) urls_wikidataid_map.csv and redirected_urls_wikidatamap.csv contain the QIDs of all unique URLs present in the duck_db Wikipedia pageview data. Now we can filter out only those that are part of WPCC

(4) WP2022_Demographic_Indicators_Medium.csv: Is used to get the population for every year so per capita pageviews can be calculated.

(5) On of the main functions is getCountryCCTotalDay() from getCountryCCTotalDay.R which needs to run manually for each year. These years are merged and then later saved for convenience in daily_cc_interest_total_per_country.csv

(6) The causal impact analysis can be done using the file wiki_cc_causal_impact.R.

In the six months ending March 2024, the United States accounted for 25.66 percent of traffic to Wikipedia.org. Japan was ranked second, accounting for over five percent of web visits to the website, closely followed by the United Kingdom and Germany.

This dataset contains the page view statistics for all the WikiMedia projects in the year 2014, ordered by (project, page, timestamp). It has been generated starting from the WikiMedia's pagecounts-raw[1] dataset.The CSV uses spaces as delimiter, without any form of escaping because it is not needed. It has 5 columns:* project: the project name* page: the page requested, url-escaped* timestamp: the timestamp of the hour (format: "%Y%m%d-%H%M%S")* count: the number of times the page has been requested (in that hour)* bytes: the number of bytes transferred (in that hour)You can download the full dataset via torrent[2].Further information about this dataset are available at:http://disi.unitn.it/~consonni/datasets/wikipedia-pagecounts-sorted-by-page-year-2014/[1] https://dumps.wikimedia.org/other/pagecounts-raw/[2] http://disi.unitn.it/~consonni/datasets/wikipedia-pagecounts-sorted-by-page-year-2014/#download

Dataset Card for Wikipedia English June 2025

Dataset created using this repo with a June 2025 Wikipedia snapshot. This repo also has a precomputed pageviews database. This database has the aggregated number of views for each page in Wikipedia. This file is built using the Wikipedia Pageview complete dumps

Species pages extracted from the English Wikipedia article XML dump from 2022-08-02. Multimedia, vernacular names and textual descriptions are extracted, but only pages with a taxobox or speciesbox template are recognized.

See https://github.com/mdoering/wikipedia-dwca for details.

This dataset was used in the Kaggle Wikipedia Web Traffic forecasting competition. It contains 145063 daily time series representing the number of hits or web traffic for a set of Wikipedia pages from 2015-07-01 to 2017-09-10.

The original dataset contains missing values. They have been simply replaced by zeros.

Context

This dataset allows for a comparison between the offline and online spread of COVID-19.

Content

• COVID-19 confirmed cases, deaths, recoveries
• Google Trends for COVID-19
• Wikipedia page views for COVID-19 from different countries

Acknowledgements

The dataset was obtained using the following APIs: https://github.com/pomber/covid19 https://github.com/GeneralMills/pytrends https://wikitech.wikimedia.org/wiki/Analytics/AQS/Pageviews

Inspiration

Can internet traffic data help to understand the spread of the virus?

Daily visits to Wikipedia articles about COVID-19

Dataset

This dataset was created by Gaurav Dutta

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