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).

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.

Dataset

This dataset was created by Bhavin Moriya

Contents

Dataset Card for Wikipedia English September 2024

Dataset created using this repo with a September 2024 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

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.

SparkWiki toolkit can be used in various scenarios where you are interested in researching Wikipedia graph and pageview statistics. Graph and pageviews can be used and studied separately. The code used to process Wikipedia SQL dumps, along with deployment instructions, are located on GitHub.

To test an example of a pre-processed graph, you can download a dump of the English Wikipedia graph (see attached wikipedia_nrc.dump), which you can directly import into a Neo4J instance. The dump is intended for neo4j version 3.x and can be imported using the following command (make sure you do not have an existing wikipedia.db database as the command below will overwrite its content):

sudo -u neo4j neo4j-admin load --force --from=wikipedia_nrc.dump --database=wikipedia.db

If you try to import it into Neo4J version 4.x, you need to set the property

dbms.allow_upgrade=true in /etc/neo4j/neo4j.conf

before importing. When you start the neo4j server it will upgrade the database s.t. it is compatible with version 4.x.

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.

Raising public awareness of sepsis, a potentially life-threatening dysregulated host response to infection, to hasten its recognition has become a major focus of physicians, investigators, and both non-governmental and governmental agencies. While the internet is a common means by which to seek out healthcare information, little is understood about patterns and drivers of these behaviors. We sought to examine traffic to Wikipedia, a popular and publicly available online encyclopedia, to better understand how, when, and why users access information about sepsis. Utilizing pageview traffic data for all available language localizations of the sepsis and septic shock pages between July 1, 2015 and June 30, 2018, significantly outlying daily pageview totals were identified using a seasonal hybrid extreme studentized deviate approach. Consecutive outlying days were aggregated, and a qualitative analysis was undertaken of print and online news media coverage to identify potential correlates. Traffic patterns were further characterized using paired referrer to resource (i.e. clickstream) data, which were available for a temporal subset of the pageviews. Of the 20,557,055 pageviews across 65 linguistic localizations, 47 of the 1,096 total daily pageview counts were identified as upward outliers. After aggregating sequential outlying days, 25 epochs were examined. Qualitative analysis identified at least one major news media correlate for each, which were typically related to high-profile deaths from sepsis and, less commonly, awareness promotion efforts. Clickstream analysis suggests that most sepsis and septic shock Wikipedia pageviews originate from external referrals, namely search engines. Owing to its granular and publicly available traffic data, Wikipedia holds promise as a means by which to better understand global drivers of online sepsis information seeking. Further characterization of user engagement with this information may help to elucidate means by which to optimize the visibility, content, and delivery of awareness promotion efforts.

This dataset contains 145063 time series representing the number of hits or web traffic for a set of Wikipedia pages from 2015-07-01 to 2022-06-30. This is an extended version of the dataset that was used in the Kaggle Wikipedia Web Traffic forecasting competition. For consistency, the same Wikipedia pages that were used in the competition have been used in this dataset as well. The colons (:) in article names have been replaced by dashes (-) to make the .tsf file readable using our data loaders.

The data were downloaded from the Wikimedia REST API. According to the conditions of the API, this dataset is licensed under CC-BY-SA 3.0 and GFDL licenses.

This dataset contains 145063 time series representing the number of hits or web traffic for a set of Wikipedia pages from 2015-07-01 to 2022-06-30. This is an extended version of the dataset that was used in the Kaggle Wikipedia Web Traffic forecasting competition. For consistency, the same Wikipedia pages that were used in the competition have been used in this dataset as well. The colons (:) in article names have been replaced by dashes (-) to make the .tsf file readable using our data loaders.

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

The data were downloaded from the Wikimedia REST API. According to the conditions of the API, this dataset is licensed under CC-BY-SA 3.0 and GFDL licenses.

Data and methods development are key to improve our ability to forecast conflict. Relatively recent data sources such as mobile phone and social media data or images have received widespread attention in conflict research. Oftentimes these do not cover substantial parts of the globe or they are difficult to obtain and manipulate, which makes regular updating challenging. The sometimes vast amounts of data can also be computationally and financially costly. The data source we propose instead is cheap, readily and openly available, and updated in real time, and it provides global coverage: Wikipedia. We argue that the number of country page views can be considered a measure of interest or salience, whereas the number of page changes can be considered a measure of controversy between competing political views. We expect these predictors to be particularly successful in capturing tensions before a conflict escalates. We test our argument by predicting changes in battle-related deaths in Africa on the country-month level. We find evidence that country page views do increase predictive performance while page changes do not. Contrary to our expectation, our model seems to capture long-term trends better than sharp short-term changes.

Per the Federal Digital Government Strategy, the Department of Homeland Security Metrics Plan, and the Open FEMA Initiative, FEMA is providing the following web performance metrics with regards to FEMA.gov.rnrnInformation in this dataset includes total visits, avg visit duration, pageviews, unique visitors, avg pages/visit, avg time/page, bounce ratevisits by source, visits by Social Media Platform, and metrics on new vs returning visitors.rnrnExternal Affairs strives to make all communications accessible. If you have any challenges accessing this information, please contact FEMAWebTeam@fema.dhs.gov.

Since the various datasets have different sizes, the percentages are not symmetrical. Each row shows the result for one of the methods. For instance, the CycleRank row shows the fraction of features it shares with the other feature sets.

This repository contains the data of the study "The impact of news exposure on collective attention in the United States during the 2016 Zika epidemic".

Epidemiological data

The folder zika_USA_weekly_cases_2016.zip contains weekly ZIKV incidence counts reported by the US Centers for Disease Control and Prevention in 2016, by state. Data were extracted from reports made publicly available by the CDC at: https://zenodo.org/record/584136#.Xk07-RNKjOQ

Web news data

The file news_GDELT_data.csv.gz contains all news items extracted from the GDELT platform (https://www.gdeltproject.org/) matching TAX_DISEASE_ZIKA as a Theme, and United_States as a Location in the GDELT platform.

TV closed captions

The file zika_TV_mentions_dataframe.csv contains all the TV news items of 2016 matching the word ``Zika" in the TV News Archive https://archive.org/details/tv

Wikipedia pageview counts

Dataset 1: wikipedia_dataset1_zika_daily_pageview_usa.csv

Content of each line of the dataset: day, pageview_count

The dataset contains the daily number of pageview counts of 128 different Wikipedia pages related to the Zika virus (aggregated and summed to total) originated in the United States, from January 1st to December 31st, 2016.

Dataset 2: wikipedia_dataset2_zika_daily_pageview_bystate.zip

Content of each line of the dataset: day, pageview_count, state

The dataset contains the daily number of pageview counts of 128 different Wikipedia pages related to the Zika virus (aggregated and summed to total) originated in the United States, disaggregated by state, from January 1st to December 31st, 2016.

Dataset 3: wikipedia_dataset3_zika_pagecount_by_city.csv

Content of each line of the dataset: US_city, pageview_count_Zika,pageview_count_total

The dataset contains the total number of pageview counts of 128 different Wikipedia pages related to the Zika virus (pageview_count_Zika) originated in 788 cities (US_city) of the United States with a population larger than 40,000 in 2016.The dataset also contains the total number of pageview counts to all Wikipedia pages (all Wikipedia projects, pageview_count_total) originated in 788 cities (US_city) of the United States with a population larger than 40,000 in 2016."