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WikiShark: An Online Tool for Analyzing Wikipedia Traffic and Trends

Published: 03 June 2021 Publication History

Abstract

Wikipedia is a major source of information utilized by internet users around the globe for fact-checking and access to general, encyclopedic information. For researchers, it offers an unprecedented opportunity to measure how societies respond to events and how our collective perception of the world evolves over time and in response to events. Wikipedia use and the reading patterns of its users reflect our collective interests and the way they are expressed in our search for information – whether as part of fleeting, zeitgeist-fed trends or long-term – on most every topic, from personal to business, through political, health-related, academic and scientific. In a very real sense, events are defined by how we interpret them and how they affect our perception of the context in which they occurred, rendering Wikipedia invaluable for understanding events and their context. This paper introduces WikiShark (www.wikishark.com) – an online tool that allows researchers to analyze Wikipedia traffic and trends quickly and effectively, by (1) instantly querying pageview traffic data; (2) comparing traffic across articles; (3) surfacing and analyzing trending topics; and (4) easily leveraging findings for use in their own research.

References

[1]
Wikipedia's Pageview dumps, https://dumps.wikimedia.org/other/pageview_complete/
[2]
Yasseri, T. and Bright, J., 2016. Wikipedia traffic data and electoral prediction: towards theoretically informed models. EPJ Data Science, 5 (1), pp.1-15. https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-016-0083-3
[3]
HighCharts – Highcharts: Interactive JavaScript charts for web pages, https://www.highcharts.com
[4]
High-Stock – Time Series charts for webpages, https://www.highcharts.com/demo/stock
[5]
Wikipedia API., https://www.mediawiki.org/wiki/API:Main_page
[6]
jQuery Sparklink. A web plugin which generates small inline charts directly in the browser, using data supplied either inline in the HTML or via javascript, https://omnipotent.net/jquery.sparkline,
[7]
Twitter Developer API, https://developer.twitter.com/en/docs/twitter-api
[8]
TextRazor – Natural Language Processing and Artificial Intelligence techniques to parse, analyze and extract semantic metadata from your content, https://www.textrazor.com/
[9]
Internet Advertising Bureau Content Taxonomy v2, https://www.iab.com/guidelines/content-taxonomy/
[10]
WikiShark Chrome Extension https://chrome.google.com/webstore/detail/wikishark-Wikipedia-stati/jmbdjjmajaloijoimjbheaohdjfednge
[11]
Lampos, Vasileios, Maimuna S. Majumder, Elad Yom-Tov, Michael Edelstein, Simon Moura, Yohhei Hamada, Molebogeng X. Rangaka, Rachel A. McKendry, and Ingemar J. Cox. “Tracking COVID-19 using online search.” NPJ digital medicine 4, no. 1 (2021): 1-11. https://www.nature.com/articles/s41746-021-00384-w
[12]
Wikitrends - Graph Visualization of Wikipedia, https://wiki-insights.epfl.ch/wikitrends/
[13]
Wikipulse, - Wikipedia popularity trends, https://wikipulse.com/
[14]
Pageviews Analysis, https://pageviews.toolforge.org/Adya, Paramvir Bahl, Jitendra Padhye, Alec Wolman, and Lidong Zhou. 2004. A multi-radio unification protocol for IEEE

Cited By

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  • (2023)ardl: Estimating autoregressive distributed lag and equilibrium correction modelsThe Stata Journal: Promoting communications on statistics and Stata10.1177/1536867X23121243423:4(983-1019)Online publication date: 21-Dec-2023
  • (2023)It’s not an encyclopedia, it’s a market of agendas: Decentralized agenda networks between Wikipedia and global news media from 2015 to 2020New Media & Society10.1177/1461444822114964126:11(6235-6259)Online publication date: 28-Jan-2023
  • (2023)Traffic Prediction Method for Time Series Networks Based on ARIMA-LSTM Model2023 IEEE 16th International Conference on Electronic Measurement & Instruments (ICEMI)10.1109/ICEMI59194.2023.10270072(384-388)Online publication date: 9-Aug-2023
  • Show More Cited By

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Published In

cover image ACM Conferences
WWW '21: Companion Proceedings of the Web Conference 2021
April 2021
726 pages
ISBN:9781450383134
DOI:10.1145/3442442
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 June 2021

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Author Tags

  1. Data Dumps
  2. WikiShark
  3. Wikipedia Page Views
  4. Wikipedia Trends

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  • Research-article
  • Research
  • Refereed limited

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WWW '21
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WWW '21: The Web Conference 2021
April 19 - 23, 2021
Ljubljana, Slovenia

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

View all
  • (2023)ardl: Estimating autoregressive distributed lag and equilibrium correction modelsThe Stata Journal: Promoting communications on statistics and Stata10.1177/1536867X23121243423:4(983-1019)Online publication date: 21-Dec-2023
  • (2023)It’s not an encyclopedia, it’s a market of agendas: Decentralized agenda networks between Wikipedia and global news media from 2015 to 2020New Media & Society10.1177/1461444822114964126:11(6235-6259)Online publication date: 28-Jan-2023
  • (2023)Traffic Prediction Method for Time Series Networks Based on ARIMA-LSTM Model2023 IEEE 16th International Conference on Electronic Measurement & Instruments (ICEMI)10.1109/ICEMI59194.2023.10270072(384-388)Online publication date: 9-Aug-2023
  • (2023)Fame through surprise: How fame-seeking mass shooters diversify their attacksProceedings of the National Academy of Sciences10.1073/pnas.2216972120120:20Online publication date: 8-May-2023
  • (2021)Measuring the name recognition of politicians through WikipediaJournal of Elections, Public Opinion and Parties10.1080/17457289.2021.200948534:1(180-189)Online publication date: 5-Dec-2021

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