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International Conference on Social Informatics

SocInfo 2017: Social Informatics pp 31–40Cite as

An Exploration of Wikipedia Data as a Measure of Regional Knowledge Distribution

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10540))

Abstract

In today’s economies, knowledge is the key ingredient for prosperity. However, it is hard to measure this intangible asset appropriately. Standard economic models mostly rely on common measures such as enrollment rates and international test scores. However, these proxies focus rather on the quality of education of pupils than on the distribution of knowledge among the whole population, which is increasingly defined by alternative sources of education such as online learning platforms. As a consequence, the economically relevant stock of knowledge in a region is only roughly approximated. Furthermore, they are abstract in content, and both capital-, and time-consuming in census. This paper proposes to explore Wikipedia data as an alternative source of capturing the knowledge distribution on a narrow geographical scale. Wikipedia is by far the largest digital encyclopedia worldwide and provides data on usage and editing publicly. We compare Wikipedia usage worldwide and edits in the U.S. to existing measures of the acquisition and stock of knowledge. The results indicate that there is a significant correlation between Wikipedia interactions and knowledge approximations on different geographical scales. Considering these results, it seems promising to further explore Wikipedia data to develop a reliable, inexpensive, and real-time proxy of knowledge distribution around the world.

F. Stephany and F. Braesemann—Both authors contributed equally to this work.

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Notes

  1. 1.

    Wikipedia provides over 40 million articles in 250 languages worldwide and is ranked among the top-ten most popular websites, see [3].

  2. 2.

    According the [3] the English language version alone has more than 30 million registered editors and additionally a large number of not-registered editors.

  3. 3.

    http://bit.ly/PageViewsPerCountry.

  4. 4.

    http://bit.ly/WikiSQL.

  5. 5.

    Visualiszations of these information geographies can be found here: [18, 19].

  6. 6.

    http://data.worldbank.org/.

  7. 7.

    http://bit.ly/PageViewsPerCountry.

  8. 8.

    Thus we include all articles that link to the category “Computer Science” itself (level 1), to the subcategories that link to the “Computer Science” (level 2) and to their subcategories (level 3). This number of layers has been chosen to avoid the collection of edit data that are only very weakly related to computer science.

  9. 9.

    http://bit.ly/WikiSQL.

  10. 10.

    An interactive version of the map can be accessed via the online dashboard that provides supplementary information to this article: http://bit.ly/Wiki_Dashboard.

  11. 11.

    The data stems from the U.S. census: https://www.census.gov/.

  12. 12.

    Data on the number of students stems from http://www.stateuniversity.com/. City-level covariates are collected from http://www.city-data.com// and a list of academic computer science departments is available on Wikipedia: http://bit.ly/CS_Departments.

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Acknowledgements

The authors are thankful for the feedback this work received on the brown-bag seminar at the Oxford Internet Institute and the SIS Statistics and Data Science conference in Florence, both taken place in June 2017. Particularly helpful comments have been made by Scott Hale, Otto Kässi, and Taha Yasseri.

Author information

Authors and Affiliations

  1. Vienna University of Economics and Business, Vienna, Austria

    Fabian Stephany

  2. Oxford Internet Institute, University of Oxford, Oxford, UK

    Fabian Braesemann

Authors
  1. Fabian Stephany
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  2. Fabian Braesemann
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Correspondence to Fabian Stephany .

Editor information

Editors and Affiliations

  1. Indiana University, Bloomington, Indiana, USA

    Giovanni Luca Ciampaglia

  2. University of Washington, Seattle, Washington, USA

    Afra Mashhadi

  3. University of Oxford, Oxford, United Kingdom

    Taha Yasseri

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Stephany, F., Braesemann, F. (2017). An Exploration of Wikipedia Data as a Measure of Regional Knowledge Distribution. In: Ciampaglia, G., Mashhadi, A., Yasseri, T. (eds) Social Informatics. SocInfo 2017. Lecture Notes in Computer Science(), vol 10540. Springer, Cham. https://doi.org/10.1007/978-3-319-67256-4_4

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  • DOI: https://doi.org/10.1007/978-3-319-67256-4_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67255-7

  • Online ISBN: 978-3-319-67256-4

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