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The Evolution of Power and Standard Wikidata Editors: Comparing Editing Behavior over Time to Predict Lifespan and Volume of Edits

  • Cristina SarasuaEmail author
  • Alessandro Checco
  • Gianluca Demartini
  • Djellel Difallah
  • Michael Feldman
  • Lydia Pintscher
Article

Abstract

Knowledge bases are becoming a key asset leveraged for various types of applications on the Web, from search engines presenting ‘entity cards’ as the result of a query, to the use of structured data of knowledge bases to empower virtual personal assistants. Wikidata is an open general-interest knowledge base that is collaboratively developed and maintained by a community of thousands of volunteers. One of the major challenges faced in such a crowdsourcing project is to attain a high level of editor engagement. In order to intervene and encourage editors to be more committed to editing Wikidata, it is important to be able to predict at an early stage, whether an editor will or not become an engaged editor. In this paper, we investigate this problem and study the evolution that editors with different levels of engagement exhibit in their editing behaviour over time. We measure an editor’s engagement in terms of (i) the volume of edits provided by the editor and (ii) their lifespan (i.e. the length of time for which an editor is present at Wikidata). The large-scale longitudinal data analysis that we perform covers Wikidata edits over almost 4 years. We monitor evolution in a session-by-session- and monthly-basis, observing the way the participation, the volume and the diversity of edits done by Wikidata editors change. Using the findings in our exploratory analysis, we define and implement prediction models that use the multiple evolution indicators.

Keywords

Wikidata Knowledge Power editors Standard editors Evolution 

Notes

Acknowledgments

We would like to thank Michele Catasta for his feedback at an early stage of this research, and the rest of the participants of our Dagstuhl Research Meeting “Crowdsourcing Research - Transcending Disciplinary Boundaries”. We also would like to thank Michael Luggen for his help to set up one of the machines used for the experiments of this project. This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 732328, as well as from the COST Action IC1302 - Keystone.

During the manuscript reviewing process, several authors changed their affiliation. Part of the work presented in this paper was carried out while Cristina Sarasua was affiliated with the University of Koblenz-Landau (Germany) and visited the University of Sheffield (United Kingdom), Gianluca Demartini was affiliated with the University of Sheffield (United Kingdom) and Djellel Difallah was affiliated with the University of Fribourg (Switzerland).

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Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  • Cristina Sarasua
    • 1
    Email author
  • Alessandro Checco
    • 2
  • Gianluca Demartini
    • 3
  • Djellel Difallah
    • 4
  • Michael Feldman
    • 1
  • Lydia Pintscher
    • 5
  1. 1.University of ZurichZurichSwitzerland
  2. 2.University of SheffieldSheffieldUK
  3. 3.University of QueenslandQueenslandAustralia
  4. 4.New York UniversityNew YorkUSA
  5. 5.Wikimedia DeutschlandDeutschlandGermany

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