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


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.

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    Wikidata’s Phabricator Ticketing System

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    Wikidata Game

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    Wikidata Revolution presentation at Wikimania 2017, in August 2017

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    According to Wikimedia Foundation, an active user is “A user with 5+ edits in the main namespace of a given project over the last 30 days”

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    Retention Science

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    In 2015 Google announced the port of the Freebase knowledge base to Wikidata.

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    Wikidata Wiki dump

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    Wikibase actions

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    Preprocessed Wikidata History

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    Research Metrics

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    Wikibase actions

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    The Random Forest parameters chosen are: 100 estimators and bootstrap technique with subsample class balancing.

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    Causes to drop out in Wikipedia by the community

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    Wikipedia Mentorship

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    This idea, together with the major findings of this research were presented in a talk at WikidataCon The Wikidata community appreciated the findings and welcomed this proposal to improve editor attrition.

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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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Correspondence to Cristina Sarasua.

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Sarasua, C., Checco, A., Demartini, G. et al. The Evolution of Power and Standard Wikidata Editors: Comparing Editing Behavior over Time to Predict Lifespan and Volume of Edits. Comput Supported Coop Work 28, 843–882 (2019).

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  • Wikidata
  • Knowledge
  • Power editors
  • Standard editors
  • Evolution