Please use this identifier to cite or link to this item: https://dl.eusset.eu/handle/20.500.12015/3748
Title: The Evolution of Power and Standard Wikidata Editors: Comparing Editing Behavior over Time to Predict Lifespan and Volume of Edits
Authors: Sarasua, Cristina
Checco, Alessandro
Demartini, Gianluca
Difallah, Djellel
Feldman, Michael
Pintscher, Lydia
Keywords: Evolution;Knowledge;Power editors;Standard editors;Wikidata
Issue Date: 2019
Publisher: Springer
metadata.dc.relation.ispartof: Computer Supported Cooperative Work (CSCW): Vol. 28, No. 5
metadata.mci.reference.pages: 843-882
Series/Report no.: Computer Supported Cooperative Work (CSCW)
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.
metadata.dc.identifier.doi: 10.1007/s10606-018-9344-y
URI: http://dx.doi.org/10.1007/s10606-018-9344-y
https://dl.eusset.eu/handle/20.500.12015/3748
ISSN: 1573-7551
Appears in Collections:JCSCW Vol. 28 (2019)

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