Department of Informatics – DDIS

Dynamic and Distributed Information Systems Group

Article accepted at the Journal of CSCW

1. May 2018 | Suzanne Tolmeijer | Keine Kommentare |

Cristina Sarasua, together with her co-authors, Alessandro Checco, Gianluca Demartini, Djellel Difallah, Michael Feldman and Lydia Pintscher, got a paper accepted in the Journal of Computer Supported Cooperative Work. The link to their paper, titled ‘The Evolution of Power and Standard Wikidata Editors: Comparing Editing Behavior over Time to Predict Lifespan and Volume of Edits‘, will be added as soon as the special issue of the journal is published. More information about the paper can be found below.

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.
Cristina Sarasua, Alessandro Checco, Gianluca Demartini, Djellel Difallah, Michael Feldman and Lydia Pintscher



Abgelegt unter: Publications