Abstract
Building a shared understanding of a specific area of interest is of increasing importance in today’s information-centric world. A shared understanding of a domain can be realized by building a structured knowledge base about it collaboratively. Our research is driven by the goal to understand participation patterns over time in collaborative knowledge building efforts. Consequently, we focus our study on one representative project – Wikidata. Wikidata is a free, structured knowledge base that provides structured data to Wikipedia and other Wikimedia projects. This paper builds upon previous research, where we identified six common participation patterns, i.e. roles, in Wikidata. In the research presented here, we study the applicability of sequence analysis methods by analyzing the dynamics in users’ participation patterns. The sequence analysis is judged by its ability to answer three questions: (i) “Are there any preferable role transitions in Wikidata?”; (ii) “What are the dominant dynamic participation patterns?”; (iii) “Are users who join earlier more turbulent contributors?” Our data set includes participation patterns of about 20,000 users in each month from October 2012 to October 2014. We show that sequence analysis methods are able to infer interesting role transitions in Wikidata, find dominant dynamic participation patterns, and make statistical inferences. Finally, we also discuss the significance of these results with respect to the understanding of the participation process in Wikidata.
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Notes
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- 2.
Wikidata is organized into namespaces. Each namespace contains a specific kind of artifacts. Property namespace, for example, groups all Wikidata’s pages about properties whereas the main namespace comprises all the items.
- 3.
The monthly period of time has been chosen to determine stable patterns of participation with fewer fluctuations than seen in weekly periods of time.
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Cuong, T.T., Müller-Birn, C. (2016). Applicability of Sequence Analysis Methods in Analyzing Peer-Production Systems: A Case Study in Wikidata. In: Spiro, E., Ahn, YY. (eds) Social Informatics. SocInfo 2016. Lecture Notes in Computer Science(), vol 10047. Springer, Cham. https://doi.org/10.1007/978-3-319-47874-6_11
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