Abstract
This paper presents research relevant to predicting future editing by Wikipedia editors. We demonstrate the importance of each characteristic and attempt to clarify the characteristics that affect prediction. Clarifying this can help the Wikimedia Foundation (WMF) understand the editor’s actions. This research adopted the increase in prediction errors as the means of evaluating the importance of a characteristic and thus computed the importance of each characteristic. We used random forest (RF) regression for calculating the importance. Characteristic evaluation in our experiment revealed that the past number of edits and the editing period increased predictive accuracy. Furthermore, information regarding earlier edit actions clearly contains factors that determine future edit actions.
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Yoshida, Y., Ohwada, H. (2012). Identifying Important Factors for Future Contribution of Wikipedia Editors. In: Richards, D., Kang, B.H. (eds) Knowledge Management and Acquisition for Intelligent Systems. PKAW 2012. Lecture Notes in Computer Science(), vol 7457. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32541-0_25
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DOI: https://doi.org/10.1007/978-3-642-32541-0_25
Publisher Name: Springer, Berlin, Heidelberg
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