Exploring the dynamic contribution behavior of editors in wikis based on time series analysis
Exploring the dynamic contribution behavior of editors in wikis based on time series analysis
复制标题
基于时间序列分析探究维基编辑动态贡献行为
DOI：
10.1108/prog-06-2013-0034
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Baozhen Lee
中科院分区：
文献类型：
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作者：
Linghe Huang;Qinghua Zhu;Jia Tina Du;Baozhen Lee
目的：维基百科是一种新的信息生产和组织形式，已成为最重要的知识资源之一。近年来，随着维基用户数量的增加，“搭便车问题”日益严重。为了激励编辑者为 wiki 系统做出更多贡献，充分了解他们的贡献行为非常重要。本文的目的是探索维基编辑者动态贡献行为的规律。设计/方法/方法。——在开发了动态贡献行为模型后，作者采用计量和聚类方法来处理时间序列数据。实验数据来自百度百科，这是一个类似于维基百科的著名中文维基系统。调查结果。——编辑者分为四类：“测试者”、“辍学者”、“拖延者”和“贴纸”。测试人员，贡献的内容最少，编辑几篇文章后就迅速停止贡献。在编辑大量内容后，退出者完全停止贡献。延迟者是指在观察时间内没有停止投稿，但可能在不久的将来停止投稿的编辑。持续贡献和编辑最多内容的贴纸是核心编辑。此外，一天中的时间和节假日对编辑的贡献数量有显着的影响。原创性/价值——通过使用时间序列分析的方法，发现了编辑和编辑类型的一些新特征。与以往的研究相比，本研究的样本也更大。因此，结果更具科学性和代表性，可以帮助管理者更好地优化维基系统，制定编辑者的激励策略。
Purpose.– Wiki is a new form of information production and organization, which has become one of the most important knowledge resources. In recent years, with the increase of users in wikis, “free rider problem” has been serious. In order to motivate editors to contribute more to a wiki system, it is important to fully understand their contribution behavior. The purpose of this paper is to explore the law of dynamic contribution behavior of editors in wikis..Design/methodology/approach.– After developing a dynamic model of contribution behavior, the authors employed both the metrological and clustering methods to process the time series data. The experimental data were collected from Baidu Baike, a renowned Chinese wiki system similar to Wikipedia..Findings.– There are four categories of editors: “testers,” “dropouts,” “delayers” and “stickers.” Testers, who contribute the least content and stop contributing rapidly after editing a few articles. After editing a large amount of content, dropouts stop contributing completely. Delayers are the editors who do not stop contributing during the observation time, but they may stop contributing in the near future. Stickers, who keep contributing and edit the most content, are the core editors. In addition, there are significant time-of-day and holiday effects on the number of editors’ contributions..Originality/value.– By using the method of time series analysis, some new characteristics of editors and editor types were found. Compared with the former studies, this research also had a larger sample. Therefore, the results are more scientific and representative and can help managers to better optimize the wiki systems and formulate incentive strategies for editors.