一种推断维基百科贡献者概况的聚类方法
A clustering approach to infer Wikipedia contributors' profile
摘要：
在在线社区中，最近的研究大大提高了我们对不同类型或档案的贡献者的了解，从偶然到非常参与的人，通过专注的人。然而，他们使用非常复杂的方法（定性-定量混合，人工编纂/描述编辑的工作量很大），使从业人员的复制受到限制。这些研究仅在英文维基百科上。本文的目的是突出不同的配置文件的贡献者与聚类技术。独创性是展示如何只使用编辑，以及它们随时间的分布，允许在语言之间建立具有良好准确性和稳定性的贡献者配置文件。罗马尼亚和丹麦的维基验证的方法。突出显示的个人资料在参与历史的早期就可以识别，这表明对新来者的轻度监控可能足以适应与他们的互动并提高留存率。
英文摘要：
In online communities, recent studies have strongly improved our knowledge about the different types or profiles of contributors, from casual to very involved ones, through focused people. However they do so by using very complex methodologies (qualitative-quantitative mix, with a high workload to manually codify/characterize the edits), making their replication for the practitioners limited. These studies are on the English Wikipedia only. The objective of this paper is to highlight different profiles of contributors with clustering techniques. The originality is to show how using only the edits, and their distribution over time, allows to build these contributors profiles with a good accuracy and stability amongst languages. The methodology is validated with both Romanian and Danish wikis. The highlighted profiles are identifiable early in the history of involvement, suggesting that light monitoring of newcomers may be sufficient to adapt the interaction with them and increase the retention rate.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
