Defense Mechanism or Socialization Tactic? Improving Wikipedia's Notifications to Rejected Contributors
Defense Mechanism or Socialization Tactic? Improving Wikipedia's Notifications to Rejected Contributors
复制标题
防御机制还是社会化策略？
DOI：
10.1609/icwsm.v6i1.14263
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
S. Walling
中科院分区：
文献类型：
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作者：
R. Geiger;Aaron L Halfaker;M. Pinchuk;S. Walling
与传统公司不同，开放协作系统依靠志愿者来运作，许多社区都难以维持足够的贡献者以确保内容的质量和数量。然而，维基百科历来面临着完全相反的问题：参与过多，特别是来自那些有意或无意地与资深维基百科人没有相同规范的用户。在其指数增长期间，维基百科社区发展了专门的社会技术防御机制，以保护自身免受大量参与带来的负面影响：垃圾信息、恶意破坏、虚假信息以及其他损害。然而最近，维基百科在招募和留住新贡献者方面面临了一些备受瞩目的问题。在本文中，我们首先说明并描述了维基百科中起作用的各种防御机制，我们假设这些机制抑制了新用户的留存。接下来，我们展示了一项实验的结果，该实验旨在通过改变这些防御机制的各种要素，特别是在撤销或拒绝贡献时发送给新编辑的预设警告和通知，来提高编辑的数量和质量。通过逻辑回归对新用户活动进行建模，我们根据用户加入维基百科时的动机，展示了哪些策略对不同用户群体最有效。特别是，我们发现，维基百科人以主动语态表明自己身份并对拒绝编辑的贡献直接负责的个性化消息，在各种结果指标上都比目前通常使用机构化和被动语态的消息成功得多。
Unlike traditional firms, open collaborative systems rely on volunteers to operate, and many communities struggle to maintain enough contributors to ensure the quality and quantity of content. However, Wikipedia has historically faced the exact opposite problem: too much participation, particularly from users who, knowingly or not, do not share the same norms as veteran Wikipedians. During its period of exponential growth, the Wikipedian community developed specialized socio-technical defense mechanisms to protect itself from the negatives of massive participation: spam, vandalism, falsehoods, and other damage. Yet recently, Wikipedia has faced a number of high-profile issues with recruiting and retaining new contributors. In this paper, we first illustrate and describe the various defense mechanisms at work in Wikipedia, which we hypothesize are inhibiting newcomer retention. Next, we present results from an experiment aimed at increasing both the quantity and quality of editors by altering various elements of these defense mechanisms, specifically pre-scripted warnings and notifications that are sent to new editors upon reverting or rejecting contributions. Using logistic regressions to model new user activity, we show which tactics work best for different populations of users based on their motivations when joining Wikipedia. In particular, we found that personalized messages in which Wikipedians identified themselves in active voice and took direct responsibility for rejecting an editor’s contributions were much more successful across a variety of outcome metrics than the current messages, which typically use an institutional and passive voice.