CHS:Small:Collaborative Research: Structured Data Peer Production: Addressing Challenges and Leveraging Opportunities

CHS：小：协作研究：结构化数据同行生产：应对挑战并利用机遇

• 批准号: 1816348
• 负责人: Loren Terveen
• 金额: $ 24.97万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1816348&HistoricalAwards=false
• 关键词: CHS; Small; Collaborative; Research; Structured

This project will illuminate and advance the critical new phenomenon of peer-produced structured data. In the past two decades, peer production has emerged as a new means of knowledge production, such as the prime success story of Wikipedia. Recently, peer production communities have begun producing a new kind of data that is structured and machine-readable, with Wikidata and OpenStreetMap being two major examples. These structured data peer production communities differ from traditional peer production in key ways: (1) For the data produced by these communities to be machine-readable, it must follow strict syntactic and semantic rules; this contrasts with traditional peer production communities' fundamental ethos of contributor freedom; (2) The data produced by these communities is intended primarily to be consumed and processed by algorithms before becoming visible to end users and, thus, the ultimate consequences of contributors' edits are mostly invisible to them; and (3) instead of offering different language versions, Wikidata and OpenStreetMap both opted to produce a single, worldwide dataset intended for use across languages and cultures. This research will address numerous issues of significant social and economic impact, notably helping to improve the transparency, accuracy, and fairness of algorithms that affect millions of people's lives on a daily basis, and the relevance and utility of structured data across multiple applications and linguistic and cultural barriers. It will quantify the value important algorithms derive from the volunteer effort of people distributed across the globe. It will show the extent to which peer produced structured data serves as a global lingua franca - or whether, on the other hand, it embodies culturally specific assumptions that limit its applicability. It will lead to the creation of software tools that improve data production processes and thus the data produced. This research seeks to produce this understanding by focusing on three themes related to the structured data peer production process and data products: (1) Motivation of members of the communities. The ability to edit what one wants is a powerful motivating factor for peer production contributors. This research will investigate how contributor behavior changes when this motivating factor is blunted by the stringent rules necessary for the creation of structured, machine-readable data. (2) Value of data produced by the communities. There are several ways to estimate the value of contributions in Wikipedia. However, Wikidata and OpenStreetMap data typically are processed by algorithms - perhaps even by multiple algorithms for different purposes - before being delivered to humans. This makes it difficult to assess the value of these data. The research will investigate the value of structured data by identifying where it is used and how it contributes to algorithmic outputs; it also will create tools to make this information visible to editors and the community as a whole. (3) Data production in a language-independent context. The research will analyze the consequences of the ambitious decision by both Wikidata and OpenStreetMap to do their work in a language-independent way. Specifically, it will examine the extent to which this decision results in undesired effects in the data this generated.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目将阐明和推进同行产生的结构化数据的关键新现象。 在过去的二十年里，同行生产已经成为一种新的知识生产方式，例如维基百科的主要成功故事。 最近，对等生产社区已经开始生产一种结构化和机器可读的新型数据，Wikidata和OpenStreetMap就是两个主要的例子。这些结构化数据对等生产社区与传统对等生产社区的主要区别在于：（1）这些社区生产的数据必须遵循严格的语法和语义规则，这与传统对等生产社区的贡献者自由的基本精神形成鲜明对比;（2）这些社区产生的数据主要是为了在对最终用户可见之前由算法消费和处理，因此，贡献者编辑的最终结果对他们来说几乎是不可见的;（3）维基数据和OpenStreetMap都选择制作一个单一的、全球性的数据集，用于跨语言和文化的使用，而不是提供不同的语言版本。 这项研究将解决许多具有重大社会和经济影响的问题，特别是有助于提高影响数百万人日常生活的算法的透明度，准确性和公平性，以及结构化数据在多个应用程序和语言和文化障碍中的相关性和实用性。它将量化重要算法从分布在地球仪各地的人们的志愿努力中获得的价值。它将显示同行产生的结构化数据作为全球弗兰卡的程度-或者，另一方面，它是否体现了限制其适用性的特定文化假设。它将导致创建软件工具，以改进数据制作流程，从而改进所制作的数据。本研究旨在通过关注与结构化数据同行生产过程和数据产品相关的三个主题来产生这种理解：（1）社区成员的动机。编辑自己想要的东西的能力是同行生产贡献者的一个强大的激励因素。本研究将探讨如何贡献者的行为变化时，这种激励因素是由创建结构化的，机器可读的数据所必需的严格的规则钝化。(2)社区产生的数据的价值。有几种方法可以估算维基百科贡献的价值。然而，维基数据和OpenStreetMap的数据通常是由算法处理的-甚至可能是由多个算法用于不同的目的-在交付给人类之前。这使得很难评估这些数据的价值。该研究将通过确定结构化数据的使用位置以及它如何对算法输出做出贡献来调查结构化数据的价值;它还将创建工具，使编辑和整个社区都能看到这些信息。(3)在独立于语言的上下文中生成数据。这项研究将分析Wikidata和OpenStreetMap雄心勃勃的决定的后果，即以独立于语言的方式开展工作。具体而言，它将检查该决定在多大程度上导致了由此产生的数据中的不良影响。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Working for the Invisible Machines or Pumping Information into an Empty Void? An Exploration of Wikidata Contributors' Motivations

为看不见的机器工作还是将信息注入空虚的空间？

• DOI: 10.1145/3512982
• 发表时间: 2022
• 期刊: Proceedings of the ACM on Human-Computer Interaction
• 作者: Zhang, Charles Chuankai;Houtti, Mo;Smith, C. Estelle;Kong, Ruoyan;Terveen, Loren
• 通讯作者: Terveen, Loren

Quantifying the Gap: A Case Study of Wikidata Gender Disparities

量化差距：维基数据性别差异的案例研究

• DOI: 10.1145/3479986.3479992
• 发表时间: 2021
• 期刊: OpenSym 2021: 17th International Symposium on Open Collaboration
• 作者: Zhang, Charles Chuankai;Terveen, Loren
• 通讯作者: Terveen, Loren