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

CHS：小型：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: 1815507
• 负责人: Darren Gergle
• 金额: $ 25万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1815507&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.

该项目将阐明并推进对等生成结构化数据的关键新现象。在过去的二十年里，对等生产已经成为一种新的知识生产方式，比如维基百科的主要成功案例。最近，对等生产社区已经开始生产一种新的结构化和机器可读的数据，Wikidata和OpenStreetMap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关键区别在于：(1)为了使这些社区产生的数据是机器可读的，它必须遵循严格的语法和语义规则；这与传统对等生产社区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最终用户看到之前被算法消费和处理，因此，贡献者编辑的最终后果对他们来说大多是不可见的；(3) Wikidata和OpenStreetMap没有提供不同的语言版本，而是选择制作一个单一的全球数据集，旨在跨语言和文化使用。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，以及跨多种应用程序和语言和文化障碍的结构化数据的相关性和实用性。它将量化重要算法的价值，这些价值来自分布在全球各地的人们的志愿努力。它将显示同行产生的结构化数据作为全球通用语言的程度——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。它将导致软件工具的创建，以改进数据生产过程，从而改进所产生的数据。本研究试图通过关注与结构化数据对等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对同行的产品贡献者来说是一个强大的激励因素。本研究将调查当这个激励因素被创建结构化的、机器可读的数据所必需的严格规则削弱时，贡献者的行为是如何变化的。(2)社区产生的数据价值。有几种估算维基百科贡献价值的方法。然而，Wikidata和OpenStreetMap的数据通常是由算法处理的——甚至可能是由不同目的的多种算法处理的——然后才交付给人类。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，使这些信息对编辑和整个社区可见。(3)独立于语言的数据生成。这项研究将分析维基数据和开放街道地图以独立于语言的方式开展工作这一雄心勃勃的决定的后果。具体地说，它将检查该决策在多大程度上导致所生成的数据中的不良影响。该奖项反映了美国国家科学基金会的法定使命，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

The Gendered Geography of Contributions to OpenStreetMap: Complexities in Self-Focus Bias

OpenStreetMap 贡献的性别地理：自我关注偏差的复杂性

• DOI: 10.1145/3290605.3300793
• 发表时间: 2019
• 期刊: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
• 作者: Das, Maitraye;Hecht, Brent;Gergle, Darren
• 通讯作者: Gergle, Darren

Behavioral Use Licensing for Responsible AI

• DOI: 10.1145/3531146.3533143
• 发表时间: 2020-11
• 期刊: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency
• 作者: Danish Contractor;Daniel J. McDuff;J. Haines;Jenny Lee;Christopher Hines;Brent J. Hecht

Ethical Tensions, Norms, and Directions in the Extraction of Online Volunteer Work

在线志愿者工作提取中的道德张力、规范和方向

• DOI: 10.1145/3500868.3560923
• 发表时间: 2022
• 期刊: Companion Publication of the 2022 Conference on Computer Supported Cooperative Work and Social Computing (CSCW'22 Companion
• 作者: Li, Hanlin;Ajmani, Leah;Zhou, Moyan;Vincent, Nicholas;Hwang, Sohyeon;Piccardi, Tiziano;Narayan, Sneha;Daniel, Sherae;Veselovsky, Veniamin
• 通讯作者: Veselovsky, Veniamin

Errors in Geotargeted Display Advertising: Good News for Local Journalism?

地理定位展示广告中的错误：对当地新闻业来说是个好消息吗？

• DOI: 10.1145/3449166
• 发表时间: 2021
• 期刊: Proceedings of the ACM on Human-Computer Interaction
• 作者: Bandy, Jack;Hecht, Brent
• 通讯作者: Hecht, Brent

Measuring the Monetary Value of Online Volunteer Work

衡量在线志愿工作的货币价值

• DOI: 10.1609/icwsm.v16i1.19318
• 发表时间: 2022
• 期刊: Proceedings of the International AAAI Conference on Web and Social Media
• 作者: Li, Hanlin;Hecht, Brent;Chancellor, Stevie
• 通讯作者: Chancellor, Stevie