Collaborative Research: EAGER: Cross-platform Election Advertising Transparency Initiative

合作研究：EAGER：跨平台选举广告透明度倡议

• 批准号: 2235008
• 负责人: Travis Ridout
• 金额: $ 1.99万
• 依托单位: Washington State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2235008&HistoricalAwards=false
• 关键词: Collaborative; Research; EAGER; Cross; platform

Nearly $2 billion was spent on digital advertising in the presidential general election in 2020, and online advertising is only expected to grow. Campaigns’ rapid shift to digital, however, has caused headaches for those who track political advertising. The data provided by online platforms, such as Facebook and Google, come in different formats, contain different collections that are not immediately comparable, and lack metadata necessary to find all ads related to a specific campaign for office, all ads featuring a specific candidate, or all ads within either set that discuss a specific issue. The sheer volume of content requires the use of computational methods to extract the information necessary to answer even basic descriptive questions such as how much money was spent online in a particular race or across all federal races. This project provides the infrastructure necessary to answer critical questions about the role of digital advertising in American democracy, including the extent to which “dark money” dominates campaigns, and the spread and reach of misinformation in campaigns.The project team is building expandable infrastructure to acquire, process, integrate, label, and distribute digital election advertising data from two large online platforms. The result is a centralized repository that provides robust documentation of all procedures and code such that they can be modified to apply to other platforms and contexts for expansion. Cross-platform integration and standardization of digital election advertising data through human and state-of-the-art computational methods reduces costs to individual researchers, provides parallel procedures already in place for analyzing TV advertising (and thus maximizing comparability to existing data), provides linkage information to other data sources, and produces accessible data to benefit the community. Accomplishing this aim is a high risk-high reward endeavor. For instance, new approaches or methods are needed to capture and label relevant advertising by focus, especially when the true sponsor is unclear or disguised.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.

2020 年总统大选的数字广告支出接近 20 亿美元，预计在线广告只会增长。然而，竞选活动向数字化的迅速转变给那些追踪政治广告的人带来了麻烦。 Facebook 和 Google 等在线平台提供的数据格式不同，包含无法立即比较的不同集合，并且缺乏查找与特定竞选活动相关的所有广告、以特定候选人为特色的所有广告或任一集合中讨论特定问题的所有广告所需的元数据。庞大的内容量需要使用计算方法来提取必要的信息，以回答甚至是基本的描述性问题，例如在特定种族或所有联邦种族中在网上花了多少钱。该项目提供了必要的基础设施，以回答有关数字广告在美国民主中的作用的关键问题，包括“黑钱”在竞选活动中的主导程度，以及竞选活动中错误信息的传播和影响范围。项目团队正在构建可扩展的基础设施，以从两个大型在线平台获取、处理、集成、标记和分发数字选举广告数据。其结果是一个集中存储库，提供所有过程和代码的可靠文档，以便可以修改它们以应用于其他平台和上下文以进行扩展。通过人工和最先进的计算方法对数字选举广告数据进行跨平台集成和标准化，降低了个人研究人员的成本，提供了用于分析电视广告的并行程序（从而最大限度地提高了与现有数据的可比性），提供了与其他数据源的链接信息，并生成了可访问的数据以使社区受益。实现这一目标是一项高风险高回报的努力。例如，需要新的途径或方法来按焦点捕获和标记相关广告，特别是当真正的赞助商不清楚或被伪装时。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。