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.

在2020年的总统大选中，数字广告花费了近20亿美元，在线广告预计只会增长。然而，竞选活动向数字化的迅速转变给那些跟踪政治广告的人带来了头痛。Facebook和谷歌等在线平台提供的数据格式不同，包含无法直接比较的不同集合，并且缺乏必要的元数据来查找与特定竞选活动相关的所有广告，所有以特定候选人为特色的广告，或者讨论特定问题的所有广告。庞大的内容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，例如在某个特定种族或所有联邦种族中在线花费了多少钱。该项目提供了必要的基础设施来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”在多大程度上主导竞选活动，以及竞选活动中错误信息的传播和影响。该项目团队正在构建可扩展的基础设施，以获取，处理，整合，标记和分发来自两个大型在线平台的数字选举广告数据。其结果是一个集中的存储库，它为所有过程和代码提供了强大的文档，以便可以修改它们以应用于其他平台和环境进行扩展。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整合和标准化，降低了个体研究人员的成本，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关联信息，并产生了可访问的数据，使社区受益。实现这一目标是一项高风险高回报的奋进。例如，需要新的方法或途径来捕捉和标记相关广告的焦点，特别是当真正的赞助商不清楚或伪装。这个奖项反映了NSF的法定使命，并已被认为是值得支持的评估使用基金会的知识价值和更广泛的影响审查标准。