CAREER: Large-Scale Examination of Problematic Online Behaviors and Their Regulators

职业：对有问题的在线行为及其监管者的大规模检查

• 批准号: 2045432
• 负责人: Ceren Budak
• 金额: $ 55万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2045432&HistoricalAwards=false
• 关键词: CAREER; Large; Scale; Examination; Problematic

This project will help improve the quality of conversations and information online by identifying the constraints that regulate cross-partisan animosity and disinformation across different social media platforms. Combining the strengths of political communication and socioeconomic theories with the methodological rigor of computational and experimental approaches, this research will identify: (1) which regulators, or strategies, are suitable or most effective for combating disinformation and cross-partisan animosity online; (2) how their strengths vary across behaviors and social media platforms; and (3) how these regulators interact, at times undermining or supporting each other. Scholars once thought that online social media platforms would bring in a new era of democratic discussion and debate. However, scholars and users alike are now mostly concerned about the dark side of these platforms - problems such as incivility, cross-partisan animosity, and disinformation are all commonplace online. While there have been efforts to combat these problems - such as the use of moral suasion to curb incivility and media literacy to curb misinformation - the approaches thus far lack a unified theoretical framework that allows for a systematic exploration of the solution space. This research will develop a framework connecting three of the modalities that regulate behavior online and offline: (1) Norms constrain through the sanctions or rules of a community. (2) Market constrains through price. (3) Architecture - built environment or code in online space - constrains through the structural burdens it imposes. The impact of these modalities on disinformation and cross-partisan animosity will be examined by developing a broad range of methodological approaches, spanning fields such as machine learning, network science, and causal inference. First, the project will contribute rich datasets and scalable machine learning and network science approaches for identifying cross-partisan animosity and disinformation online. Second, this project will bring together the theoretical strengths of legal and political communication scholarship and the computational strengths of computer and information sciences to combat problematic behaviors online. It will investigate the efficacy of different modalities of regulation through natural and randomized experiments and identify their interdependencies using structural equation modeling. Third, it will determine the generalizability of strategies by examining the behavior and efficacy of regulators across different platforms. Finally, most approaches that address problematic behaviors online treat individuals as the unit of analysis. However, structural regulators act upon communities. This project will overcome issues that generally undermine research at the individual level by performing comparative analyses across not just individuals but also communities.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.

该项目将通过确定调节不同社交媒体平台上跨党派敌意和虚假信息的限制因素，帮助提高在线对话和信息的质量。将政治传播和社会经济理论的优势与计算和实验方法的严谨性相结合，本研究将确定：（1）哪些监管机构或策略适合或最有效地打击网上虚假信息和跨党派仇恨； (2) 他们的优势如何因行为和社交媒体平台而异； (3) 这些监管机构如何相互作用，有时相互破坏或相互支持。学者们曾经认为在线社交媒体平台将带来民主讨论和辩论的新时代。然而，学者和用户现在最关心的是这些平台的阴暗面——不文明、跨党派仇恨和虚假信息等问题在网上司空见惯。尽管人们一直在努力解决这些问题，例如利用道德劝说来遏制不文明行为，利用媒体素养来遏制错误信息，但迄今为止，这些方法缺乏一个统一的理论框架，无法系统地探索解决方案空间。这项研究将开发一个框架，连接三种规范线上和线下行为的模式：（1）通过社区的制裁或规则进行规范约束。 (2)通过价格约束市场。 (3) 架构——在线空间中的构建环境或代码——通过其施加的结构负担进行约束。这些模式对虚假信息和跨党派敌意的影响将通过开发广泛的方法论来检验，涵盖机器学习、网络科学和因果推理等领域。首先，该项目将提供丰富的数据集以及可扩展的机器学习和网络科学方法，用于识别在线跨党派敌意和虚假信息。其次，该项目将汇集法律和政治传播学术的理论优势以及计算机和信息科学的计算优势，以打击在线问题行为。它将通过自然和随机实验研究不同监管方式的有效性，并使用结构方程模型确定它们的相互依赖性。第三，它将通过检查不同平台上监管机构的行为和效率来确定策略的普遍性。最后，大多数解决在线问题行为的方法都将个人视为分析单位。然而，结构性监管者对社区起作用。该项目将通过对个人和社区进行比较分析来克服通常破坏个人层面研究的问题。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。

项目成果

Wisdom of Two Crowds: Misinformation Moderation on Reddit and How to Improve this Process---A Case Study of COVID-19

两群人的智慧：Reddit 上的错误信息审核以及如何改进这一流程——以 COVID-19 为例

• DOI: 10.1145/3579631
• 发表时间: 2023
• 期刊: Proceedings of the ACM on Human-Computer Interaction
• 作者: Bozarth, Lia;Im, Jane;Quarles, Christopher;Budak, Ceren
• 通讯作者: Budak, Ceren

The Stability of Cable and Broadcast News Intermedia Agenda Setting Across the COVID-19 Issue Attention Cycle

COVID-19 问题关注周期内有线和广播新闻媒介议程设置的稳定性

• DOI: 10.1080/10584609.2023.2222382
• 发表时间: 2023
• 期刊: Political Communication
• 影响因子: 7.5
• 作者: Budak, Ceren;Jomini Stroud, Natalie;Muddiman, Ashley;Murray, Caroline C.;Kim, Yujin
• 通讯作者: Kim, Yujin