EAGER: SaTC: Early-Stage Interdisciplinary Collaboration: Collaborative: Advances in Socio-Algorithmic Information Diversity

EAGER：SaTC：早期跨学科合作：协作：社会算法信息多样性的进展

• 批准号: 1915837
• 负责人: Brendan Nyhan
• 金额: $ 15万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-01 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1915837&HistoricalAwards=false
• 关键词: EAGER; SaTC; Early; Stage; Interdisciplinary

Social media now play an important role in exposing people to information about a wide range of topics ranging from entertainment to hard news and political debate. What can be seen on these platforms is heavily influenced by algorithms that are designed to select the most engaging and relevant content for each user. By seeking to maximize engagement, these algorithms may inadvertently amplify factually dubious or poor quality information that reinforces users' existing beliefs. In doing so, these algorithms could reduce the diversity of information to which users are exposed. This project will develop new content recommendation algorithms that reduce this risk and improve the quality and diversity of information circulating on social media.This research will develop an understanding of how coupled cyber-human systems process information in the context of news consumption on social media. This context creates important information-processing vulnerabilities at the social, behavioral, cognitive, and algorithmic levels. Using data from a nationally representative sample of the U.S. population, investigators will measure the association between political attitudes, readership, engagement, and information quality. They will also test the effect of behavioral nudges designed to promote the consumption of diverse information in a browser extension/smartphone app. Finally, the researchers will develop a generic modeling framework to evaluate the effect of these recommendations on audience-slant diversification and to test their robustness against fraudulent (shilling) attacks.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.

现在，社交媒体在使人们了解有关娱乐到艰难新闻和政治辩论的广泛主题的信息方面发挥了重要作用。在这些平台上可以看到的内容受到算法的严重影响，这些算法旨在为每个用户选择最吸引人和最相关的内容。通过寻求最大限度地提高参与度，这些算法可能会无意中放大实际上可疑或质量差的信息，从而增强了用户现有的信念。这样，这些算法可以减少用户暴露的信息的多样性。该项目将开发新的内容推荐算法，以降低这种风险并提高社交媒体传播的信息的质量和多样性。这项研究将对在社交媒体上新闻消费的背景下如何耦合网络人类系统处理信息。此上下文在社会，行为，认知和算法层面上创造了重要的信息处理漏洞。使用来自美国人口的全国代表性样本的数据，研究人员将衡量政治态度，读者，参与和信息质量之间的关联。他们还将测试旨在在浏览器扩展/智能手机应用程序中促进各种信息消费的行为推动力的效果。最后，研究人员将开发一个通用的建模框架，以评估这些建议对受众范围的多样化的影响，并测试其对欺诈性（先令）攻击的鲁棒性。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的审查审查标准来通过评估来通过评估来获得支持的。