CAREER: Socio-Algorithmic Foundations of Trustworthy Recommendations

社交媒体现在是大多数美国人和全球数十亿人的主要新闻来源。这些平台采用的推荐算法旨在通过寻找吸引人的内容来最大限度地提高浏览量和点击量，而这反过来又往往会放大来自可疑新闻来源、阴谋论和未经证实的谣言的故事。老年人越来越多地转向社交媒体，作为一种保持信息灵通的方式，特别容易受到网上错误信息的影响。这种情况对党派新闻消费者来说尤其令人担忧，他们倾向于与符合他们信仰的信息接触，而不管其准确性如何。这项研究的目的是建立一个模拟的社交媒体平台，使研究人员能够测试更强大的新闻推荐算法，旨在提高社交媒体用户的新闻消费，同时推荐与他们相关的内容。该项目将包括研究老年人如何在主要社交媒体平台上参与新闻，以及替代算法是否会提高他们的新闻消费质量。这个项目是基于这样一种观察，即按（预测或实现的）受欢迎程度对新闻进行排名，创造了一个自我维持的循环，优先考虑支持态度的信息，而不管其质量如何。为了打破这个循环，该项目测试了一个假设，即优先考虑在不同受众中产生参与的内容将提高推荐的可信度。该项目将探讨受众的异质性为新闻质量提供良好信号的相关维度。技术贡献将包括一套新的规则化技术，将新闻来源的受众多样性纳入内容推荐方法，定量评估不同的重新排名方法对新闻消费者，特别是老年消费者的信息饮食质量的影响，和一种新的实验方法，对反事实的排名与高生态有效性。这一奖项反映了NSF的法定使命，并已被视为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，

Social media are now the main source of news for the majority of Americans and for billions of people worldwide. The recommendation algorithms employed by these platforms are designed to maximize views and clicks by finding engaging content, which in turn often ends up amplifying stories from dubious news sources, conspiracy theories, and unverified rumors. Older adults are a demographic increasingly turning to social media as a way to stay informed and are especially vulnerable to online misinformation.This situation is especially concerning for partisan news consumers, who have a tendency to engage with information that conforms to their beliefs, regardless of its accuracy. The objective of this research is to build a simulated social media platform that will allow researchers to test more robust news recommendation algorithms designed to improve the news consumption of social media users while recommending content that is still relevant to them. The project will include studies of how older adults engage with the news on major social media platforms and whether alternative algorithms would improve the quality of their news consumption. This project is based on the observation that ranking the news by (either predicted or achieved) popularity creates a self-sustaining cycle that prioritizes pro-attitudinal information regardless of its quality. To break this loop, the project tests the hypothesis that prioritizing content that generates engagement in diverse audiences will improve the trustworthiness of recommendations. The project will explore the relevant dimensions under which the heterogeneity of an audience provides a good signal for news quality. The technical contributions will include a set of new regularization techniques to incorporate audience diversity of news sources into content recommendation methods, a quantitative evaluation of the effect of different re-ranking methods on the quality of the information diet of news consumers, especially older consumers, and a new experimental methodology on counterfactual ranking with high ecological validity.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.