NSF Convergence Accelerator Track F: How Large-Scale Identification and Intervention Can Empower Professional Fact-Checkers to Improve Democracy and Public Health

美国的民主和公共卫生依赖于对机构的信任。对美国选举诚信的怀疑和对COVID-19疫苗的犹豫是对基本政治进程和核心医疗机构信心下降的两个后果。社交媒体是有关选举和疫苗的非法信息的主要来源，用户网络积极传播对选举诚信和疫苗有效性的怀疑，助长了错误信息的传播。该项目旨在支持和授权记者，开发商和公民的努力，以核实此类错误信息。他们迫切需要这样的工具：1）能够在Twitter、Reddit和Facebook等社交媒体平台上对选举和疫苗等主题的事实核查故事进行测试; 2）能够在真实的时间内提供关于更正效果的反馈，并具有完全的性能透明度。因此，该项目将开发一个互动系统，使事实核查人员能够对事实核查信息进行快速循环测试，并监测其在有可能出现错误信息的在线社区中的实时表现。为了透明，所有的基本代码、调查和数据都将与社会科学和计算机科学社区共享，所有对公共卫生专业人员和选举管理人员有直接效用的循证信息都将公开。该项目的动机是希望了解并帮助解决美国面临的两个民主和公共卫生危机：对美国选举诚信的怀疑和对新型冠状病毒疫苗的犹豫。这两次危机都是由社交媒体上广泛传播的在线错误信息引发的，用户网络积极传播对选举诚信和疫苗有效性的怀疑。 该项目将提供一种创新的三步法，以识别，测试和纠正这些形式的在线错误信息的现实世界的实例。首先，使用计算手段，如自然语言处理、机器学习、社交网络分析和建模以及计算机视觉技术，以识别流通和易受错误信息影响的帖子和账户。第二，将使用推荐系统对最突出的误导性声明形式进行实验室测试，以优化信息功效。第三，该项目将通过平台赞助的内容系统，利用各种可扩展的干预技术，传播和评估循证矫正的有效性。更具体地说，在该方法的第一步，该项目将使用多模态信号检测和知识图来进行关于社交媒体上的选举怀疑和疫苗犹豫的知识驱动信息提取，整合用户属性，消息特征和在线网络结构属性，以预测未来可能暴露于错误信息的风险，并识别易受影响的在线社区进行干预。第二步将包括与专业的事实核查组织合作，对两种类型的干预信息进行实验室测试-暴露前接种和暴露后纠正-旨在减轻选举怀疑和疫苗犹豫，使用推荐系统技术优化它们。 第三步，将进行实地实验，部署实验室开发的干预措施，通过广告购买，自动机器人和在线影响者的组合提供;并评估我们的干预措施在健康和民主相关领域的最佳决策方面的成功。 最终，这三步方法可以应用于政治和健康领域的一系列主题。该奖项反映了NSF的法定使命，并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，被认为值得支持。

Democracy and public health in the United States rely on trust in institutions. Skepticism regarding the integrity of U.S. elections and hesitancy related to COVID-19 vaccines are two consequences of a decline in confidence in basic political processes and core medical institutions. Social media serve as a major source of delegitimizing information about elections and vaccines, with networks of users actively sowing doubts about election integrity and vaccine efficacy, fueling the spread of misinformation. This project seeks to support and empower efforts by journalists, developers, and citizens to fact-check such misinformation. They urgently need tools that can 1) enable testing of fact-checking stories on topics like elections and vaccines as they move across social media platforms like Twitter, Reddit, and Facebook, and 2) deliver feedback on how well the corrections worked in real time and with full performance transparency. Accordingly, this project will develop an interactive system that enables fact-checkers to perform rapid-cycle testing of fact-checking messages and monitor their real-time performance among online communities at-risk of misinformation exposure. To be transparent, all of the underlying code, surveys, and data will be available to share with the social science and computer science communities, and all evidence-based messages of immediate utility to public health professionals and electoral administrators will be made publicly accessible.This project is motivated by a desire to understand and help address two democratic and public health crises facing the U.S.: skepticism regarding the integrity of U.S. elections and hesitancy related to COVID-19 vaccines. Both of these crises are fueled by online misinformation, widely circulating on social media, with networks of users actively sowing doubts about election integrity and vaccine efficacy. The project will deliver an innovative, three-step method to identify, test, and correct real-world instances of these forms of online misinformation. First, using computational means, such as techniques in natural language processing, machine learning, social network analysis and modeling, and computer vision to identify posts and accounts circulating and susceptible to misinformation. Second, lab-tested corrections to the most prominent forms of misinforming claims using recommender systems to optimize message efficacy will be produced. And third, the project will disseminate and evaluate the effectiveness of evidence-based corrections using various scalable intervention techniques available through the platforms sponsored content systems. More specifically, for the first step of the method, the project will use multimodal signal detection and knowledge graph to engage in knowledge driven information extraction about electoral skepticism and vaccine hesitancy on social media, integrating user attributes, message features, and online network structural properties to predict likely exposure to future misinformation and identify susceptible online communities for intervention. The second step will consist of working with professional fact-checking organizations to lab test two types of intervention messages—pre-exposure inoculation and post-exposure correction— aimed at mitigating electoral skepticism and vaccine hesitancy, optimizing them using recommender system techniques. For the third step, field experiments will be conducted that deploy the lab-developed interventions, delivered through a combination of ad-purchasing, automated bots, and online influencers; and assess the success of our interventions with respect to optimal decision-making in both health and democracy-related arenas. Ultimately, this three-step approach can be applied across a range of topics in politics and health.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.