Collaborative Research: SaTC: CORE: Medium: Information Integrity: A User-centric Intervention

协作研究：SaTC：核心：媒介：信息完整性：以用户为中心的干预

• 批准号: 2323794
• 负责人: My Thai
• 金额: $ 74.4万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2323794&HistoricalAwards=false
• 关键词: Collaborative; Research; SaTC; CORE; Medium

Combating misinformation in the digital age has been a challenging subject with significant social implications, as misinformation continues to impact contentious contemporary events from elections to responses to pandemics. Despite decades of research, misinformation remains a serious threat as most technical mitigation methods focus on improving detection accuracy and fail to consider social and emotional perspectives. This project assists in enhancing information integrity by identifying influencing communities, agents, and culturally resonant information to identify tipping points in public dialogue on controversial issues and offering venues of user-centric interventions at scale.This project moves away from source-centric accuracy detection and debunking to focus on user-centric interventions that integrates psychological and socio-cultural constructs, computational theories, and machine learning (ML) algorithms to prototype interventions for testing. The first focus of research has the goal of analyzing and identifying social norm emergence--the shared beliefs or acceptable behaviors of communities, and tipping points when beliefs are about to change rapidly. The second focus of this research is to uncover the cultural contexts of belief, personalized to each individual, to optimize the receptivity of scientific evidence in online network dissemination. The third pillar (Interaction) provides human-in-the-loop visual analytics framework to support users in verifying and making users' own decisions as to what they believe. Underpinning this work is the development and testing of novel deep learning models based on topology ML, which effectively predict heterogeneous social norm emergence for timely intervention, identify top trusted features for engagement, and temporal explainable artificial intelligence for transparent interaction with users. The involvement of leading misinformation mitigation and journalism education organizations such as the Poynter Institute helps to ensure social impacts in the field.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.

在数字时代，打击错误的信息一直是一个充满挑战的主题，具有重大的社会影响，因为错误信息继续影响从选举到对大流行的回应的当代事件。尽管进行了数十年的研究，但错误的信息仍然是一个严重的威胁，因为大多数技术缓解方法都集中在提高检测准确性，而无法考虑社会和情感观点。该项目通过识别影响社区，推动者和文化共鸣的信息来协助增强信息完整性，以在公众对话中确定有关有争议的问题的临界点，并在规模上提供以用户为中心的干预措施的场所。该项目从源中心的准确性检测和揭露，从用于测试原型干预措施的算法。研究的第一个重点是分析和识别社会规范出现的目标 - 社区的共同信念或可接受的行为，以及当信仰即将迅速改变时的转折点。这项研究的第二个重点是揭示对每个人的个性化信仰的文化背景，以优化在线网络传播中科学证据的接受能力。第三个支柱（互动）提供了人类的视觉分析框架，以支持用户验证和做出对用户对自己的信念的决定。为这项工作支撑的是基于拓扑ML的新型深度学习模型的开发和测试，该模型有效地预测了及时干预的异质社会规范出现，确定了可信赖的最信任的特征，以及可解释的时间范围的人工智能，以与用户透明相互作用。诸如Poynter Institute之类的领先错误信息缓解和新闻教育组织的参与有助于确保该领域的社会影响。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智力优点和更广泛的影响来通过评估来支持的。