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

打击数字时代的错误信息一直是一个具有重大社会影响的具有挑战性的主题，因为错误信息继续影响着从选举到对流行病的反应等当代有争议的事件。尽管经过了几十年的研究，但错误信息仍然是一个严重的威胁，因为大多数技术缓解方法侧重于提高检测精度，而没有考虑社会和情感角度。该项目通过识别有影响的社区、代理人和文化共鸣信息来帮助增强信息完整性，以确定有争议问题的公共对话的引爆点，并提供大规模以用户为中心的干预措施。该项目从以来源为中心的准确性检测和揭穿转向专注于以用户为中心的干预措施，该干预措施整合了心理和社会文化结构、计算理论和机器学习(ML)算法，以形成用于测试的干预措施的原型。研究的第一个重点是分析和识别社会规范的出现--社区的共同信念或可接受的行为，以及当信念即将迅速改变时的临界点。这项研究的第二个重点是揭示信仰的文化背景，个性化到每个人，以优化在线网络传播中科学证据的接受度。第三个支柱(交互)提供了人在回路中的视觉分析框架，以支持用户验证和做出用户自己关于他们的信仰的决定。支持这项工作的是基于拓扑ML的新型深度学习模型的开发和测试，该模型有效地预测异质社会规范的出现，以便及时干预，识别最受信任的参与特征，以及用于与用户透明交互的时间可解释人工智能。波因特研究所等领先的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的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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 optimzie 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 belief. 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.