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

在数字时代打击错误信息一直是一个具有挑战性的主题，具有重大的社会影响，因为错误信息继续影响有争议的当代事件，从选举到应对流行病。尽管经过数十年的研究，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都专注于提高检测准确性，而没有考虑社会和情感的角度。该项目通过识别有影响力的社区、代理人和文化共鸣的信息来帮助提高信息的完整性，以确定有争议问题的公共对话的临界点，并提供以用户为中心的干预场所。该项目从以来源为中心的准确性检测和揭穿转向以用户为中心的干预，将心理和社会文化结构、计算理论、和机器学习（ML）算法来对干预措施进行原型化测试。研究的第一个重点是分析和确定社会规范的出现-共同的信念或社区可接受的行为，以及信念即将迅速改变时的临界点。本研究的第二个重点是揭示信仰的文化背景，个性化的每一个人，以优化在线网络传播中的科学证据的接受度。第三个支柱（交互）提供了人在环视觉分析框架，以支持用户验证和做出用户自己的决定，他们相信什么。这项工作的基础是基于拓扑ML的新型深度学习模型的开发和测试，这些模型可以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，以及用于与用户进行透明交互的时间可解释人工智能。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。

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.