Collaborative Research: SaTC: CORE: Small: Targeting Challenges in Computational Disinformation Research to Enhance Attribution, Detection, and Explanation

社交媒体的使用加速了信息共享和即时通信。进入社交媒体的低门槛使更多的用户能够参与其中，并使他们参与的时间更长，这激励了有隐藏议程的个人在网上传播虚假信息，以操纵信息和影响舆论。虚假信息，如假新闻、骗局和阴谋论，日益成为在线社交媒体作为可靠信息有效渠道发挥作用的障碍。故意编造的虚假信息被用作武器，以分裂人民并造成有害的社会影响，这种情况正在出现。因此，考虑到信息需求与安全以及防范虚假信息之间的紧张关系，了解虚假信息并系统地研究如何提高对虚假信息的抵抗力是势在必行的。该项目旨在研究虚假信息的科学基础，并开发一个计算框架来确定、检测和解释虚假信息，从而为政策制定提供信息。该项目涉及通过开发新知识和系统计算框架来解决检测在线虚假信息的主要（来源、数据和可解释性）挑战，从根本上改变打击虚假信息的过程。所开发的技术结合了跨学科理论和计算算法，以帮助政策制定者和社交媒体用户处理虚假信息。项目成果有助于推进最先进的虚假信息研究，并引入基于风格和基于图形的优化方法，这些方法可以确定虚假信息的来源及其特征，虚假信息检测方法需要最少的数据或通过利用多模态数据和高水平的社会背景关系进行监督，以及依赖于完善的心理学和认知理论的可解释检测技术。并使人类互动能够加强检测和解释。更广泛地说，该项目有助于数据挖掘、机器学习、图挖掘和文本挖掘研究，以及传播和新闻领域关于可信度、透明度和虚假信息缓解的社会科学研究。该奖项反映了美国国家科学基金会的法定使命，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。

The use of social media has accelerated information sharing and instantaneous communications. The low barrier to entering social media enables more users to participate and keeps them engaged longer, incentivizing individuals with a hidden agenda to spread disinformation online to manipulate information and sway opinion. Disinformation, such as fake news, hoaxes, and conspiracy theories, has increasingly become a hindrance to the functioning of online social media as an effective channel for trustworthy information. Cases are emerging where deliberately fabricated disinformation is weaponized to divide people and create detrimental societal effects. Therefore, it is imperative to understand disinformation and systematically investigate how to improve resistance against it, considering the tension between the need for information and security and protection from disinformation. The project aims to study the scientific underpinnings of disinformation and develop a computational framework to attribute, detect, and explain disinformation to inform policymaking. The project involves fundamentally transforming the process to combat disinformation by developing new knowledge and a systematic computational framework to address major (provenance, data, and explanaibility) challenges of detecting online disinformation. The techniques developed combine interdisciplinary theories and computational algorithms to help policymakers and social media users address disinformation. The project outcomes help advance state-of-the-art research on disinformation and introduce style-based and graph-based optimization methods that can determine the source of disinformation and its characteristics, disinformation detection methods requiring minimal data or supervision by harnessing multimodal data and high-level social context relations, and interpretable detection techniques that rely on well-established psychological and cognitive theories, and enable human interactions to enhance detection and explanation. More broadly, the project contributes to data mining, machine learning, graph mining, and text mining research as well social science research in communication and journalism on credibility, transparency, and disinformation mitigation.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.