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

协作研究：SaTC：核心：小型：针对计算虚假信息研究中的挑战以增强归因、检测和解释

• 批准号: 2241069
• 负责人: Miriam Metzger
• 金额: $ 15.6万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-01 至 2026-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2241069&HistoricalAwards=false
• 关键词: Collaborative; Research; SaTC; CORE; Small

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.

社交媒体的使用加速了信息共享和即时通信。进入社交媒体的门槛较低，使更多用户能够参与并保持更长时间的参与，从而激励具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。假新闻、恶作剧和阴谋论等虚假信息日益成为在线社交媒体作为获取可信信息的有效渠道发挥作用的障碍。故意捏造的虚假信息被用作武器来分裂人民并造成有害的社会影响的案例不断出现。因此，考虑到信息安全需求与虚假信息防护之间的紧张关系，有必要了解虚假信息，并系统地研究如何提高对虚假信息的抵抗力。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归因、检测和解释虚假信息，为决策提供信息。该项目涉及通过开发新知识和系统计算框架从根本上改变打击虚假信息的流程，以解决检测在线虚假信息的主要（来源、数据和可解释性）挑战。 开发的技术结合了跨学科理论和计算算法，帮助政策制定者和社交媒体用户解决虚假信息。该项目成果有助于推进关于虚假信息的最先进研究，并引入基于风格和基于图形的优化方法，可以确定虚假信息的来源及其特征，通过利用多模态数据和需要最少数据或监督的虚假信息检测方法。高级社会情境关系和可解释的检测技术依赖于完善的心理学和认知理论，并使人类互动能够增强检测和解释。更广泛地说，该项目为数据挖掘、机器学习、图形挖掘和文本挖掘研究以及传播和新闻领域的可信度、透明度和虚假信息缓解方面的社会科学研究做出了贡献。该奖项反映了 NSF 的法定使命，并被认为是值得的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来提供支持。