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: 2241068
• 负责人: Kai Shu
• 金额: $ 22.4万
• 依托单位: Illinois Institute of Technology
• 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=2241068&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瞬时通信。进入社交媒体的低障碍使更多的用户能够参与并使他们参与更长的时间，激励具有隐藏议程的个人在线传播虚假信息，以操纵信息和摇摆意见。虚假信息，例如虚假新闻，骗局和阴谋论，越来越多地成为对在线社交媒体的运作的障碍，作为可信赖信息的有效渠道。案件正在出现，故意捏造的虚假信息被武器化以划分人并产生有害的社会影响。因此，考虑到信息，安全性和免受虚假信息的保护之间的张力，必须了解虚假信息并系统地研究如何提高对它的抵抗力。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以告知决策。该项目涉及通过开发新知识和系统的计算框架来解决问题以应对检测在线虚假信息的主要（出处，数据和解释性）挑战，从而从根本上转变这一过程以打击虚假信息。 这些技术开发了跨学科理论和计算算法，以帮助决策者和社交媒体用户解决虚假信息。该项目结果有助于提高有关虚假信息的最新研究，并引入基于样式的和基于图形的优化方法，这些方法可以确定虚假信息及其特征及其特征的来源，虚假信息检测方法，需要最小数据或监督，通过利用多模态数据以及依靠高级社交上下文关系，并构成人类的高度检测技术，并启用人类的精神和构成技术，并启用了良好的检测技术，并构成了良好的检测技术，并构成了良好的方法，并认识良好的and soccoces and copsoccon and copsoccoption and socess consocipt 解释。从更广泛的角度来看，该项目有助于数据挖掘，机器学习，图形挖掘和文本挖掘研究以及有关信誉，透明度和减轻虚假信息缓解的沟通和新闻业的社会科学研究。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛的影响来评估的支持，并被认为是值得的。

项目成果

Attacking Fake News Detectors via Manipulating News Social Engagement

• DOI: 10.1145/3543507.3583868
• 发表时间: 2023-02
• 期刊: Proceedings of the ACM Web Conference 2023
• 作者: Haoran Wang;Yingtong Dou;Canyu Chen;Lichao Sun;Philip S. Yu;Kai Shu

MUSER: A MUlti-Step Evidence Retrieval Enhancement Framework for Fake News Detection

• DOI: 10.1145/3580305.3599873
• 发表时间: 2023-06
• 期刊: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
• 作者: Hao Liao;Jiaohao Peng;Zhanyi Huang;Wei Zhang;Guang‐hua Li;Kai Shu;Xingyu Xie

Explainable Claim Verification via Knowledge-Grounded Reasoning with Large Language Models

• DOI: 10.48550/arxiv.2310.05253
• 发表时间: 2023-10
• 期刊: ArXiv
• 作者: Haoran Wang;Kai Shu

PromptDA: Label-guided Data Augmentation for Prompt-based Few Shot Learners

PromptDA：针对基于提示的少镜头学习者的标签引导数据增强

• DOI: 10.18653/v1/2023.eacl-main.41
• 发表时间: 2023
• 期刊: Association for Computational Linguistics
• 作者: Chen, Canyu;Shu, Kai
• 通讯作者: Shu, Kai