KEEN - Knowledge-driven Explainable Misinformation Detection for Trustworthy Computational Social Systems

KEEN - 知识驱动的可解释错误信息检测，用于可信赖的计算社会系统

• 批准号: EP/Y015894/1
• 负责人: Geyong Min
• 金额: $ 25.55万
• 依托单位: UNIVERSITY OF EXETER
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY015894%2F1
• 关键词: KEEN; Knowledge; driven; Explainable; Misinformation

With the prosperity of social media platforms like Facebook and Twitter, misinformation can be disseminated widely among the general public, causing a severe threat to the trustworthiness of computational social systems. To address this critical issue, various misinformation detection models have been proposed recently. However, the existing methods either use black-box deep learning (DL) models which cannot provide explainability of detection results, or leverage shallow experience-based explainable models which leads to low detection accuracy.This project aims to create a novel knowledge-driven approach to build both accurate and explainable misinformation detection models for trustworthy computational social systems. To this end, I will first establish a novel knowledge-driven integration mechanism to seamlessly integrate social psychological theories with DL models based on multi-modal social media data. Secondly, a novel explanation scheme will be developed to effectively convey social psychological theories into reliable model explainability through knowledge extraction. Thirdly, an accurate and explainable DL framework will be constructed base on hybrid DL models and hierarchical attention-based explanation. Finally, a prototype system will be developed to implement the proposed solutions and evaluate their performance. The scientific breakthroughs to be made in this project will contribute to provide the effective design of accurate and explainable misinformation detection models. The originality of this project lies in its interdisciplinary research on how to establish an innovative explainable DL approach for trustworthy computational social systems. A series of well-arranged research, training, knowledge transfer, and open science activities are planned to accomplish the ambitious aim of this project, facilitate knowledge transfer and dissemination, and enhance my creative and innovative potential and careerprospects with new skills and competences.

随着Facebook和Twitter等社交媒体平台的繁荣，错误信息可以在公众中广泛传播，对计算社会系统的可信度造成严重威胁。为了解决这个关键问题，最近提出了各种错误信息检测模型。然而，现有的方法要么使用黑盒深度学习（DL）模型，无法提供检测结果的可解释性，要么利用基于浅层经验的可解释模型，导致检测精度较低。该项目旨在创建一种新颖的知识驱动方法为值得信赖的计算社会系统构建准确且可解释的错误信息检测模型。为此，我将首先建立一种新颖的知识驱动集成机制，将社会心理学理论与基于多模态社交媒体数据的深度学习模型无缝集成。其次，将开发一种新颖的解释方案，通过知识提取有效地将社会心理学理论转化为可靠的模型可解释性。第三，基于混合深度学习模型和基于注意力的分层解释，构建准确且可解释的深度学习框架。最后，将开发一个原型系统来实施所提出的解决方案并评估其性能。该项目将取得的科学突破将有助于提供准确且可解释的错误信息检测模型的有效设计。该项目的独创性在于其跨学科研究，即如何为可信赖的计算社会系统建立创新的可解释的深度学习方法。计划开展一系列精心安排的研究、培训、知识转移和开放科学活动，以实现该项目的宏伟目标，促进知识转移和传播，并通过新的技能和能力增强我的创造和创新潜力和职业前景。