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

基本信息

  • 批准号:
    EP/Y015894/1
  • 负责人:
  • 金额:
    $ 25.55万
  • 依托单位:
  • 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

项目摘要

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.
借助Facebook和Twitter等社交媒体平台的繁荣,可以在公众中广泛传播错误信息,从而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。但是,现有方法要么使用黑盒深度学习(DL)模型,该模型无法提供检测结果的解释性,要么利用基于经验的可解释模型,这会导致较低的检测准确性。该项目旨在创建一种新颖的知识驱动方法来构建准确且可解释的误解误导性检测模型,以实现可信赖的计算社会系统。为此,我将首先建立一种新颖的知识融合机制,以将社会心理理论与基于多模式社交媒体数据的DL模型无缝整合。其次,将制定一种新颖的解释计划,以有效地将社会心理理论传达到可靠的模型中,通过知识提取来解释。第三,将在混合DL模型和基于分层注意力的解释上构建一个准确且可解释的DL框架。最后,将开发一个原型系统来实施提出的解决方案并评估其性能。该项目要进行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DL方法。计划进行一系列良好的研究,培训,知识转移和开放科学活动,以实现该项目的雄心勃勃的目标,促进知识转移和传播,并以新的技能和能力来增强我的创造力和创新潜力以及职业生涯。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

暂无数据

数据更新时间:2024-06-01

Geyong Min其他文献

Performance analysis of an integrated scheduling scheme in the presence of bursty MMPP traffic
存在突发 MMPP 流量时集成调度方案的性能分析
On the Study of Sustainability and Outage of SWIPT-Enabled Wireless Communications
基于SWIPT的无线通信的可持续性和中断研究
Overcoming Occlusions: Perception Task-Oriented Information Sharing in Connected and Autonomous Vehicles
克服遮挡:联网和自动驾驶车辆中面向感知任务的信息共享
  • DOI:
  • 发表时间:
    2023
    2023
  • 期刊:
  • 影响因子:
    9.3
  • 作者:
    Zhu Xiao;Jinmei Shu;Hongbo Jiang;Geyong Min;Hongyang Chen;Zhu Han
    Zhu Xiao;Jinmei Shu;Hongbo Jiang;Geyong Min;Hongyang Chen;Zhu Han
  • 通讯作者:
    Zhu Han
    Zhu Han
A Light-Weight Statistical Latency Measurement Platform at Scale
轻量级大规模统计延迟测量平台
Cooperative Edge Caching Based on Temporal Convolutional Networks
基于时间卷积网络的协作边缘缓存
共 41 条
  • 1
  • 2
  • 3
  • 4
  • 5
  • 6
  • 9
前往

Geyong Min的其他基金

VIPAuto: Robust and Adaptive Visual Perception for Automated Vehicles in Complex Dynamic Scenes
VIPAuto:复杂动态场景中自动驾驶车辆的鲁棒自适应视觉感知
  • 批准号:
    EP/Y015878/1
    EP/Y015878/1
  • 财政年份:
    2024
  • 资助金额:
    $ 25.55万
    $ 25.55万
  • 项目类别:
    Fellowship
    Fellowship
RITA: Reliable and Efficient Task Management in Edge Computing for AIoT Systems
RITA:AIoT 系统边缘计算中可靠、高效的任务管理
  • 批准号:
    EP/Y015886/1
    EP/Y015886/1
  • 财政年份:
    2024
  • 资助金额:
    $ 25.55万
    $ 25.55万
  • 项目类别:
    Fellowship
    Fellowship
ASCENT: Autonomous Vehicular Edge Computing and Networking for Intelligent Transportation
ASCENT:智能交通的自主车辆边缘计算和网络
  • 批准号:
    EP/X038866/1
    EP/X038866/1
  • 财政年份:
    2023
  • 资助金额:
    $ 25.55万
    $ 25.55万
  • 项目类别:
    Research Grant
    Research Grant
Proposal for Support of the Keynote Speakers for the 10th IEEE International Conference on Computer and Information Technology (CIT-2010)
支持第十届 IEEE 计算机与信息技术国际会议 (CIT-2010) 主讲嘉宾的提案
  • 批准号:
    EP/I011676/1
    EP/I011676/1
  • 财政年份:
    2010
  • 资助金额:
    $ 25.55万
    $ 25.55万
  • 项目类别:
    Research Grant
    Research Grant

相似国自然基金

知识驱动的可解释性药物重定位方法及作用机制研究
  • 批准号:
    62373348
  • 批准年份:
    2023
  • 资助金额:
    50 万元
  • 项目类别:
    面上项目
知识和数据协同驱动的车联网关键技术研究
  • 批准号:
    62371309
  • 批准年份:
    2023
  • 资助金额:
    53 万元
  • 项目类别:
    面上项目
数据与知识双驱动的抗体分子智能设计方法研究
  • 批准号:
    62372204
  • 批准年份:
    2023
  • 资助金额:
    51 万元
  • 项目类别:
    面上项目
知识与数据混合驱动的含缺陷点阵结构不确定性分析与优化方法研究
  • 批准号:
    12302149
  • 批准年份:
    2023
  • 资助金额:
    30 万元
  • 项目类别:
    青年科学基金项目
数据-知识融合驱动的半导体硅单晶质量监控与批次学习控制方法研究
  • 批准号:
    62303376
  • 批准年份:
    2023
  • 资助金额:
    30 万元
  • 项目类别:
    青年科学基金项目

相似海外基金

Real-time inversion using self-explainable deep learning driven by expert knowledge
使用由专家知识驱动的可自我解释的深度学习进行实时反演
  • 批准号:
    EP/Z000653/1
    EP/Z000653/1
  • 财政年份:
    2024
  • 资助金额:
    $ 25.55万
    $ 25.55万
  • 项目类别:
    Research Grant
    Research Grant
CAREER: Timeliness as a Controllable Dimension via Knowledge-driven System Management
职业:通过知识驱动的系统管理将及时性作为可控维度
  • 批准号:
    2238476
    2238476
  • 财政年份:
    2023
  • 资助金额:
    $ 25.55万
    $ 25.55万
  • 项目类别:
    Continuing Grant
    Continuing Grant
EAGER: Development of a Hybrid Knowledge- and Data-Driven Approach to Guide the Design of Immunotherapeutic Cells
EAGER:开发混合知识和数据驱动的方法来指导免疫治疗细胞的设计
  • 批准号:
    2324742
    2324742
  • 财政年份:
    2023
  • 资助金额:
    $ 25.55万
    $ 25.55万
  • 项目类别:
    Continuing Grant
    Continuing Grant
A PROGRESS-Driven Approach to Cognitive Outcomes after Traumatic Brain Injury: Advancing Equity, Diversity, and Inclusion through Knowledge Synthesis and Mobilization
创伤性脑损伤后认知结果的进步驱动方法:通过知识合成和动员促进公平、多样性和包容性
  • 批准号:
    492338
    492338
  • 财政年份:
    2023
  • 资助金额:
    $ 25.55万
    $ 25.55万
  • 项目类别:
    Operating Grants
    Operating Grants
Accurate and Individualized Prediction of Excitation-Inhibition Imbalance in Alzheimer's Disease using Data-driven Neural Model
使用数据驱动的神经模型准确、个性化地预测阿尔茨海默病的兴奋抑制失衡
  • 批准号:
    10727356
    10727356
  • 财政年份:
    2023
  • 资助金额:
    $ 25.55万
    $ 25.55万
  • 项目类别: