NSF Convergence Accelerator Track F: A Disinformation Range to Improve User Awareness and Resilience to Online Disinformation

NSF 融合加速器轨道 F:提高用户对在线虚假信息的认识和抵御能力的虚假信息范围

基本信息

  • 批准号:
    2137871
  • 负责人:
  • 金额:
    $ 75万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2021
  • 资助国家:
    美国
  • 起止时间:
    2021-10-01 至 2023-09-30
  • 项目状态:
    已结题

项目摘要

The unprecedented spread of disinformation, false information intentionally created to manipulate public opinions, is the flip-side of the Internet’s promise of universal access and information democratization. The presence of false and/or misleading information in the media ecosystem erodes trust in legitimate sources of information and poses a significant threat to society. We posit that enhancing user awareness and building resilience are the keys to combating disinformation, as ‘inoculated’ users can form the first line of defense against the spread of corrupted and misleading information. The overarching goal of our Disinformation Range (DRange) project is the development of a research/educational platform with integrated digital tools, advanced pedagogical techniques, and timely materials to increase disinformation awareness and improve user resilience, so as to inoculate them against the impact of harmful disinformation, and further prevent its spread. DRange will facilitate the pursuit of high impact goals in three overarching categories: 1) developing flexible technologies and culturally responsive group learning activities to facilitate communal examination and discussion of false and misleading information and inauthentic online behaviors in safe and familiar settings; 2) conducting transdisciplinary research to advance our understanding of the impact of dis/misinformation; and 3) identifying and implementing preventive (‘immunization’) strategies and mitigation practices. DRange is envisioned as a comprehensive learning process that interweaves facilitated discussions, collaborative games, and group activities, supported by a flexible and adaptable technical platform that uses simulated (or de-toxed) disinformation to both encourage critical conversations about online risks and vulnerabilities, and cultivate user resilience. DRange will be designed, developed and structured in collaboration with community partners to foster group interactions in diverse settings (e.g., classrooms, after school activities, public libraries, summer camps, senior and community centers, etc.).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.
虚假信息的史无前例的传播--故意制造虚假信息以操纵公众舆论--是互联网普及和信息民主化承诺的另一面。媒体生态系统中虚假和/或误导性信息的存在侵蚀了人们对合法信息来源的信任,并对社会构成重大威胁。我们假设,提高用户意识和建立韧性是打击虚假信息的关键,因为接种疫苗的用户可以形成防止腐败和误导性信息传播的第一道防线。我们的虚假信息范围(Drange)项目的总体目标是开发一个研究/教育平台,包括集成的数字工具、先进的教学技术和及时的材料,以提高虚假信息意识和提高用户的应变能力,从而使他们免受有害虚假信息的影响,并进一步防止其传播。Drange将促进在三个主要类别中追求高影响力的目标:1)开发灵活的技术和响应文化的小组学习活动,以促进在安全和熟悉的环境中对虚假和误导性信息以及不真实的在线行为进行集体检查和讨论;2)进行跨学科研究,以促进我们对信息不实/错误信息的影响的了解;以及3)确定和实施预防性(免疫)战略和缓解做法。Drange被设想为一个全面的学习过程,将促进的讨论、协作游戏和小组活动交织在一起,并得到灵活和适应性强的技术平台的支持,该平台使用模拟(或去除)虚假信息来鼓励关于在线风险和漏洞的批判性对话,并培养用户的应变能力。Drange将与社区合作伙伴合作设计、开发和构建,以促进在不同环境(例如,教室、课后活动、公共图书馆、夏令营、老年人和社区中心等)中的群体互动。该奖项反映了NSF的法定使命,并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,被认为值得支持。

项目成果

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Siwei Lyu其他文献

Countering JPEG anti-forensics based on noise level estimation
基于噪声水平估计的 JPEG 反取证对抗
  • DOI:
    10.1007/s11432-016-0426-1
  • 发表时间:
    2017-08
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Hui Zeng;Xiangui Kang;Jingjing Yu;Siwei Lyu
  • 通讯作者:
    Siwei Lyu
Online Deformable Object Tracking Based on Structure-Aware Hyper-Graph
基于结构感知超图的在线变形目标跟踪
  • DOI:
    10.1109/tip.2016.2570556
  • 发表时间:
    2016-08
  • 期刊:
  • 影响因子:
    10.6
  • 作者:
    Dawei Du;Honggang Qi;Wenbo Li;Longyin Wen;Qingming Huang;Siwei Lyu
  • 通讯作者:
    Siwei Lyu
Deep Constrained Low-Rank Subspace Learning for Multi-View Semi-Supervised Classification
用于多视图半监督分类的深度约束低秩子空间学习
  • DOI:
    10.1109/lsp.2019.2923857
  • 发表时间:
    2019
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Zhe Xue;Junping Du;Dawei Du;Guorong Li;Qingming Huang;Siwei Lyu
  • 通讯作者:
    Siwei Lyu
Vertebral artery course variation leading to an insufficient proximal anchoring area for thoracic endovascular aortic repair.
椎动脉走行变化导致胸主动脉腔内修复的近端锚固区域不足。
  • DOI:
    10.1177/17085381221140319
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    1.1
  • 作者:
    Zuanbiao Yu;Siwei Lyu;Dehai Lang;Di Wang;Songjie Hu;Xiaoliang Yin;Yunpeng Ding;Chunbo Xu;Chen Lin;Jiangnan Hu
  • 通讯作者:
    Jiangnan Hu
Unifying Non-Maximum Likelihood Learning Objectives with Minimum KL Contraction
将非最大似然学习目标与最小 KL 收缩统一起来

Siwei Lyu的其他文献

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{{ truncateString('Siwei Lyu', 18)}}的其他基金

SaTC: CORE: Small: Combating AI Synthesized Media Beyond Detection
SaTC:核心:小型:对抗无法检测的人工智能合成媒体
  • 批准号:
    2153112
  • 财政年份:
    2022
  • 资助金额:
    $ 75万
  • 项目类别:
    Standard Grant
NSF Convergence Accelerator Track F: Online Deception Awareness and Resilience Training (DART)
NSF 融合加速器轨道 F:在线欺骗意识和弹性培训 (DART)
  • 批准号:
    2230494
  • 财政年份:
    2022
  • 资助金额:
    $ 75万
  • 项目类别:
    Cooperative Agreement
RI: Small: A Study of New Aggregate Losses for Machine Learning
RI:小:机器学习新总损失的研究
  • 批准号:
    2008532
  • 财政年份:
    2020
  • 资助金额:
    $ 75万
  • 项目类别:
    Standard Grant
RI: Small: A Study of New Aggregate Losses for Machine Learning
RI:小:机器学习新总损失的研究
  • 批准号:
    2103450
  • 财政年份:
    2020
  • 资助金额:
    $ 75万
  • 项目类别:
    Standard Grant
NRI: Collaborative Research: A Dynamic Bayesian Approach to Real Time Estimation and Filtering in Grasp Acquisition and Other Contact Tasks (Continuation)
NRI:协作研究:抓取采集和其他接触任务中实时估计和过滤的动态贝叶斯方法(续)
  • 批准号:
    1537257
  • 财政年份:
    2015
  • 资助金额:
    $ 75万
  • 项目类别:
    Standard Grant
Blind Noise Estimation Using Signal Statistics in Random Band-Pass Domains
使用随机带通域中的信号统计进行盲噪声估计
  • 批准号:
    1319800
  • 财政年份:
    2013
  • 资助金额:
    $ 75万
  • 项目类别:
    Standard Grant
NRI-Small: Collaborative Research: A Dynamic Bayesian Approach to Real-Time Estimation and Filtering in Grasp Acquisition and Other Contact Tasks
NRI-Small:协作研究:在抓取采集和其他接触任务中进行实时估计和过滤的动态贝叶斯方法
  • 批准号:
    1208463
  • 财政年份:
    2012
  • 资助金额:
    $ 75万
  • 项目类别:
    Standard Grant
CAREER: A New Statistical Framework for Natural Images with Applications in Vision
职业:一种新的自然图像统计框架及其在视觉中的应用
  • 批准号:
    0953373
  • 财政年份:
    2010
  • 资助金额:
    $ 75万
  • 项目类别:
    Continuing Grant

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