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
- 来源:
- 关键词:
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.
虚假的诺言的前所未有的诺言的承诺在媒体生态系统中存在虚假和误导性的信息,这是在合法的信息来源中,并对社会构成了重大威胁。钥匙墓葬是“接种”的用户可以形成我们的防御范围(Drange)项目的第一线防御能力。弹性,以便对充实的虚假信息的影响,进一步阻止其传播。 )进行跨学科的研究以提高我们对DIS/误解的影响(“免疫”)策略和发明的实践。和适应性的平台,这些平台使用模拟的(或反对性的)GE关于在线风险和漏洞的关键对话,并与社区合作伙伴合作,以培养各种环境中的互动营地,高级社区中心等。标准。
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)
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Siwei Lyu其他文献
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
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
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
An implicit Markov random field model for the multi-scale oriented representations of natural images
- DOI:
10.1109/cvpr.2009.5206797 - 发表时间:
2009-06 - 期刊:
- 影响因子:0
- 作者:
Siwei Lyu - 通讯作者:
Siwei Lyu
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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