Collaborative Research: SaTC: CORE: Large: Multi-Disciplinary Analyses of the Nature and Spread of Unsubstantiated Information Online
协作研究:SaTC:核心:大型:对未经证实的在线信息的性质和传播进行多学科分析
基本信息
- 批准号:2123635
- 负责人:
- 金额:$ 161.53万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-07-15 至 2024-06-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Americans need reliable information to make decisions that can improve national security, public health, community resilience, economic competitiveness, and quality of life. Online media platforms make it possible for misinformation on these topics to spread with unprecedented speed and scale. To counter the deleterious effects of misinformation, it is important to understand how it spreads and the conditions under which people believe it. This interdisciplinary project examines these dynamics in ways that have the potential to help people make better decisions about what kinds of information will help them make better decisions.The research design focuses on how different ways of presenting misinformation affect people’s attitudes, beliefs, and social identities. The design will show how psychological predispositions, moral values, sociocultural attitudes, social identity, group affinities and other factors influence who believes misinformation -- and who rejects or ignores it. The project also examines how network structures shape where and how false beliefs spread. The project’s research will be broadly disseminated to help scholars, policy experts, and social media designers create more contexts that empower more people to base their decisions on accurate facts.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.
美国人需要可靠的信息来做出可以改善国家安全、公共卫生、社区复原力、经济竞争力和生活质量的决策,从而使有关这些主题的错误信息以前所未有的速度和规模传播。为了了解错误信息的影响,了解错误信息的传播方式以及人们在何种条件下相信错误信息非常重要。这个跨学科项目以有可能帮助人们做出更好决策的方式来研究这些动态,了解哪些信息可以帮助他们做出更好的决策。 .研究设计的重点是不同的方式如何该设计将展示心理倾向、道德价值观、社会文化态度、社会认同、群体亲和力和其他因素如何影响谁相信错误信息以及谁拒绝或忽视它。研究网络结构如何影响错误信念的传播地点和方式。该项目的研究将被广泛传播,以帮助学者、政策专家和社交媒体设计者创造更多环境,使更多人能够根据准确的事实做出决定。通过使用基金会的智力价值和更广泛的影响审查标准进行评估,NSF 的法定使命被认为值得支持。
项目成果
期刊论文数量(7)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
How “Loco” Is the LOCO Corpus? Annotating the Language of Conspiracy Theories
LOCO 语料库如何“Loco”?
- DOI:
- 发表时间:2022
- 期刊:
- 影响因子:0
- 作者:Mompelat, Ludovic;Tian, Zuoyu;Kessler, Amanda;Luettgen, Matthew;Rajanala, Aaryana;Kübler, Sandra;Seelig, Michelle
- 通讯作者:Seelig, Michelle
Have beliefs in conspiracy theories increased over time?
- DOI:10.1371/journal.pone.0270429
- 发表时间:2022
- 期刊:
- 影响因子:3.7
- 作者:Uscinski, Joseph;Enders, Adam;Klofstad, Casey;Seelig, Michelle;Drochon, Hugo;Premaratne, Kamal;Murthi, Manohar
- 通讯作者:Murthi, Manohar
Are Republicans and Conservatives More Likely to Believe Conspiracy Theories?
- DOI:10.1007/s11109-022-09812-3
- 发表时间:2022-07-22
- 期刊:
- 影响因子:3.9
- 作者:Enders, Adam;Farhart, Christina;Miller, Joanne;Uscinski, Joseph;Saunders, Kyle;Drochon, Hugo
- 通讯作者:Drochon, Hugo
How Anti-Social Personality Traits and Anti-Establishment Views Promote Beliefs in Election Fraud, QAnon, and COVID-19 Conspiracy Theories and Misinformation
- DOI:10.1177/1532673x221139434
- 发表时间:2022-11-10
- 期刊:
- 影响因子:1.5
- 作者:Enders, Adam;Klofstad, Casey;Uscinski, Joseph E.
- 通讯作者:Uscinski, Joseph E.
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Manohar Narayanamurthi其他文献
Manohar Narayanamurthi的其他文献
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{{ truncateString('Manohar Narayanamurthi', 18)}}的其他基金
NeTS-NBD: Illuminating Congestion Control: Analytical Guidance and Practical Implementations
NeTS-NBD:阐明拥塞控制:分析指导和实际实施
- 批准号:
0519933 - 财政年份:2005
- 资助金额:
$ 161.53万 - 项目类别:
Standard Grant
CAREER: Agile Speech and Audio Coding and Transmission over Heterogeneous Networks
职业:异构网络上的敏捷语音和音频编码和传输
- 批准号:
0347229 - 财政年份:2004
- 资助金额:
$ 161.53万 - 项目类别:
Continuing Grant
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相似海外基金
Collaborative Research: SaTC: CORE: Medium: Differentially Private SQL with flexible privacy modeling, machine-checked system design, and accuracy optimization
协作研究:SaTC:核心:中:具有灵活隐私建模、机器检查系统设计和准确性优化的差异化私有 SQL
- 批准号:
2317232 - 财政年份:2024
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$ 161.53万 - 项目类别:
Continuing Grant
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- 批准号:
2330940 - 财政年份:2024
- 资助金额:
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Continuing Grant
Collaborative Research: NSF-BSF: SaTC: CORE: Small: Detecting malware with machine learning models efficiently and reliably
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$ 161.53万 - 项目类别:
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协作研究:SaTC:核心:中:具有灵活隐私建模、机器检查系统设计和准确性优化的差异化私有 SQL
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2317233 - 财政年份:2024
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2338302 - 财政年份:2024
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