RAPID: Tracking and Evaluation of the Coronavirus (COVID-19) Epidemic Propagation by Finding and Maintaining Live Knowledge in Social Media
RAPID:通过在社交媒体中查找和维护实时知识来跟踪和评估冠状病毒(COVID-19)的流行传播
基本信息
- 批准号:2026945
- 负责人:
- 金额:$ 15万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-06-15 至 2021-05-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Accurate situational awareness becomes an increasingly difficult challenge in rapidly changing environments. With currently exponential growth of COVID-19 confirmed cases, timely and reliable information becomes extremely important for informed decision making. Official reports based on confirmed test results are reliable, but widely considered to be a subset of the real situation. In contrast, social media provide broad coverage, but they have low reliability due to significant misinformation and disinformation or inaccurate news. With the gradual opening of businesses in the US, while the prospect of an effective vaccine remains uncertain, the need for reliable and accurate situation awareness becomes paramount, since the decisions for further business openings and practices of social distancing will depend on the information and perception of risks of contagion and the need for economic recovery. This project addresses the technical challenges of finding new, verifiable facts from noisy online media and social networks in a timely manner. Social media contain the necessary timely information, but they also carry significant challenges represented by misinformation, disinformation, and concept drift. Traditional machine learning (ML) models trained from closed data sets have been unable to meet these challenges when faced with true novelty in evolving new data, beyond the fixed training data. To handle these challenges, the Evidence-Based Knowledge Acquisition (EBKA) approach automates the integration of noisy social media data such as Twitter and Weibo with recognized, respected authoritative sources to detect verifiable facts timely and reliably. The project build on the LITMUS software tools to provide timely and reliable information com complement physical test result data, and enable better informed decision making by government officials, first responders, and the general public.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.
在迅速变化的环境中,准确的情境意识成为越来越困难的挑战。随着目前COVID-19的确认案例的指数级增长,及时且可靠的信息对于明智的决策变得极为重要。基于确认的测试结果的官方报告是可靠的,但被广泛认为是真实情况的一个子集。相比之下,社交媒体提供了广泛的覆盖范围,但由于严重的错误信息和虚假信息或新闻不正确,它们的可靠性较低。随着企业在美国的逐步开业,尽管有效疫苗的前景仍然不确定,但对可靠和准确的状况意识的需求变得至关重要,因为对进一步的业务开放和社会疏远的决策的决定将取决于传染风险的信息和感知,以及对经济恢复的需求。该项目解决了及时从嘈杂的在线媒体和社交网络找到新的,可验证的事实的技术挑战。社交媒体包含必要的及时信息,但它们也面临着以错误信息,虚假信息和概念漂移为代表的重大挑战。从封闭数据集中训练的传统机器学习(ML)模型在面对真正新颖的新数据(除了固定培训数据之外)时无法应对这些挑战。为了应对这些挑战,基于证据的知识获取(EBKA)方法可以自动化嘈杂的社交媒体数据(例如Twitter和Weibo)与公认的,受尊敬的权威资源,以及时可靠地检测可验证的事实。该项目以LITMUS软件工具为基础,以提供及时可靠的信息补充物理测试结果数据,并为政府官员,急救人员和公众提供更好的明智性决策。该奖项反映了NSF的法定任务,并被认为是值得通过基金会的知识分子和更广泛影响的评估来通过评估来获得支持的。
项目成果
期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Challenges and Opportunities in Rapid Epidemic Information Propagation with Live Knowledge Aggregation from Social Media
社交媒体实时知识聚合在疫情信息快速传播中的挑战与机遇
- DOI:10.1109/cogmi50398.2020.00026
- 发表时间:2020
- 期刊:
- 影响因子:0
- 作者:Pu, Calton;Suprem, Abhijit;Lima, Rodrigo Alves
- 通讯作者:Lima, Rodrigo Alves
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Calton Pu其他文献
Buffer overflows: attacks and defenses for the vulnerability of the decade
缓冲区溢出:十年来漏洞的攻击与防御
- DOI:
10.1109/discex.2000.821514 - 发表时间:
2000 - 期刊:
- 影响因子:0
- 作者:
Crispin Cowan;Perry Wagle;Calton Pu;Steve Beattie;Jonathan Walpole - 通讯作者:
Jonathan Walpole
JTangCSB: A Cloud Service Bus for Cloud and Enterprise Application Integration
JTangCSB:用于云和企业应用集成的云服务总线
- DOI:
10.1109/mic.2014.62 - 发表时间:
2015 - 期刊:
- 影响因子:0
- 作者:
Xingjian Lu;Calton Pu;Zhaohui Wu;Hanwei Chen - 通讯作者:
Hanwei Chen
Calton Pu的其他文献
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{{ truncateString('Calton Pu', 18)}}的其他基金
EAGER: Live Reality: Sustainable and Up-to-Date Information Quality in Live Social Media through Continuous Evidence-Based Knowledge Acquisition
EAGER:实时现实:通过持续的循证知识获取,实时社交媒体中可持续且最新的信息质量
- 批准号:
2039653 - 财政年份:2020
- 资助金额:
$ 15万 - 项目类别:
Standard Grant
HNDS-I: Collaborative Research: Developing a Data Platform for Analysis of Nonprofit Organizations
HNDS-I:协作研究:开发用于分析非营利组织的数据平台
- 批准号:
2024320 - 财政年份:2020
- 资助金额:
$ 15万 - 项目类别:
Standard Grant
1st US-Japan Workshop Enabling Global Collaborations in Big Data Research; June, 2017, Atlanta, GA
第一届美日研讨会促进大数据研究的全球合作;
- 批准号:
1741034 - 财政年份:2017
- 资助金额:
$ 15万 - 项目类别:
Standard Grant
RCN: SAVI: Adaptive Management and Use of Resilient Infrastructures in Smart Cities: Support for Global Collaborative Research on Real-Time Analytics of Heterogeneous Big Data
RCN:SAVI:智慧城市弹性基础设施的适应性管理和使用:支持异构大数据实时分析的全球协作研究
- 批准号:
1550379 - 财政年份:2015
- 资助金额:
$ 15万 - 项目类别:
Standard Grant
EAGER: An Exploratory Study of Multi-Hazard Management through Multi-Source Integration of Physical and Social Sensors
EAGER:通过物理和社会传感器的多源集成进行多危害管理的探索性研究
- 批准号:
1402266 - 财政年份:2014
- 资助金额:
$ 15万 - 项目类别:
Standard Grant
CSR: Small: Lightning in Clouds: Detection and Characterization of Very Short Bottlenecks
CSR:小:云中闪电:极短瓶颈的检测和表征
- 批准号:
1421561 - 财政年份:2014
- 资助金额:
$ 15万 - 项目类别:
Standard Grant
SAVI: EAGER: for Global Research on Applying Information Technology to Support Effective Disaster Management (GRAIT-DM)
SAVI:EAGER:应用信息技术支持有效灾害管理的全球研究 (GRAIT-DM)
- 批准号:
1250260 - 财政年份:2012
- 资助金额:
$ 15万 - 项目类别:
Standard Grant
RAPID: Automating Emergency Data and Metadata Management to Support Effective Short Term and Long Term Disaster Recovery Efforts
RAPID:自动化应急数据和元数据管理,支持有效的短期和长期灾难恢复工作
- 批准号:
1138666 - 财政年份:2011
- 资助金额:
$ 15万 - 项目类别:
Standard Grant
CSR:Small: Multi-Bottlenecks: What They Are and How to Find Them
CSR:小:多瓶颈:它们是什么以及如何找到它们
- 批准号:
1116451 - 财政年份:2011
- 资助金额:
$ 15万 - 项目类别:
Standard Grant
II-NEW: Collaborative Research: Spam Processing, Archiving, and Monitoring Community Facility (SPAM Commons)
II-新:协作研究:垃圾邮件处理、归档和监控社区设施 (SPAM Commons)
- 批准号:
0855180 - 财政年份:2009
- 资助金额:
$ 15万 - 项目类别:
Standard Grant
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