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

• 批准号: 2026945
• 负责人: Calton Pu
• 金额: $ 15万
• 依托单位: Georgia Tech Research Corporation
• 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: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2026945&HistoricalAwards=false
• 关键词: RAPID; Tracking; Evaluation; Coronavirus; COVID

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.

在快速变化的环境中，准确的态势感知成为越来越困难的挑战。随着目前 COVID-19 确诊病例呈指数级增长，及时、可靠的信息对于做出明智的决策变得极其重要。基于已确认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部分。相比之下，社交媒体提供了广泛的覆盖范围，但由于存在严重的错误信息和虚假信息或不准确的新闻，其可靠性较低。随着美国企业的逐步开放，虽然有效疫苗的前景仍不确定，但对可靠和准确的态势感知的需求变得至关重要，因为进一步开放企业的决定和社交距离的做法将取决于信息和感知蔓延的风险和经济复苏的必要性。该项目解决了从嘈杂的在线媒体和社交网络中及时发现新的、可验证的事实的技术挑战。社交媒体包含必要的及时信息，但它们也带来了以错误信息、虚假信息和概念漂移为代表的重大挑战。当面临超出固定训练数据的新数据演变的真正新颖性时，从封闭数据集训练的传统机器学习 (ML) 模型无法应对这些挑战。为了应对这些挑战，基于证据的知识获取（EBKA）方法自动将 Twitter 和微博等嘈杂的社交媒体数据与公认的、受人尊敬的权威来源集成，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，提供及时、可靠的信息，补充物理测试结果数据，并使政府官员、急救人员和公众能够做出更明智的决策。该奖项反映了 NSF 的法定使命，并被认为是值得的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来获得支持。

项目成果

Beyond Artificial Reality: Finding and Monitoring Live Events from Social Sensors

超越人工现实：从社交传感器查找和监控实时事件

• DOI: 10.1145/3374214
• 发表时间: 2020-03
• 期刊: ACM transactions on Internet technology
• 作者: Pu, C.;Suprem, A.;Lima, R.A.;Musaev, A.;Wang, D.;Irani, D.;Webb, S.;Ferreira, J.E.
• 通讯作者: Ferreira, J.E.

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: 2020-10
• 期刊: 2020 IEEE Second International Conference on Cognitive Machine Intelligence
• 作者: Pu, Calton;Suprem, Abhijit;Lima, Rodrigo Alves
• 通讯作者: Lima, Rodrigo Alves