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; 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。