The University of Tokyo（东京大学）；Barcelona Supercomputing Center（巴塞罗那超级计算中心）；Yale University（耶鲁大学）；Preferred Networks, inc.（Preferred Networks公司）；Tokyo Institute of Technology（东京工业大学）；Tokyo Medical and Dental University（东京医科齿科大学）；ESSEC Business School（埃塞克商学院）；Tokyo Metropolitan Hospital（东京都立医院）；Janssen Pharmaceutical（杨森制药）；Kantar（凯度）；SUSTech（南方科技大学）
This paper aims at providing the summary of the Global Data Science Project (GDSC) for COVID-19. as on May 31 2020. COVID-19 has largely impacted on our societies through both direct and indirect effects transmitted by the policy measures to counter the spread of viruses. We quantitatively analysed the multifaceted impacts of the COVID-19 pandemic on our societies including people's mobility, health, and social behaviour changes. People's mobility has changed significantly due to the implementation of travel restriction and quarantine measurements. Indeed, the physical distance has widened at international (cross-border), national and regional level. At international level, due to the travel restrictions, the number of international flights has plunged overall at around 88 percent during March. In particular, the number of flights connecting Europe dropped drastically in mid of March after the United States announced travel restrictions to Europe and the EU and participating countries agreed to close borders, at 84 percent decline compared to March 10th. Similarly, we examined the impacts of quarantine measures in the major city: Tokyo (Japan), New York City (the United States), and Barcelona (Spain). Within all three cities, we found the significant decline in traffic volume. We also identified the increased concern for mental health through the analysis of posts on social networking services such as Twitter and Instagram. Notably, in the beginning of April 2020, the number of post with #depression on Instagram doubled, which might reflect the rise in mental health awareness among Instagram users. Besides, we identified the changes in a wide range of people's social behaviors, as well as economic impacts through the analysis of Instagram data and primary survey data.
Beyond a binary of (non)racist tweets: A four-dimensional categorical detection and analysis of racist and xenophobic opinions on Twitter in early Covid-19
People Still Care About Facts: Twitter Users Engage More with Factual Discourse than Misinformation--A Comparison Between COVID and General Narratives on Twitter
University of Florida（佛罗里达大学）；Federal University of Paraná（巴拉那联邦大学）
Mapping the Landscape of Artificial Intelligence Applications against COVID-19
United Nations Global Pulse（联合国全球脉动）；Institute for Data Science, Durham University（杜伦大学数据科学研究所）；Mila Québec Artificial Intelligence Institute（Mila魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；NYU Stern School of Business（纽约大学斯特恩商学院）；World Health Organization（世界卫生组织）
