作者：Hanjia Lyu Arsal Imtiaz Yufei Zhao Jiebo Luo
自世界卫生组织（WHO）于2020年3月将新冠肺炎定性为流行性疾病以来，截至2022年10月，已确诊的新冠肺炎病例超过6亿，死亡人数超过600万。新冠肺炎大流行与人类行为之间的关系很复杂。一方面，人们发现人类行为会影响疾病的传播。另一方面，疫情几乎在各个方面影响甚至改变了人类的行为。为了全面了解人类行为与新冠肺炎疫情之间的复杂相互作用，研究人员一直在使用大数据技术，如自然语言处理、计算机视觉、音频信号处理、频繁模式挖掘和机器学习。在本研究中，我们概述了利用大数据技术研究新冠肺炎大流行期间人类行为的现有研究。特别是，我们将这些研究分为三组——使用大数据进行测量、建模和杠杆分析
Since the World Health Organization (WHO) characterized COVID-19 as apandemic in March 2020, there have been over 600 million confirmed cases ofCOVID-19 and more than six million deaths as of October 2022. The relationshipbetween the COVID-19 pandemic and human behavior is complicated. On one hand,human behavior is found to shape the spread of the disease. On the other hand,the pandemic has impacted and even changed human behavior in almost everyaspect. To provide a holistic understanding of the complex interplay betweenhuman behavior and the COVID-19 pandemic, researchers have been employing bigdata techniques such as natural language processing, computer vision, audiosignal processing, frequent pattern mining, and machine learning. In thisstudy, we present an overview of the existing studies on using big datatechniques to study human behavior in the time of the COVID-19 pandemic. Inparticular, we categorize these studies into three groups – using big data tomeasure, model, and leverage human behavior, respectively. The related tasks,data, and methods are summarized accordingly. To provide more insights into howto fight the COVID-19 pandemic and future global catastrophes, we furtherdiscuss challenges and potential opportunities.
论文链接：http://arxiv.org/pdf/2303.13452v1
更多计算机论文：http://cspaper.cn/