AUTHOR=Yuan Yuan , Guan Muzhi , Zhou Zhilun , Kim Sundong , Cha Meeyoung , Jin Depeng , Li Yong TITLE=Disruption in Chinese E-Commerce During COVID-19 JOURNAL=Frontiers in Computer Science VOLUME=3 YEAR=2021 URL=https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2021.668711 DOI=10.3389/fcomp.2021.668711 ISSN=2624-9898 ABSTRACT=

The recent outbreak of the novel coronavirus (COVID-19) has infected millions of citizens worldwide and claimed many lives. This paper examines the impact of COVID-19 on Chinese e-commerce by analyzing behavioral changes observed on a large online shopping platform. We first conduct a time series analysis to identify product categories that faced the most extensive disruptions. The time-lagged analysis shows that behavioral patterns of shopping actions are highly responsive to the epidemic's development. Based on these findings, we present a consumer demand prediction method by encompassing the epidemic statistics and behavioral features of COVID-19-related products. Experimental results demonstrate that our predictions outperform existing baselines and further extend to long-term and province-level forecasts. Finally, we discuss how our market analysis and prediction can help better prepare for future pandemics by gaining extra time to launch preventive measures.