Frontiers in Computer Science (Mar 2021)

Disruption in Chinese E-Commerce During COVID-19

  • Yuan Yuan,
  • Muzhi Guan,
  • Zhilun Zhou,
  • Sundong Kim,
  • Meeyoung Cha,
  • Meeyoung Cha,
  • Depeng Jin,
  • Yong Li

DOI
https://doi.org/10.3389/fcomp.2021.668711
Journal volume & issue
Vol. 3

Abstract

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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.

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