Design of a Framework using Bidirectional Encoder Representations from Transformers to Understanding Panic Buying Behavior During the COVID-19 Pandemic

Panic buying, characterized by consumers purchasing unusually large quantities of products in response to disasters, perceived threats, or anticipated price raises or shortages, remains a multifaceted phenomenon requiring further investigation. The COVID-19 crisis has provided a unique opportunity to conduct thorough analyses of panic buying behavior in a real-world context. Furthermore, the pandemic has underscored the importance of understanding panic buying dynamics, given its significant impact on consumer behavior and supply chain resilience. While many studies have concentrated on the psychological aspects of this phenomenon, there exists a gap in exploring its impact on products and goods. Therefore, there is a critical need to examine its effects across various product categories. In this study, we employed innovative topic modeling techniques to examine panic buying behavior and its implications during the COVID-19 crisis. Leveraging data from the X platform, our study adopts a novel approach integrating Sentence-BERT and BERTopic methodologies to identify key topics across diverse product categories. By providing insights into the outcomes of panic buying, this study contributes to a more comprehensive understanding of consumer behavior during crisis. Moreover, our findings hold considerable significance for policymakers and supply chain managers, offering insights to develop targeted interventions aimed at mitigating the impact of panic buying on supply chains and ensuring efficient resource allocation during future crises.