ABSTRACT
This paper explores the potential association between the spread of fake news and the panic buying behavior, in urban and rural UK, widely accessible on Twitter since COVID 19 was announced by the WHO as a global pandemic. It describes how consumer's behavior is affected by the content generated over social media and discuss various means to control such occurrence that results in an undesirable social change. The research methodology is based on extracting data from texts on the subject of panic buying and analysing both the total volume and the rate of fake news classification during COVID-19, through crowdsourcing techniques with text-mining and Natural Language Processing models. In this paper, we have extracted the main topics in different phases of the pandemic using term frequency strategies and word clouds as well as applied artificial intelligence in exploring the reliability behind online written text on Twitter. The findings of the research indicate an association between the pattern of panic buying behavior and the spread of fake news among urban and rural UK. We have highlighted the magnitude of the undesired behavior of panic buying and the spread of fake news in the rural UK in comparison with the urban UK.