TY - JOUR
T1 - Panic Buying and Fake News in Urban vs. Rural UK
T2 - A Case Study of Twitter During COVID-19
AU - Ali, Maged
AU - Gomes, Lucas Moreira
AU - Azab, Nahed
AU - Souza, João Gabriel de Moraes
AU - Sorour, Karim
AU - Kimura, Herbert
PY - 2023/4/16
Y1 - 2023/4/16
N2 - This paper explores the potential association between the spread of fake news and the panic buying behaviour, 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 behaviour 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 behaviour and the spread of fake news among urban and rural UK. We have highlighted the magnitude of the undesired behaviour of panic buying and the spread of fake news in the rural UK in comparison with the urban UK.
AB - This paper explores the potential association between the spread of fake news and the panic buying behaviour, 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 behaviour 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 behaviour and the spread of fake news among urban and rural UK. We have highlighted the magnitude of the undesired behaviour of panic buying and the spread of fake news in the rural UK in comparison with the urban UK.
M3 - Article
JO - Technological Forecasting and Social Change
JF - Technological Forecasting and Social Change
SN - 0040-1625
ER -