Understanding the evolutions of public responses using social media: Hurricane Matthew case study

Abstract

Understanding timely evolutions of public responses during disasters can help crisis response managers design and implement response strategies. Recent studies mainly employed public sentiment and concerns to investigate public responses with social media data. However, these studies neglected social media users' post frequencies for sentiment analysis which can exaggerate the impact of users with high post frequencies. A sentiment baseline is also missing to reveal disaster impacts on public sentiment. Moreover, a quantification index to represent evolutions of public concerns across the disaster periods is necessary but not investigated yet. In order to bridge the above-mentioned research gaps, this research proposes to analyze social media users' post frequencies and employ the annual average sentiment as a sentiment baseline. This paper innovatively employs the LDA (Latent Dirichlet allocation) topic model to calculate the weights and sentiment for topics in public expressions. To validate this method, a case study of Hurricane Matthew is implemented for investigating the evolutions of public responses. The results show public sentiment in most affected regions has received negative impacts. Public expressions mainly concentrate on crisis-related topics and their sentiment towards these topics varies significantly across the whole disaster periods. The findings can help crisis response managers understand the public's special concerns and panics with the evolution of disasters and further support their design and implementation of effective response strategies.