Using Word2Vec-LDA-Word Mover Distance for Comparing the Patterns of Information Seeking and Sharing during the COVID-19 Pandemic

During pandemics such as COVID-19, government announcements were sources to convey accurate and relevant information to the public in times of outbreak. Prior studies attempted to explore the public awareness and behavioral changes from various research disciplines in response to the COVID-19 pandemic. Literature has pointed out that the appropriate use of information sources significantly relates to public attitudes in battling the pandemic. Social media has been the widely used medium to express public interests in current events. Literature shows that social media use during a crisis effectively coordinates relevant information from different sources and promotes situational awareness. Therefore, it is crucial to investigate scalable approaches to promptly gather insights into the public's interests and how governments responded to the interests relevant to the COVID-19 pandemic. However, there is little empirical research found that tackles these needs. Therefore, we aim to close the research gap by examining the feasible approaches for (1) identifying if public information-seeking has similar patterns as information-sharing on social media during the COVID-19 pandemic, and (2) comparing the patterns with the government announcements to confirm if the announcements show aligned response to the public information-seeking and sharing during the COVID-19 pandemic. We applied text processing, LDA topic modeling, and Word Mover Distance techniques to realize our aim through a Malaysian case study. Our research work contributes to the application of the LDA-Word2Vec-Word Mover Distance architecture and algorithms that can be used for future investigation and comparison of information seeking and sharing patterns in different research subjects.