In Social network analysis and mining
Social media platforms have become a common place for information exchange among their users. People leave traces of their emotions via text expressions. A systematic collection, analysis, and interpretation of social media data across time and space can give insights into local outbreaks, mental health, and social issues. Such timely insights can help in developing strategies and resources with an appropriate and efficient response. This study analysed a large Spatio-temporal tweet dataset of the Australian sphere related to COVID19. The methodology included a volume analysis, topic modelling, sentiment detection, and semantic brand score to obtain an insight into the COVID19 pandemic outbreak and public discussion in different states and cities of Australia over time. The obtained insights are compared with independently observed phenomena such as government-reported instances.
Bashar Md Abul, Nayak Richi, Balasubramaniam Thirunavukarasu
COVID19, Deep learning, Dynamic topic modelling, Impact analysis, Informed machine learning, Neural topic modelling, SBS, Sentiment analysis, Topic analysis