  When people notice something unusual, they discuss it on social media. They
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 on 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, dynamic topic modelling, sentiment detection, and semantic
brand score to obtain an insight on 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.
