Topic, Sentiment and Impact Analysis: COVID19 Information Seeking on Social Media
This provides incremental insights for public health officials and researchers by analyzing social media data to monitor COVID-19 impacts in Australia.
The study analyzed a large spatio-temporal tweet dataset from Australia to understand COVID-19 information seeking, using methods like topic modeling and sentiment detection to gain insights into local outbreaks and public discussion, with results compared to government-reported data.
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.