Event Detection from Social Media for Epidemic Prediction
Tanmay Parekh Anh Mac Jiarui Yu Yuxuan Dong Syed Shahriar Bonnie Liu Eric Yang Kuan-Hao Huang Wei Wang Nanyun Peng Kai-Wei Chang
Social media is an easy-to-access platform providing timely updates about
societal trends and events. Discussions regarding epidemic-related events such
as infections, symptoms, and social interactions can be crucial for informing
policymaking during epidemic outbreaks. In our work, we pioneer exploiting
Event Detection (ED) for better preparedness and early warnings of any upcoming
epidemic by developing a framework to extract and analyze epidemic-related
events from social media posts. To this end, we curate an epidemic event
ontology comprising seven disease-agnostic event types and construct a Twitter
dataset SPEED with human-annotated events focused on the COVID-19 pandemic.
Experimentation reveals how ED models trained on COVID-based SPEED can
effectively detect epidemic events for three unseen epidemics of Monkeypox,
Zika, and Dengue; while models trained on existing ED datasets fail miserably.
Furthermore, we show that reporting sharp increases in the extracted events by
our framework can provide warnings 4-9 weeks earlier than the WHO epidemic
declaration for Monkeypox. This utility of our framework lays the foundations
for better preparedness against emerging epidemics.