1 research outputs found
Causal Modeling of Twitter Activity During COVID-19
Understanding the characteristics of public attention and sentiment is an
essential prerequisite for appropriate crisis management during adverse health
events. This is even more crucial during a pandemic such as COVID-19, as
primary responsibility of risk management is not centralized to a single
institution, but distributed across society. While numerous studies utilize
Twitter data in descriptive or predictive context during COVID-19 pandemic,
causal modeling of public attention has not been investigated. In this study,
we propose a causal inference approach to discover and quantify causal
relationships between pandemic characteristics (e.g. number of infections and
deaths) and Twitter activity as well as public sentiment. Our results show that
the proposed method can successfully capture the epidemiological domain
knowledge and identify variables that affect public attention and sentiment. We
believe our work contributes to the field of infodemiology by distinguishing
events that correlate with public attention from events that cause public
attention.Comment: 13 pages, 3 figure