  Though significant efforts such as removing false claims and promoting
reliable sources have been increased to combat COVID-19 "misinfodemic", it
remains an unsolved societal challenge if lacking a proper understanding of
susceptible online users, i.e., those who are likely to be attracted by,
believe and spread misinformation. This study attempts to answer {\it who}
constitutes the population vulnerable to the online misinformation in the
pandemic, and what are the robust features and short-term behavior signals that
distinguish susceptible users from others. Using a 6-month longitudinal user
panel on Twitter collected from a geopolitically diverse network-stratified
samples in the US, we distinguish different types of users, ranging from social
bots to humans with various level of engagement with COVID-related
misinformation. We then identify users' online features and situational
predictors that correlate with their susceptibility to COVID-19 misinformation.
This work brings unique contributions: First, contrary to the prior studies on
bot influence, our analysis shows that social bots' contribution to
misinformation sharing was surprisingly low, and human-like users'
misinformation behaviors exhibit heterogeneity and temporal variability. While
the sharing of misinformation was highly concentrated, the risk of occasionally
sharing misinformation for average users remained alarmingly high. Second, our
findings highlight the political sensitivity activeness and responsiveness to
emotionally-charged content among susceptible users. Third, we demonstrate a
feasible solution to efficiently predict users' transient susceptibility solely
based on their short-term news consumption and exposure from their networks.
Our work has an implication in designing effective intervention mechanism to
mitigate the misinformation dissipation.

Social and Information Networks;Computers and Society