  During COVID-19, misinformation on social media affects the adoption of
appropriate prevention behaviors. It is urgent to suppress the misinformation
to prevent negative public health consequences. Although an array of studies
has proposed misinformation suppression strategies, few have investigated the
role of predominant credible information during crises. None has examined its
effect quantitatively using longitudinal social media data. Therefore, this
research investigates the temporal correlations between credible information
and misinformation, and whether predominant credible information can suppress
misinformation for two prevention measures (i.e. topics), i.e. wearing masks
and social distancing using tweets collected from February 15 to June 30, 2020.
We trained Support Vector Machine classifiers to retrieve relevant tweets and
classify tweets containing credible information and misinformation for each
topic. Based on cross-correlation analyses of credible and misinformation time
series for both topics, we find that the previously predominant credible
information can lead to the decrease of misinformation (i.e. suppression) with
a time lag. The research findings provide empirical evidence for suppressing
misinformation with credible information in complex online environments and
suggest practical strategies for future information management during crises
and emergencies.
