  In a fast evolving major public health crisis such as the COVID-19 pandemic,
multiple pieces of relevant information can be posted sequentially in a social
media platform. The interval between subsequent posting times may have
different impact on the transmission and cross-propagation of the old and new
information to result in different peak value and final size of forwarding
users of the new information, depending on the content correlation and whether
the new information is posted during the outbreak or quasi steady state phase
of the old information. To help in designing effective communication strategies
to ensure information is delivered to the maximal number of users, we develop
and analyze two classes of susceptible-forwarding-immune information
propagation models with delay in transmission, to describe the
cross-propagation process of relevant information. We parametrize these models
using real data from the Sina-Microblog and use the parametrized models to
define and evaluate mutual attractiveness indices, and we use these indices and
parameter sensitivity analyses to inform strategies to ensure optimal
strategies for a new information to be effectively propagated in the microblog.
