  Information spreading social media platforms has become ubiquitous in our
lives due to viral information propagation regardless of its veracity. Some
information cascades turn out to be viral since they circulated rapidly on the
Internet. The uncontrollable virality of manipulated or disorientated true
information (fake news) might be quite harmful, while the spread of the true
news is advantageous, especially in emergencies. We tackle the problem of
predicting information cascades by presenting a novel variant of SEIZ
(Susceptible/ Exposed/ Infected/ Skeptics) model that outperforms the original
version by taking into account the cognitive processing depth of users. We
define an information cascade as the set of social media users' reactions to
the original content which requires at least minimal physical and cognitive
effort; therefore, we considered retweet/ reply/ quote (mention) activities and
tested our framework on the Syrian White Helmets Twitter data set from April
1st, 2018 to April 30th, 2019. In the prediction of cascade pattern via
traditional compartmental models, all the activities are grouped, and their
summation is taken into account; however, transition rates between compartments
should vary according to the activity type since their requirements of physical
and cognitive efforts are not same. Based on this assumption, we design a
cognition-driven SEIZ (CD-SEIZ) model in the prediction of information cascades
on Twitter. We tested SIS, SEIZ, and CD-SEIZ models on 1000 Twitter cascades
and found that CD-SEIZ has a significantly low fitting error and provides a
statistically more accurate estimation.
