  This study explores the connection between disinformation, defined as
deliberate spread of false information, and rate-induced tipping (R-tipping), a
phenomenon where systems undergo sudden changes due to rapid shifts in
ex-ternal forces. While traditionally, tipping points were associated with
exceeding critical thresholds, R-tipping highlights the influence of the rate
of change, even without crossing specific levels. The study argues that
disinformation campaigns, often organized and fast-paced, can trigger R-tipping
events in public opinion and societal behavior. This can happen even if the
disinformation itself doesn't reach a critical mass, making it challenging to
predict and control. Here, by Transforming a population dynamics model into a
network model, Investigating the interplay between the source of
disinformation, the exposed population, and the medium of transmission under
the influence of external sources, the study aims to provide valuable insights
for predicting and controlling the spread of disinformation. This mathematical
approach holds promise for developing effective countermeasures against this
increasingly prevalent threat to public discourse and decision-making.
