  Threshold models of global cascades have been extensively used to model
real-world collective behavior, such as the contagious spread of fads and the
adoption of new technologies. A common property of those cascade models is that
a vanishingly small seed fraction can spread to a finite fraction of an
infinitely large network through local infections.
  In social and economic networks, however, individuals' behavior is often
influenced not only by what their direct neighbors are doing, but also by what
the majority of people are doing as a trend. A trend affects individuals'
behavior while individuals' behavior creates a trend.
  To analyze such a complex interplay between local- and global-scale
phenomena, I generalize the standard threshold model by introducing a new type
of node, called \textit{global nodes} (or \textit{trend followers}), whose
activation probability depends on a global-scale trend; specifically the
percentage of activated nodes in the population. The model shows that global
nodes play a role as accelerating cascades once a trend emerges while reducing
the probability of a trend emerging. Global nodes thus either facilitate or
inhibit cascades, suggesting that a moderate share of trend followers may
maximize the average size of cascades.
