  Modelling information cascades over online social networks is important in
fields from marketing to civil unrest prediction, however the underlying
network structure strongly affects the probability and nature of such cascades.
Even with simple cascade dynamics the probability of large cascades are almost
entirely dictated by network properties, with well-known networks such as
Erdos-Renyi and Barabasi-Albert producing wildly different cascades from the
same model. Indeed, the notion of 'superspreaders' has arisen to describe
highly influential nodes promoting global cascades in a social network. Here we
use a simple model of global cascades to show that the presence of locality in
the network increases the probability of a global cascade due to the increased
vulnerability of connecting nodes. Rather than 'super-spreaders', we find that
the presence of these highly connected 'super-blockers' in heavy-tailed
networks in fact reduces the probability of global cascades, while promoting
information spread when targeted as the initial spreader.
