  Social network based information campaigns can be used for promoting
beneficial health behaviours and mitigating polarisation (e.g. regarding
climate change or vaccines). Network-based intervention strategies typically
rely on full knowledge of network structure. It is largely not possible or
desirable to obtain population-level social network data due to availability
and privacy issues. It is easier to obtain information about individuals'
attributes (e.g. age, income), which are jointly informative of an individual's
opinions and their social network position. We investigate strategies for
influencing the system state in a statistical mechanics based model of opinion
formation. Using synthetic and data based examples we illustrate the advantages
of implementing coarse-grained influence strategies on Ising models with
modular structure in the presence of external fields. Our work provides a
scalable methodology for influencing Ising systems on large graphs and the
first exploration of the Ising influence problem in the presence of ambient
(social) fields. By exploiting the observation that strong ambient fields can
simplify control of networked dynamics, our findings open the possibility of
efficiently computing and implementing public information campaigns using
insights from social network theory without costly or invasive levels of data
collection.

Physics and Society;Statistical Mechanics;Social and Information Networks;Optimization and Control