  The plague of false information, popularly called fake news has affected
lives of news consumers ever since the prevalence of social media. Thus
understanding the spread of false information in social networks has gained a
lot of attention in the literature. While most proposed models do content
analysis of the information, no much work has been done by exploring the
community structures that also play an important role in determining how people
get exposed to it. In this paper we base our idea on Computational Trust in
social networks to propose a novel Community Health Assessment model against
fake news. Based on the concepts of neighbor, boundary and core nodes of a
community, we propose novel evaluation metrics to quantify the vulnerability of
nodes (individual-level) and communities (group-level) to spreading false
information. Our model hypothesizes that if the boundary nodes trust the
neighbor nodes of a community who are spreaders, the densely-connected core
nodes of the community are highly likely to become spreaders. We test our model
with communities generated using three popular community detection algorithms
based on two new datasets of information spreading networks collected from
Twitter. Our experimental results show that the proposed metrics perform
clearly better on the networks spreading false information than on those
spreading true ones, indicating our community health assessment model is
effective.
