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The plague of false information, popularly called fake news has affectedlives of news consumers ever since the prevalence of social media. Thusunderstanding the spread of false information in social networks has gained alot of attention in the literature. While most proposed models do contentanalysis of the information, no much work has been done by exploring thecommunity structures that also play an important role in determining how peopleget exposed to it. In this paper we base our idea on Computational Trust insocial networks to propose a novel Community Health Assessment model againstfake news. Based on the concepts of neighbor, boundary and core nodes of acommunity, we propose novel evaluation metrics to quantify the vulnerability ofnodes (individual-level) and communities (group-level) to spreading falseinformation. Our model hypothesizes that if the boundary nodes trust theneighbor nodes of a community who are spreaders, the densely-connected corenodes of the community are highly likely to become spreaders. We test our modelwith communities generated using three popular community detection algorithmsbased on two new datasets of information spreading networks collected fromTwitter. Our experimental results show that the proposed metrics performclearly better on the networks spreading false information than on thosespreading true ones, indicating our community health assessment model iseffective.

Assessing Individual and Community Vulnerability to Fake News in Social Networks