  User-generated reviews of products are vital assets of online commerce, such
as Amazon and Yelp, while fake reviews are prevalent to mislead customers. GNN
is the state-of-the-art method that detects suspicious reviewers by exploiting
the topologies of the graph connecting reviewers, reviews, and target products.
However, the discrepancy in the detection accuracy over different groups of
reviewers can degrade reviewer engagement and customer trust in the review
websites. Unlike the previous belief that the difference between the groups
causes unfairness, we study the subgroup structures within the groups that can
also cause discrepancies in treating different groups. This paper addresses the
challenges of defining, approximating, and utilizing a new subgroup structure
for fair spam detection. We first identify subgroup structures in the review
graph that lead to discrepant accuracy in the groups. The complex dependencies
over the review graph create difficulties in teasing out subgroups hidden
within larger groups. We design a model that can be trained to jointly infer
the hidden subgroup memberships and exploits the membership for calibrating the
detection accuracy across groups. Comprehensive comparisons against baselines
on three large Yelp review datasets demonstrate that the subgroup membership
can be identified and exploited for group fairness.
