  Social reviews are indispensable resources for modern consumers' decision
making. For financial gain, companies pay fraudsters preferably in groups to
demote or promote products and services since consumers are more likely to be
misled by a large number of similar reviews from groups. Recent approaches on
fraudster group detection employed handcrafted features of group behaviors
without considering the semantic relation between reviews from the reviewers in
a group. In this paper, we propose the first neural approach, HIN-RNN, a
Heterogeneous Information Network (HIN) Compatible RNN for fraudster group
detection that requires no handcrafted features. HIN-RNN provides a unifying
architecture for representation learning of each reviewer, with the initial
vector as the sum of word embeddings of all review text written by the same
reviewer, concatenated by the ratio of negative reviews. Given a co-review
network representing reviewers who have reviewed the same items with the same
ratings and the reviewers' vector representation, a collaboration matrix is
acquired through HIN-RNN training. The proposed approach is confirmed to be
effective with marked improvement over state-of-the-art approaches on both the
Yelp (22% and 12% in terms of recall and F1-value, respectively) and Amazon (4%
and 2% in terms of recall and F1-value, respectively) datasets.
