Online　review　fraud　and　review　manipulation　hurt　the　profits　of　stakeholders　and　undermine　the　value　of　online　reviews.　For　this　reason,　it　is　critical　to　detect　online　review　fraud　and　fraudulent　reviewers　effectively　for　the　development　of　e-commerce.　Extent　studies　propose　various　fraud　detection　techniques　to　detect　fraudulent　reviewers.　However,　most　of　these　studies　do　not　handle　the　data　imbalance　problem　in　fraudulent　reviewer　detection.　To　fill　this　research　gap,　this　paper　proposes　a　novel　approach　to　detect　fraudulent　reviewers　in　handling　the　data　imbalance　based　on　Expectation　Maximization　(EM)　and　Kullback-Leibler　(KL)　divergence　(called　EMKL).　We　first　use　the　expectation　maximization　algorithm　to　model　the　latent　topic　distributions　of　reviewers　on　the　review　features.　Then,　we　adopt　the　Kullback-Leibler　divergence　to　measure　the　similarities　of　reviewers　based　on　their　topic　distributions　to　detect　fraudulent　reviewers.　The　experiment　on　Yelp　dataset　shows　that　the　EMKL　approach　has　a　good　performance　in　detecting　fraudulent　reviewers.　In　addition,　the　proposed　EMKL　method　performs　better　than　the　performance　of　state-of-the-art　techniques.