The　task　of　detecting　fraudulent　reviewers　is　of　great　importance　to　E-commerce　platforms.　Existing　research　has　invested　much　effort　into　developing　comprehensive　features　and　advanced　techniques　to　detect　fraudulent　reviewers.　However,　most　of　these　studies　have　ignored　the　data　imbalance　problem　inherent　in　fraudulent　reviewer　detection:　non-fraudulent　reviewers　are　the　majority,　while　fraudulent　reviewers　are　the　minority　in　real　practice.　To　fill　this　gap,　we　propose　a　novel　approach　called　ImDetector　to　detect　fraudulent　reviewers　while　handling　data　imbalance　based　on　weighted　latent　Dirichlet　allocation　(LDA)　and　Kullback-Leibler　(KL)　divergence.　Specifically,　we　develop　a　weighted　LDA　model　to　extract　the　latent　topics　of　reviewers　distributed　on　the　review　features.　Asymmetric　KL　divergence　is　adopted　to　make　the　similarity　measure　between　reviewers　biased　toward　the　fraudulent　minority　when　using　the　K-nearest-neighbor　for　classification.　By　mapping　the　reviewers　to　the　latent　topics　of　features　derived　from　the　weighted　LDA　model　and　measuring　the　similarities　between　reviewers　using　asymmetric　KL　divergence,　the　data　imbalance　problem　in　fraudulent　reviewer　detection　is　alleviated.　Extensive　experiments　on　the　Yelp.com　dataset　demonstrate　that　the　proposed　ImDetector　approach　is　superior　to　the　state-of-the-art　techniques　used　for　fraudulent　reviewer　detection.　We　also　explain　the　experimental　results　and　present　the　managerial　implications　of　this　paper.