Review　spammers　detection　is　an　important　task　in　social　media　sentiment　analysis.　Previous　works　employ　reviewer　behaviors　such　as　text　similarities,　duplications　and　rating　patterns　to　indentify　suspicious　spammers.　However,　there　are　still　other　kinds　of　abnormal　spamming　activities　which　could　not　be　detected　by　the　available　techniques.　This　paper　proposes　a　review　spammer　detection　approach　combining　both　TrustRank　and　Anti-TrustRank　propagation　algorithm　to　identify　review　spammers.　Firstly,　a　two　layer　heterogeneous　review　relation　graph　is　constructed　to　capture　the　relationships　among　reviewers　and　products.　Secondly,　a　TrustRank　based　propagation　model　and　an　Anti-TrustRank　based　propagation　model　are　established　to　calculate　the　reviewers＇　trustiness　value　and　the　reviewers＇　anti-trustiness　value　respectively.　Finally,　review　spammers　are　detected　according　to　the　comprehensive　trustiness　value　which　combines　both　reviewers＇　trustiness　value　and　anti-trustiness　value.　Experimental　results　show　that　according　to　two　datasets,　our　presented　method　significantly　outperforms　the　existing　baselines,　and　is　able　to　find　more　abnormal　spamming　activities.