Recent　years　have　witnessed　an　increasing　number　of　manipulated　online　reviews　in　e-commerce　platforms.　Previous　research　has　provided　substantial　evidence　that　vendor　manipulation　of　online　reviews　has　a　significant　negative　impact　on　the　stakeholders　involved　in　the　e-commerce　business.　Many　platforms　take　various　governance　measures　to　filter　manipulated　reviews.　Nevertheless,　the　effectiveness　of　these　measures　still　remains　unknown　to　a　large　extent.　To　bridge　this　research　gap,　this　paper　investigates　the　effect　of　differentiated　platform　governance,　including　defined　as　interventions　to　counterattack　manipulation　intensity,　manipulation　duration,　and　perceived　quality　manipulated,　on　the　probability　of　future　review　manipulation.　We　develop　a　game　theoretical　model　that　incorporates　the　strategic　interactions　between　the　platform　and　vendors,　which　yield　several　testable　hypotheses.　We　then　conduct　an　empirical　analysis　of　platform　governance　and　review　manipulation　by　using　the　review　manipulation　data　collected　from　Amazon.com.　Results　of　the　analytical　model　and　empirical　analysis　show　that　platform　governance　that　targets　manipulation　intensity　and　manipulation　duration　can　both　effectively　mitigate　review　manipulation　probability.　On　the　contrary,　platform　governance　to　counterattack　manipulating　perceived　product　quality　exhibits　an　inverted　U-shape　relationship　with　review　manipulation　probability.　This　study　provides　novel　insights　into　how　to　better　mitigate　online　review　manipulation　for　e-commerce　platforms.