Online review helps reducing uncertainty in the pre-purchasing decision phase and thus becomes an important information source for consumers. With the increasing popularity of online review systems, a large volume of reviews of varying quality is generated. Meanwhile, individual and professional spamming activities have been observed in almost all online review platforms. Deceptive reviews with fake ratings or fake content are inserted into the system to influence people’s perception from reading these reviews. The deceptive reviews and reviews of poor quality significantly affect the effectiveness of online review systems. In this work, we define novel aspect-specific indicators that measure the deviations of aspect-specific opinions of a review from the aggregated opinions. Then, we propose a three-layer trust framework that relies on aspect-specific indicators to ascertain veracity of reviews and compute trust scores of their reviewers. An iterative algorithm is developed for propagation of trust scores in the three-layer trust framework. The converged trust score of a reviewer is a credibility indicators that reflects the trustworthiness of the reviewer and the quality of his reviews, which becomes an effective trust index for online review spam detection.