Online reviews on products and services can be very useful for customers, but they need to be protected from manipulation. So far, most studies have focused on analyzing online reviews from a single hosting site. How could one leverage information from multiple review hosting sites? This is the key question in our work. In response, we develop a systematic methodology to merge, compare, and evaluate reviews from multiple hosting sites. We focus on hotel reviews and use more than 15 million reviews from more than 3.5 million users spanning three prominent travel sites. Our work consists of three thrusts: (a) we develop novel features capable of identifying cross-site discrepancies effectively, (b) we conduct arguably the first extensive study of cross-site variations using real data, and develop a hotel identity-matching method with 93% accuracy, (c) we introduce the TrueView score, as a proof of concept that cross-site analysis can better inform the end user. Our results show that: (1) we detect 7 times more suspicious hotels by using multiple sites compared to using the three sites in isolation, and (2) we find that 20% of all hotels appearing in all three sites seem to have low trustworthiness score. Our work is an early effort that explores the advantages and the challenges in using multiple reviewing sites towards more informed decision making.