Social networking has been used widely by millions of people over the world. It has become the most popular way for people who want to connect and interact online with their friends. Currently, there are many social networking sites, for instance, Facebook, My Space, and Twitter, with a huge number of active users. Therefore, they are also good places for spammers or cheaters who want to steal the personal information of users or advertise their products. Recently, many proposed methods are applied to detect spam comments on social networks with different techniques. In this paper, we propose a similarity-based method that combines fingerprinting technique with trie-tree data structure and meet-in-the-middle approach in order to achieve a higher accuracy in spam comments detection. Using our proposed approach, we are able to detect around 98% spam comments in our dataset.