Nowadays, many online communities provide means for users to contribute in the evaluation of community created media by tagging, commenting and rating. Judging the users expertise in such collaborative systems is an important issue. As these systems are becoming increasingly popular, they are attackable, e.g. by Sybil Attacks. Thus, an effective expert ranking strategy must be robust to such attacks. In this paper, we propose MHITS, an algorithm to rank users' expertise by exploiting the number of users' fair ratings and direct trust users gain in the online community. We integrate SumUp, a Sybil-resilient algorithm, into MHITS algorithm as a robust ranking strategy. Experimental results show the effectiveness of the proposed method, which can ensure that the highly ranked experts are highly trusted users and provide the high number of fair ratings for the relevant media. We contribute to the experimental evaluation of algorithms for online systems, fighting malicious behavior.