Distributed reputation systems establish trust among strangers in online communities and provide incentives for users to contribute. In these systems, each user monitors the interactions of others and computes the reputations accordingly. Collecting information for computing the reputations is challenging for the users due to their vulnerability to attacks, their limited resources, and the burst of their interactions. The low cost of creating accounts in most reputation systems makes them popular to million of users, but also enables malicious users to boost their reputations by performing Sybil attacks. Furthermore, the burst of user interactions causes an information overload. To avoid the impact of malicious users and information overload, we propose EscapeLimit, a sybil attack-resistant, computationally simple, and fully distributed method for information collection. EscapeLimit leverages user interactions as indicators of trust and similarity between the corresponding users, and collects relevant and trusted information by limiting the escape probability into the Sybil area. We evaluate it by emulating interaction patterns derived from synthetic and real-world networks. Our evaluation shows EscapeLimit's effectiveness in terms of its resilience to Sybil attacks, its scalability, and its ability to provide relevant information to each user.