Verified users on online social media (OSM) largely determine the quality of OSM services and applications, but most OSM users are unverified due to the significant effort involved in becoming a verified user. This paper presents SocialDistance, a novel technique to identify unverified users that can be considered as trustworthy as verified users. SocialDistance is motivated by the observation that online interactions initiated from verified users towards unverified users can translate into some sort of trustworthiness. It treats all verified users equally and assigns a trust score between 0 and 1 to each unverified user. The higher the trust score, the closer an unverified user to verified users. We propose various metrics to model the interactions from verified to unverified users and then derive corresponding trust scores. SocialDistance is thoroughly evaluated with large Twitter datasets containing 276,143 verified users and 19,047,202 unverified users. Our results demonstrate that SocialDistance can produce a non-trivial number of unverified users that can be regarded as verified users for OSM applications. We also show the high efficacy of SocialDistance in sybil detection, a fundamental operation performed by virtually every OSM operator.