The number of users on Twitter has drastically increased in the past years. However, Twitter does not have an effective user grouping mechanism. Therefore tweets from other users can quickly overrun and become inconvenient to read. In this paper, we propose methods to help users group the people they follow using their provided seeding users. Two sources of information are used to build sub-systems: textural information captured by the tweets sent by users, and social connections among users. We also propose a measure of fitness to determine which sub-system best represents the seed users and use it for target user ranking. Our experiments show that our proposed framework works well and that adaptively choosing appropriate sub-system for group suggestion results in increased accuracy.