Paper: Interactive Group Suggesting for Twitter

ACL ID P11-2091
Title Interactive Group Suggesting for Twitter
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011
Authors

The number of users on Twitter has drasti- cally increased in the past years. However, Twitter does not have an effective user group- ing mechanism. Therefore tweets from other users can quickly overrun and become in- convenient to read. In this paper, we pro- pose methods to help users group the peo- ple they follow using their provided seeding users. Two sources of information are used to build sub-systems: textural information cap- tured 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 the appropriate sub-system for group suggestion results in in- cre...