TSim: a system for discovering similar users on Twitter

This paper presents a framework for discovering similar users on Twitter that can be used in profiling users for social, recruitment and security reasons. The framework contains a novel formula that calculates the similarity between users on Twitter by using seven different signals (features). The signals are followings and followers, mention, retweet, favorite, common hashtag, common interests, and profile similarity. The proposed framework is scalable and can handle big data because it is implemented using the MapReduce paradigm. It is also adjustable since the weight and contribution of each signal in calculating the final similarity score is determined by the user based on their needs. The accuracy of the system was evaluated through human judges and by comparing the system's results against Twitter's Who To Follow service. The results show moderately accurate results.