Identifying similar users on Twitter plays a crucial role in applications such as recommendation systems, community discovery, and audience targeting. This study presents a comparative analysis of three established frameworks for user similarity detection: TSim, Characterizing and Detecting Similar Twitter Users, and Self-Similarity of Twitter Users. Each framework employs a distinct combination of interaction-based, content-based, and graph-based similarity signals. Using the Twitter API, we collected data for one examined user and eleven candidate users, including tweets, followers and followings, profile attributes, and interaction history. A total of ten similarity signals were implemented and analyzed across all frameworks. Correlation analysis revealed strong relationships between certain metrics, particularly between interaction and retweet similarity—while others, such as profile-based features, contributed unique, uncorrelated insights. Candidates were ranked using a composite similarity score derived from normalized signal values, and the results demonstrated strong alignment with human-evaluated rankings, achieving a Spearman correlation of 0.91. The findings confirm that combining diverse signals leads to a more accurate and interpretable user similarity model. This work lays a foundation for building adaptive and scalable similarity-based systems for real-time applications on social media platforms.