Content and Activity Based Friends Suggestion in an Annotation System

Annotation systems enhance user collaboration by allowing structured sharing, commenting, and content discus-sion. They improve clarity, ensure contextual relevance, facilitate real-time or asynchronous interaction, and foster a collaborative environment. The success of collaborative projects relies on finding annotators who share similar interests, as this ensures that feedback and contributions are relevant and meaningful. This project aims to create a text-based annotation system that will allow users to apply similarity metrics between their annotations and those of others to identify the most suitable annotators. Additionally, the program will enable users to take online tests automatically generated based on a selected term from the annotated text. The system recommends appropriate YouTube materials and identifies annotators who score highly in comparable quizzes based on the results of the quizzes. Users can search for and chat with others, promoting an enriched and collaborative learning experience. Experimental tests in this work reflect a promising enhancement in user collaboration and knowledge dissemination.