Member contributions power many online communities. Users have uploaded billions of images to flickr, bookmarked millions pages on del.icio.us, and authored millions of encyclopedia articles at Wikipedia. Tags --- member contributed words or phrases that describe items --- have emerged as a powerful method for searching, organizing, and making sense of, these vast corpora.
In this thesis we explore the dynamics, challenges, and possibilities of tagging systems. We study the way in which factors influencing an individual user's choice of tags can affect the evolution of community tags as a whole. Like other community-maintained systems, tagging systems can suffer from low quality contributions. We study interfaces and algorithms that can differentiate between low quality and high quality tags. Finally, we explore tagommenders, tag-based recommendation algorithms that combine the flexibility of tags with the automation of recommender systems.
We base our explorations on tagging activity in the MovieLens movie recommendation system. We analyze tagging behavior, user studies, and surveys, of 97,000 tags and 3,600 users. Our results provide insight into the dynamics of existing tagging communities, and suggest mechanisms that address challenges of, and provide extensions to, tagging systems.