A comparison of generated Wikipedia profiles using social labeling and automatic keyword extraction

Abstract

In many collaborative systems, researchers are interested in creating user profiles. There are many ways to create user profiles. In this paper, we are particularly interested in social labeling and automatic keyword extraction techniques for generating user profiles. Social labeling is a process in which users manually tag other users with keywords, which has become popular with the rise of Web2.0 systems and user generated contents. Automatic keyword extraction technique selects the most salient words to represent the user contribution. In this paper, we apply these two profile generation methods to highly active Wikipedia editors and their contributions, and compare the results. In our comparison, we found that profiles generated through social labeling matches the profiles extracted using automatic keyword extraction applied to Wikipedia revisions. The result suggests that user profiles generated from one method can be used as a seed for the other method.