Understanding the efficiency of social tagging systems using information theory

Abstract

Given the rise in popularity of social tagging systems, it seems only natural to ask how efficient is the organically evolved tagging vocabulary in describing any underlying document objects? Does this distributed process really provide a way to circumnavigate the traditional categorization problem with ontologies? We analyze a social tagging site, namely del.icio.us, with information theory in order to evaluate the efficiency of this social tagging site for encoding navigation paths to information sources. We show that entropy analysis from information theory provides a natural way to understand the descriptive encoding power of tags, which appears to be weaning. We found that users appear to have responded by increasing the number of tags they use to describe each item. This metric should be helpful in future analysis of social tagging systems.