Exploring the long tail of social media tags

Abstract

There are millions of users who tag multimedia content, generating a large vocabulary of tags. Some tags are frequent, while other tags are rarely used following a long tail distribution. For frequent tags, most of the multimedia methods that aim to automatically understand audio-visual content, give excellent results. It is not clear, however, how these methods will perform on rare tags. In this paper we investigate what social tags constitute the long tail and how they perform on two multimedia retrieval scenarios, tag relevance and detector learning. We show common valuable tags within the long tail, and by augmenting them with semantic knowledge, the performance of tag relevance and detector learning improves substantially.