Further experiments in micro-blog categorization

Abstract

Since the creation of Twitter in 2008, micro-blogging services have received a lot of attention among users who wish to share news items, opinions and information with friends and colleagues. However, these services typically provide for only limited organisation of content, with the main ranking criterion being post time with perhaps some basic message filtering accommodated. Given the substantial and increasing volume of posts that micro-blogging services attract, there is a clear need to assist users when it comes to effectively consuming this content. In this regard, categorisation offers one approach to organise content by grouping related messages together. In this paper we present a study in the recommendation of categories for short-form messages in order to provide for better search and message filtering. In particular, we present an index-based approach where real-time web data can be used as a source of knowledge for category recommendation. Further, we evaluate our approach on two different micro-blogging datasets and results show that micro-blog messages in sufficient quantities provide a useful recommendation signal for category recommendation.