Towards tagging and categorization for micro-blogs

Micro-blogging services are becoming very popular among users who want to share local or global news, their knowledge or their opinions on the real-time web. Lately, users are also using these services to search for information, and some services include tag or category information to better facilitate search. However, these tags are typically free-form in nature with users permitted to adopt their own conventions without restriction, which can make the set of tags noisy and sparse. A solution to this problem is to recommend tags (or categories) to users. Our work represents an initial study in the recommendation of categories for short-form messages in order to provide for better search and message filtering. In particular, we describe how such real-time web data can be used as a source of indexing and retrieval information for category recommendation. An evaluation performed on two different micro-blogging datasets indicates that promising performance is achieved by our approach.