Twitmographics: Learning the emergent properties of the twitter community

Abstract

This paper presents a framework for discovery of the emergent properties of users of the Twitter microblogging platform. The novelty of our methodology is the use of machine-learning methods to deduce user demographic information and online usage patterns and habits not readily apparent from the raw messages posted on Twitter. This is different from existing social network analysis performed on de facto social networks such as Face-book, in the sense that we use publicly available metadata from Twitter messages to explore the inherent characteristics about different segments of the Twitter community, in a simple yet effective manner. Our framework is coupled with the self-organizing map visualization method, and tested on a corpus of messages which deal with issues of socio politi-cal and economic impact, to gain insight into the properties of human interaction via Twitter as a medium for computer-mediated self-expression. © 2010 Springer-Verlag Wien.