A pattern-based approach for an early detection of popular Twitter accounts

Abstract

Social networks (SN) are omnipresent in our lives today. Not all users have the same behaviour on these networks. If some have a low activity, rarely posting messages and following few users, some others at the other extreme have a significant activity, with many followers and regularly posts. The important role of these popular SN users makes them the target of many applications for example for content monitoring or advertising. It is therefore relevant to be able to predict as soon as possible which SN users will become popular. In this work, we propose a technique for early detection of such users based on the identification of characteristic patterns. We present an index, H2M, which allows a scaling up of our approach to large social networks. We also describe our first experiments that confirm the validity of our approach.