Analyzing users’ activity in online social networks over time through a multi-agent framework

Abstract

Abstract. The number of people and organizations using on-line social networks as a new way of communication is continually increasing. Messages that users write in networks and their interactions with other users leave a digital trace that is recorded. In order to understand what is going on in these virtual environments, it is necessary systems that collect, process, and analyze the information generated. The majority of existing tools analyze information related to an on-line event once it has finished or in a specific point of time (i.e., without considering an in-depth analysis of the evolution of users' activity during the event). They focus on an analysis based on statistics about the quantity of information generated in an event. In this article, we present a multi-agent system (MAS) that automates the process of gathering data from users' activity in social networks and performs an in-depth analysis of the evolution of social behavior at different levels of granularity in on-line events based on network theory metrics. We evaluated its functionality analyzing users' activity in events on Twitter.

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