Proceedings of the 2013 Winter Simulation Conference R. Pasupathy, S.-H. Kim, A. Tolk, R. Hill, and M. E. Kuhl, eds. A SIMULATION-BASED APPROACH TO ANALYZE THE INFORMATION DIFFUSION IN MICROBLOGGING ONLINE SOCIAL NETWORK Maira A de C Gatti Ana Paula Appel Cicero Nogueira dos Santos Claudio Santos Pinhanez Paulo Rodrigo Cavalin Samuel Barbosa Neto IBM Research-Brazil Av. Tut ´ oia 1157, Para´ ıso, S˜ ao Paulo (SP), BRAZIL ABSTRACT In this paper we propose a stochastic multi-agent based approach to analyze the information diffusion in Microblogging Online Social Networks (OSNs). OSNs, like Twitter and Facebook, became extremely popular and are being used to target marketing campaigns. Key known issues on this targeting is to be able to predict human behavior like posting a message with regard to some topics, and to analyze the emergent behavior of such actions. We explore Barack Obama’s Twitter network as an egocentric network to present our simulation-based approach and predictive behavior modeling. Through experimental analysis, we evaluated the impact of inactivating both Obama and the most engaged users, aiming at understanding the influence of those users that are the most likely to disseminate information over the network. 1 INTRODUCTION Online social networks (OSNs) have become very popular in the last years, not only for users but also for researchers. In this context, Twitter is just a few years old and has attracted much attention already (Kwak, Lee, Park, and Moon 2010). Through OSNs, users connect with each other, share and find content, and disseminate information. Nowadays, OSNs are the most used way of information diffusion, a process for widely spreading a new idea or action through communication channels (Rogers and Rogers 2003). Information diffusion has been widely studied by sociologists, marketers, and epidemiologists (Kempe, Kleinberg, and Tardos 2003, Leskovec, Adamic, and Huberman 2006, Strang and Soule 1998). Large OSN are useful for studying information diffusion as topic propagation in blogspace (Gruhl et al. 2004), linking patterns in blog graph (Leskovec and Horvitz 2008), favorite photo marking in a social photo sharing service (Cha, Mislove, and Gummadi 2009), among others. In this case, understanding how users behave when they connect to these sites is important for a number of reasons. In viral marketing, for instance, one might want to exploit models of user interactions to spread their content or promotions quickly and widely. Although there are numerous models of influence spread in social networks that try to model the process of adoption of an idea or a product, it is still difficult to measure and predict how a market campaign will spread in an OSN. One style of modeling that is consistent with the sciences of complexity is agent-based simulation (ABS) (Jennings 2001). In a multi-agent system, many software agents interact among themselves and with the environment. ABS looks at agent behavior at a decentralized level, at the level of the individual agent, in order to explain the dynamic behavior of the system at the macro-level. Agents are autonomous entities: an agent is capable of acting without direct external intervention. Multi-agent systems can handle 1685 978-1-4799-2076-1/13/$31.00 ©2013 IEEE