Abstract
Online Social Networks (OSN) are self-organized systems with emergent behavior from the individual interactions. Microblogging services in OSN, 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, forwarding or replying a message with regard to topics and sentiments, and to analyze the emergent behavior of such actions. To tackle this problem we present a method to model and simulate interactive behavior in microblogging OSN taking into account the users sentiment. We make use of a stochastic multi-agent based approach and we explore Barack Obama’s Twitter network as an egocentric network to present the experimental simulation results. We demonstrate that with this engineering method it is possible to develop social media simulators using a bottom-up approach (micro level) to evaluate the emergent behavior (macro level) and our preliminary results show how to better tune the modeler and the sampling and text classification impact on the simulation model.
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Notes
- 1.
Suppose a user posted a positive message about Obama, a negative message about Other topic in the \(\varDelta t\) time interval and there are only these two topics and two sentiments (positive and negative) being observed; if the first 2 positions of the vector are for positive and negative Obama index, and the other two for Other in that order; the vector is \([1,0,0,1]\).
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Gatti, M. et al. (2014). Large-Scale Multi-agent-Based Modeling and Simulation of Microblogging-Based Online Social Network. In: Alam, S., Parunak, H. (eds) Multi-Agent-Based Simulation XIV. MABS 2013. Lecture Notes in Computer Science(), vol 8235. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54783-6_2
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DOI: https://doi.org/10.1007/978-3-642-54783-6_2
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