Social Mood Revealed

Social mood, the aggregated mood of a society, emerges from complex system of individual moods and their influences on each other. The real social networks consist of millions or even billions nodes constantly interacting with each other. Can such a complex system be modeled by a graph consisting of a small number of agents with simple interactions between them? Profile of Mood States, known and well-vetted psychometric instrument, distinguishes seven mood dimensions (Tension, Happiness, Calmness, Vigor, Fatigue, Confusion and Friendliness). If we apply them to a society at large, i.e. to social mood, is it possible to measure influences of one mood dimension on another? In addition to this, is it possible to both maintain good approximations of social mood changes and be able to observe such interactions at the same time? In this work we investigate these questions and propose a framework which can approximate or even, in some circumstances, be predictive of future social mood states. The framework consists of a model of social influence and an evolutionary algorithm learning proper network topology and model parameters.