Analyse Des Comportements D'individus Sur Les Réseaux Sociaux

Résumé: The study of the innovation diffusion offers an insight to predict its adoption by a particular community. The diffusion of innovation theory was introduced as a general framework to expound and analyze this social phenomenon. Despite the fact that several studies have been conducted about the innovation diffusion and the richness of literature, little attention has been given to modeling innovation nature and individuals’ characteristics that generate different behaviors toward this innovation. The evolutionary algorithm was chosen to deepen the evolution of the adoption decision, as a gradual process affected by exposure to the adoption of neighbors or to innovation itself because of the urgent need to include as many of these important concepts as possible. The current thesis proposes two original models to account this limitation, which are sustained with the theory of innovation diffusion; two types of experimentations were carried out. To better describe the impact of information exchange, a simulation with three forms of social structure lattice, ring and random network, as a first type was performed. The use of data from Facebook platform was the second type. Indeed, the research focused on the relationship between the micro-level behavior and the macro-level diffusion patterns. Namely, different simulation scenarios were conducted using a probabilistic foundation, which demonstrated the influence of every represented notion. The model, therefore, proves an explicit diffusion pattern and the ability to determine the early adopters without the need of historical data. The results show that there is a consistency of representation inspired from innovation diffusion theory - reinforced by individuals’ gradual acceptance model – along with the features of evolutionary algorithms. This outcome constitutes a promising area for further exploratory researches. Keywords diffusion of innovation, evolutionary algorithm, gradual acceptance, innovation characteristics, individuals’ heterogeneity, social network, Facebook datasets.

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