Modélisation de la diffusion de l’innovation dans les réseaux sociaux

Abstract

Recently, the diffusion of innovation has seen a paradigm shift and emerged as a renewed and interesting field due to advances in artificial intelligence, behavioral modeling, and empirical simulation. Understanding adoption behavior is increasingly complex, as individuals’ decisions are influenced not only by innovation attributes and social pressure but also by psychological characteristics, especially personality traits. This thesis offers a personality-driven diffusion model that integrates Big Five personality framework (OCEAN) into the Rogers' Diffusion of Innovation theory. Using agent-based modeling (ABM), individuals are presented as agents having distinct profiles, which shapes their perception of innovation features (relative_advantage, compatibility, complexity, trialability, observability). The framework consists of four major phases: perception, communication, persuasion, and decision. The special feature of the present research is the two-phase modeling strategy: (1) an exploratory simulation with randomly generated values in order to understand the fundamental diffusion dynamics. (2) the model is applied empirically, by applying a hybrid BERT-Random Forest model to predict Twitter users' personality traits based on ChatGPT-related data, these extracted qualities are subsequently used to regenerate the values of the remaining model' attributes and simulate adoption processes. The findings demonstrate that actually adoption behavior is profoundly influenced by personality driven dimensions, resulting in more realistic adoption curves than traditional models. This approach opens up new perspectives for the behavioral study of innovation diffusion