A framework of opinion dynamics using fuzzy linguistic 2-tuples Giráldez Cru, Jesús Chica Serrano, Manuel Cordón García, Óscar Opinion dynamics Fuzzy linguistic 2-tuples Agent-based models Social networks This work was supported by the Spanish Ministry of Science, Innovation and Universities, the Andalusian Government, Spain, the University of Granada, Spain, the Spanish National Agency of Research (AEI), and European Regional Development Funds (ERDF) under grants EXASOCO (PGC2018-101216-B-I00), AIMAR (A-TIC-284-UGR18), and SIMARK (P18-TP-4475). J. Giráldez-Cru is also supported through the Juan de la Cierva program (FJCI-2017-32420, IJC2019-040489-I). M. Chica is also supported through the Ramón y Cajal program (RYC-2016-19800). Opinion dynamics investigate the spreading and evolution of opinions among a set of individuals. This is especially relevant in decision-making — the process of selecting an alternative from a set of possible options —, that is commonly based on personal opinions which may evolve along time. In this work, we present a model of opinion dynamics where opinions are represented using fuzzy linguistic 2-tuples, a realistic representation of imprecise information. In our framework, the propagation of opinions in the communication is divided into three independent sub-processes. Additionally, we use a social network to represent agents’ interactions and an awareness deactivation mechanism to model the awareness dynamics in the system (i.e., options for which agents have opinions). Our opinion dynamics model can be easily integrated into an agent-based system to how opinions spread and evolve. Experimental results show the impact of the communication processes, the social network topology, the awareness deactivation mechanism, and the agents’ influence on the opinion dynamics of others. Furthermore, we present two case studies of our opinion dynamics model applied to marketing and politics. 2025-01-27T11:39:40Z 2025-01-27T11:39:40Z 2021-12-05 journal article J. Giráldez-Cru, M. Chica and O. Cordón. Knowledge-Based Systems 233 (2021) 107559. https://doi.org/10.1016/j.knosys.2021.107559 https://hdl.handle.net/10481/100548 10.1016/j.knosys.2021.107559 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier