Parameter Identification Method for Opinion Dynamics Models: Tested via Real Experiments

Abstract

One of the interesting topics in the field of social networks engineering is opinion change dynamics in a discussion group and how to use real experimental data in order to identify an interaction pattern among individuals. In this paper, we propose a method that utilizes experimental data in order to identify the influence network between individuals in social networks. The employed method is based on convex optimization and can identify interaction patterns precisely. This technique considers individuals' opinions in multiple dimensions. Moreover, the opinion dynamics models that have been introduced in the literature are investigated. Then, the three models which are the most comprehensive and vastly accepted in the literature, are considered. These three models are then proven to satisfy the convexity condition, which means they can be used for the introduced method of identification. Four real experiments have been conducted in this research that their results verify the application of our method. The outcomes of these experiments are presented in this paper.