Calibration of an agent-based model for opinion formation through a retweet social network

Calibration of agent-based models (ABM) for opinion formation is needed to set their parameters and allow their employment in the real world. In this paper, we propose to use the correspondence between the agent-based model and the social network where those agents express their opinions, namely Twitter. We propose a calibration method that uses the frequency of retweets as a measure of influence and allows to obtain the influence coefficients in the ABM by direct inspection of the weighted adjacency matrix of the social network graph. The method has a fairly general applicability to linear ABMs. We report a sample application to a Twitter dataset where opinions about wind power (where turbines convert the kinetic energy of wind into mechanical or electrical energy) are voiced. Most influence coefficients (76%) result to be zero, and very few agents (less than 5%) exert a strong influence on other agents.