Generation of probabilistic synthetic data for serious games: a case study on cyberbullying

J. Pérez, M. Castro, E. Awad, G. López

Knowledge-Based Systems Vol. 286, pp. 111440-1 - 111440-10

Summary:

Synthetic data generation has been a growing area of research in recent years. However, its potential applications in serious games have yet to be thoroughly explored. Advances in this field could anticipate data modeling and analysis, as well as speed up the development process. To fill this gap in the literature, we propose a simulator architecture for generating probabilistic synthetic data for decision-based serious games. This architecture is designed to be versatile and modular so that it can be used by other researchers on similar problems (e.g., multiple choice exams, political surveys, any type of questionnaire). To simulate the interaction of synthetic players with the game, we use a cognitive testing model based on the Item Response Theory framework. We also show how probabilistic graphical models (in particular, Bayesian networks) can introduce expert knowledge and external data into the simulation. Finally, we apply the proposed architecture and methods in the case of a serious game focused on cyberbullying. We perform Bayesian inference experiments using a hierarchical model to demonstrate the identifiability and robustness of the generated data.

Spanish layman's summary:

Esta investigación propone una arquitectura de simulador para generar datos sintéticos en juegos serios, utilizando modelos cognitivos y redes bayesianas. El objetivo es mejorar el modelado de datos y acelerar desarrollos, lo que se demuestra mediante un estudio de caso sobre un juego de ciberacoso.

English layman's summary:

This research proposes a simulator architecture for generating synthetic data in serious games, utilizing cognitive models and Bayesian networks. It aims to enhance data modeling and speed up development, demonstrated through a case study on a cyberbullying game.

Keywords: Synthetic data; Serious games; Cyberbullying; Item response theory; Bayesian network; Hierarchical Bayesian model; Computational social science

JCR Impact Factor and WoS quartile: 8,800 - Q1 (2022)

DOI reference: https://doi.org/10.1016/j.knosys.2024.111440

Published on paper: February 2024.

Published on-line: January 2024.

Citation:
J. Pérez, M. Castro, E. Awad, G. López, Generation of probabilistic synthetic data for serious games: a case study on cyberbullying. Knowledge-Based Systems. Vol. 286, pp. 111440-1 - 111440-10, February 2024. [Online: January 2024]