Classification bullying/cyberbullying through smartphone sensor and a questionnaire application

Abstract

AbstractThis study establishes a correlation between computer science and psychology, specifically focusing on the incorporation of smartphone sensors and users' personality index. A limited number of state-of-the-art approaches have considered these factors, while no existing dataset currently encompasses this correlation. In this study, an Android application was developed to implement a questionnaire on bullying and cyberbullying, using smartphone sensors to predict Personal Index. Sensor data are collected in the “UNIBA HAR Dataset” and were analyzed using AI algorithms to find a correlation between the categorization class of the questionnaire (Personality Index) and the prediction of ML behavioral models. The results indicate that the Bayesian Bridge with "Bullying bully vs. Victimization bullying" and "Total bullying vs. Total victimization" performs better on average 0.94 accuracy, and the LSTM with the last categorization performs 0.89 accuracy. These results are crucial for future development in the same research area. Graphical abstract