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- Title
Classification bullying/cyberbullying through smartphone sensor and a questionnaire application.
- Authors
Convertini, Vito Nicola; Gattulli, Vincenzo; Impedovo, Donato; Terrone, Grazia
- Abstract
This 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.
- Subjects
CYBERBULLYING; BULLYING; MACHINE learning; COMPUTER science; HUMAN behavior models; SMARTPHONES
- Publication
Multimedia Tools & Applications, 2024, Vol 83, Issue 17, p51291
- ISSN
1380-7501
- Publication type
Article
- DOI
10.1007/s11042-023-17609-7