A bullying-severity identifier framework based on machine learning and fuzzy logic

Abstract

Bullying at schools is a serious social phenomenon around the world that negatively affects the development of children. However, anti-bullying programs should not focus on labeling children as either bullies or victims since they could produce opposite effects. Thus, an approach to deal with bullying episodes, without labeling children, is to determine their severity, so that school staff may respond to them appropriately. Related work about computational techniques to fight against bullying showed promising results but they offer categorical information as a set of labels. This work proposes a framework to determine bullying severity in texts, composed by two parts: (1) evaluation of texts using Support Vector Machine (SVM) classifiers found in the literature, and (2) development of a Fuzzy Logic System that uses the outputs of SVM classifiers as its inputs to identify the bullying severity. Results show that it is necessary to improve the accuracy of SVM classifiers to determine the bullying severity through Fuzzy Logic.