Automatic Cyberbullying Detection: a Mexican case in High School and Higher Education students

Abstract

The social interaction among young students has been partially or totally transformed to mobile-based communication, specifically through the use of social networks. This new communication environment has allowed a more immediate, diverse and massive interaction, offering a faster and more effective situation when carrying out academic and recreational activities. However, this scenario has also promoted the phenomenon of social harassment known as bullying, exponentially increasing its scope and diversifying the types and forms of aggression. Machine learning and natural language processing techniques have been used to create models that detect bullying situations among students, using data corpus from mainly public social networks. However, generally, these data sources are not representative of the social networks commonly used by the students; generating classification models that do not consider the vocabulary used by this social group. This article describes the methodology used to create a representative data corpus of the interaction between Mexican high school and university students, and a comparative analysis on characteristics that influence the quality of the content of a corpus in this domain. In addition, the performance achieved by implementing various machine learning models to identify bullying situations is presented. The best result is reported for the Naive Bayesian classifier (F1-Score of 0.862), performing better than models based on deep learning such as Recurrent (F1-Score of 0.845) and Convolutional (F1-Score of 0.807) Neural Networks.