Classifying the Severity of Cyberbullying Incidents by Using a Hierarchical Squashing-Attention Network

Abstract

Cyberbullying has become more prevalent in online social media platforms. Natural language processing and machine learning techniques have been employed to develop automatic cyberbullying detection models, which are only designed for binary classification tasks that can only detect whether the text contains cyberbullying content. Cyberbullying severity is a critical factor that can provide organizations with valuable information for developing cyberbullying prevention strategies. This paper proposes a hierarchical squashing-attention network (HSAN) for classifying the severity of cyberbullying incidents. Therefore, the study aimed to (1) establish a Chinese-language cyberbullying severity dataset marked with three severity ratings (slight, medium, and serious) and (2) develop a new squashing-attention mechanism (SAM) of HSAN according to the squashing function, which uses vector length to estimate the weight of attention. Experiments indicated that the SAM could sufficiently analyze sentences to determine cyberbullying severity. The proposed HSAN model outperformed other machine-learning-based and deep-learning-based models in determining the severity of cyberbullying incidents.