Data Expansion Using WordNet-based Semantic Expansion and Word Disambiguation for Cyberbullying Detection

Automatic identification of cyberbullying from textual content is known to be a challenging task. The challenges arise from the inherent
structure of cyberbullying and the lack of labeled large-scale corpus, enabling efficient machine-learning-based tools including neural
networks. This paper advocates a data augmentation-based approach that could enhance the automatic detection of cyberbullying in
social media texts. We use both word sense disambiguation and synonymy relation in WordNet lexical database to generate coherent
equivalent utterances of cyberbullying input data. The disambiguation and semantic expansion are intended to overcome the inherent
limitations of social media posts, such as an abundance of unstructured constructs and limited semantic content. Besides, to test the
feasibility, a novel protocol has been employed to collect cyberbullying traces data from AskFm forum, where about a 10K-size dataset
has been manually labeled. Next, the problem of cyberbullying identification is viewed as a binary classification problem using an
elaborated data augmentation strategy and an appropriate classifier. For the latter, a Convolutional Neural Network (CNN) architecture
with FastText and BERT was put forward, whose results were compared against commonly employed Na¨ıve Bayes (NB) and Logistic
Regression (LR) classifiers with and without data augmentation. The research outcomes were promising and yielded almost 98.4% of
classifier accuracy, an improvement of more than 4% over baseline results.