Transformer learning-based neural network algorithms for identification and detection of electronic bullying in social media

Abstract

The global phenomenon known as cyberbullying is a form of modern harassment that cannot be entirely stopped but can be avoided. Most current solutions to the cyberbullying problem have relied on tools and methods to identify online bullying. However, end users do not have free access to these tools. The goal of this study is to create a model to combat cyberbullying on social media sites based on users’ appearance. In this article, we present a cyberbullying detection system constructed using the Word2Vec word-embedding method and a deep learning convolutional neural network combined with bidirectional long short-term memory (CNN-BiLSTM), as well as the XLM-Roberta transformer, to develop a model for cyberbullying detection. We carried out two experiments based on binary (hate speech or non-hate speech bullying comments) and multiclass (religion, age, gender, ethnicity, and non-bullying tweets) datasets collected from Kaggle online discussions and Twitter. To evaluate the model’s performance, we used standard measurement metrics, such as precision, recall, F1-score, and accuracy. Through a comparison of the results, it is noted that the XLM-Roberta model outperformed the CNN-BiLSTM model, resulting in 84% accuracy using the Kaggle online discussion dataset and 94% accuracy using the Twitter dataset.

Keywords

• artificial intelligence
• cyberbullying
• deep learning
• social media
• 68t50
• 68t07