Self-supervised hate speech detection in Norwegian texts with lexical and semantic augmentations

The proliferation of social media platforms has significantly contributed to the spread of hate speech, targeting individuals based on race, gender, impaired functioning, religion, or sexual orientation. Online hate speech not only provokes prejudice and violence in cyber-space, but it also has profound impacts in real-world communities, eroding social harmony and increasing the risk of physical harm. This necessitates the urgency for effective hate speech detection systems, especially in low-resource languages such as Norwegian, where limited data availability presents additional challenges. This study utilizes the Barlow Twins methodology, applying a self-supervised learning framework to initially develop robust language representations for Norwegian, a language that is typically underrepresented in NLP research. These learned representations are then utilized in a semi-supervised classification task to detect hate speech. Leveraging a combination of text augmentation techniques at both the word and sentence level, along with self-training strategies, our approach demonstrates the potential to efficiently learn meaningful representations with a minimal amount of annotated data. Experimental results show that the Nor-BERT model is well-suited for detecting hate speech within the limited Norwegian data available, consistently outperforming other models. Additionally, Nor-BERT surpassed all deep learning-based models in terms of F1-score. • Collecting and Annotating Norwegian hate speech data. • Enhanced self-learning with Barlow Twins. • Advancing NLP with Lexical and Semantic Augmentation. • LM-based supervised classification.