Enhancing Multilingual Hate Speech Detection: From Language-Specific Insights to Cross-Linguistic Integration

The rise of social media has enabled individuals with biased perspectives to spread hate speech, directing it toward individuals based on characteristics such as race, gender, religion, or sexual orientation. Constructive interactions in varied communities can greatly enhance self-esteem, yet it is vital to consider that adverse comments may affect individuals' social standing and emotional health. The crucial task of detecting and addressing this type of content is imperative for reducing its negative effects on communities and individuals alike. The rising occurrence highlights the urgency for enhanced methods and robust regulations on digital platforms to protect humans from such prejudicial and damaging conduct. Hate speech typically appears as a deliberate hostile action aimed at a particular group, often with the intent to demean or isolate them based on various facets of their identity. Research on hate speech predominantly targets resource-aware languages like English, German, and Chinese. Conversely, resource-limited languages, including European languages such as Italian, Spanish, and Portuguese, alongside Asian languages like Roman Urdu, Korean, and Indonesian, present obstacles. These challenges arise from a lack of linguistic resources, making the extraction of information a more strenuous task. This study is focused on the detection and improvement of multilingual hate speech detection across 13 different languages. To conduct a thorough analysis, we carried out a series of experiments that ranged from classical machine learning techniques and mainstream deep learning approaches to recent transformer-based methods. Through hyperparameter tuning, optimization techniques, and generative configurations, we achieved robust and generalized performance capable of effectively identifying hate speech across various dialects. Specifically, we achieved a notable enhancement in detection performance, with precision and recall metrics exceeding baseline models by up to 10% across several lesser-studied languages. Additionally, our work extends the capabilities of explainable AI within this context, offering deeper insights into model decisions, which is crucial for regulatory and ethical considerations in AI deployment. Our study presents substantial performance improvements across various datasets and languages through meticulous comparisons. For example, our model significantly outperformed existing benchmarks: it achieved F1-scores of 0.90 in German (GermEval-2018), up from the baseline score of 0.72, and 0.93 in German (GermEval-2021), a substantial increase from 0.58. Additionally, it scored 0.95 in Roman Urdu HS, surpassing the previous peak of 0.91. Furthermore, for mixed-language datasets such as Italian and English (AMI 2018), our accuracy rose dramatically from 0.59 to 0.96. These outcomes emphasize the robustness and versatility of our model, establishing a new standard for hate speech detection systems across diverse linguistic settings.