Assessing Transformers and Traditional Models for Spanish-English Code-Switched Hate Detection

Hate speech detection research has mainly focused on monolingual contexts, with limited exploration of multilingual and code-switched languages that introduce distinct linguistic complexities. This study examines hate speech detection in code-switched Spanglish content from social media, comparing transformer-based models—XLM-RoBERTa, DistilBERT, Multilingual BERT, and mT5—with traditional machine learning approaches, including support vector machines, logistic regression, and multinomial naïve Bayes using TF-IDF features. The results indicate that XLM-RoBERTa achieves the highest performance, with 96.14 percent accuracy, 96.16 percent precision, 96.14 percent recall, and a 96.12 percent F1-score, demonstrating its superiority in detecting code-switched hate speech. Although traditional models, particularly SVM (94.03 percent accuracy), perform well, transformer-based approaches offer clear advantages in multilingual contexts.