Multilingual Hate Speech Detection Using NLP Techniques

Hate speech detection in multiple languages has emerged as a significant challenge in Natural Language Processing (NLP), primarily due to the diverse linguistic structures, cultural nuances, and variations in contextual meanings across languages. Unlike monolingual hate speech detection, which relies on well-established lexicons and training datasets, multilingual detection requires sophisticated models capable of handling code-switching, dialectal variations, and the absence of extensive labeled data for many languages. We explore various NLP techniques, including machine learning models, deep learning architectures, and transformer-based approaches for detecting hate speech across different languages. A critical aspect of hate speech detection is text preprocessing, which varies depending on the language. The preprocessing techniques such as tokenization, stopword removal, stemming, lemmatization, and handling emojis, slang, and abbreviations commonly found in online discourse. Additionally, we examine feature engineering methods, including Term Frequency-Inverse Document Frequency (TF-IDF), word embeddings (Word2Vec, GloVe, FastText), and contextual embeddings generated by transformer models.