Crossing Borders: Multilingual Hate Speech Detection - Chanda S.; Dhaka A.; Pal S. (My University, 2025)
With the relentless growth of technology usage, particularly among younger generations, the alarming prevalence of hate speech on the internet has become an urgent global concern. This research paper addresses this critical need by presenting an extensive investigation encompassing three distinct hate speech detection tasks across a diverse linguistic landscape. The first task involves hate and offensive speech classification in Gujarati and Sinhala, assessing sentence-level hatefulness. The second task extends to fine-grained BIO tagging, enabling precise identification of hate speech within sentences. Finally, the third task expands the scope to hate speech classification in Bengali, Bodo, and Assamese using social media data, categorizing content as hateful or not. Employing state-of-the-art deep learning techniques tailored to each language’s characteristics, this research contributes significantly to the development of robust and culturally sensitive hate speech detection systems, imperative for nurturing safer online spaces and fostering cross-cultural understanding. Warning: The content of this paper may contain offensive material, reader discretion is advised. © 2023 Copyright for this paper by its authors.