Navigating Dialectal Bias and Ethical Complexities in Levantine Arabic Hate Speech Detection
It addresses the challenge of dialectal bias and ethical complexities in hate speech detection for Levantine Arabic speakers, but is incremental as it focuses on analysis and advocacy rather than new methods or data.
This paper tackles the problem of hate speech detection for underrepresented Levantine Arabic dialects by examining the limitations of current datasets and highlighting dialectal bias, advocating for more culturally informed NLP tools without presenting specific results or numbers.
Social media platforms have become central to global communication, yet they also facilitate the spread of hate speech. For underrepresented dialects like Levantine Arabic, detecting hate speech presents unique cultural, ethical, and linguistic challenges. This paper explores the complex sociopolitical and linguistic landscape of Levantine Arabic and critically examines the limitations of current datasets used in hate speech detection. We highlight the scarcity of publicly available, diverse datasets and analyze the consequences of dialectal bias within existing resources. By emphasizing the need for culturally and contextually informed natural language processing (NLP) tools, we advocate for a more nuanced and inclusive approach to hate speech detection in the Arab world.