Navigating Dialectal Bias and Ethical Complexities in Levantine Arabic Hate Speech Detection

TL;DR

This paper addresses the challenge of detecting hate speech in Levantine Arabic, a dialect with rich regional variation and sociopolitical significance, which current NLP tools struggle to handle. It critically analyzes the limitations of existing datasets, notably the Lebanese bias in L-HSAB, and demonstrates that domain-specific embeddings tailored to Levantine Arabic outperform generic pre-trained models. The authors advocate for culturally aware data collection, community engagement, and ethical design to mitigate dialectal bias and misclassification, aiming to produce more accurate and inclusive hate speech detection for the Arab world. The practical impact lies in guiding dataset construction, annotation practices, and model development toward linguistically and culturally informed NLP tools that respect speaker identities while enhancing online safety.

Abstract

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.