Navigating Dialectal Bias and Ethical Complexities in L evantine A rabic Hate Speech Detection (2025)

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.

## introduction in the levant, deep-rooted socio-political tensions have turned language into a weapon. the rise of digital platforms has amplified hate speech, necessitating robust detection and mitigation mechanisms @xcite . automated tools leveraging nlp are essential for curbing online hate speech (jahan and oussalah, 2023). however, these tools are not equally effective across all languages and dialects. while significant progress has been made for languages like english, levantine arabic remains underserved @xcite . levantine arabic, spoken across syria, jordan, palestine, and lebanon, is a dialect continuum with significant regional variations, making it challenging for current nlp technologies to capture @xcite . existing hate speech detection models often overlook the rich cultural and sociolinguistic nuances of the dialect. this paper addresses the ethical, cultural, and linguistic challenges in detecting hate speech in levantine arabic and highlights the critical need for more representative datasets. ## ethical considerations in hate speech detection the dialectical bias identified above privileges one regional dialect over others, and risk marginalizing communities whose voices are already underrepresented in the digital sphere. there are also ethical concerns beyond issues of data bias. false positives-where non-hate speech is misclassified-can result in the suppression of legitimate cultural expressions, especially in a region where language is tightly bound to identity. a prominent example is the misclassification of the arabic word "shaheed", meaning "martyr", by social media platforms like meta (the oversight board, 2024). the term holds significant cultural and religious importance, often used to honor individuals who have died for a sacred cause. however, automated moderation systems have frequently removed content containing "shaheed," interpreting it as a reference to terrorism or violent extremism due to its association with entities on terrorism watchlists. conversely, false negatives-where actual hate speech goes undetected-allow harmful narratives to spread unchecked, fueling further violence. for example, derogatory terms or slurs specific to a particular region or group may go unnoticed by moderation systems trained primarily on other dialects or on modern standard arabic. in the context of the syrian conflict, hate speech containing region-specific pejoratives aimed at certain ethnic or sectarian groups might not be recognized as such by models lacking comprehensive dialectal data. this oversight enables the propagation of inflammatory content that can exacerbate tensions and incite real-world violence. technologists and researchers have a responsibility to develop models that not only detect hate speech but do so in a way that respects the linguistic and cultural integrity of levantine arabic. practically, ethical considerations are particularly relevant within a conflict-ridden region like the levant where the failure to identify and address hate speech content undermines efforts to promote peace and stability. by incorporating diverse linguistic inputs and cultural insights, developers can create more nuanced models that differentiate between harmful content and legitimate expression, thereby protecting both free speech and community safety. ## towards more culturally aware language technologies addressing the challenges of hate speech detection in levantine arabic requires practical solutions that consider the language's unique properties. bergman and diab (2022) offer valuable guidelines for developing effective and ethically sound nlp tools for underrepresented dialects. by incorporating these recommendations, we can create language technologies that are culturally aware and inclusive, specifically tailored to levantine arabic. ## conclusion detecting hate speech in levantine arabic presents unique cultural, linguistic, and ethical challenges due to intricate dialectal variations and biased datasets. this highlights the urgent need for more inclusive nlp approaches. by engaging local communities, reimagining data collection, and embedding ethical considerations into technology design, we can develop tools that effectively identify hate speech while honoring the levant's rich linguistic diversity. this paper advocates for renewed cultural sensitivity in nlp applications targeting levantine arabic. addressing sociolinguistic complexities and ethical implications enables us to create tools that serve all speakers, enhance detection accuracy, and promote a more just digital environment throughout the arab world.