Work

Offensive Language Detection in Arabizi

Year: 2023

Type: conference-paper

Abstract: Detecting offensive language in underresourced languages presents a significant real-world challenge for social media platforms.This paper is the first work focused on the issue of offensive language detection in Arabizi, an under-explored topic in an under-resourced form of Arabic.For the first time, a comprehensive and critical overview of the existing work on the topic is presented.In addition, we carry out experiments using different BERT-like models and show the feasibility of detecting offensive language in Arabizi with high accuracy.Throughout a thorough analysis of results, we emphasize the complexities introduced by dialect variations and out-ofdomain generalization.We use in our experiments a dataset that we have constructed by leveraging existing, albeit limited, resources.To facilitate further research, we make this dataset publicly accessible to the research community.

Source: Arabic Natural Language Processing Conference (ArabicNLP)

DOI: 10.18653/v1/2023.arabicnlp-1.36

Authors: Imene Bensalem, Meryem Mout, Paolo Rosso

Institution: Aix-Marseille Université

Language: English

Topic: Hate Speech and Cyberbullying Detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: Qatar Foundation, Fonds National de la Recherche Luxembourg, Qatar National Research Fund

OpenAlex ID: W4389518284