OMCD: Offensive Moroccan Comments Dataset

Offensive content, such as verbal attacks, demeaning comments, or hate speech, has become widespread on social media. Automatic detection of this content is considered an important and challenging task. Although several research works have been proposed to address this challenge for high-resource la...

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Bibliographic Details
Published in:Language resources and evaluation 2023-12, Vol.57 (4), p.1745-1765
Main Authors: Essefar, Kabil, Ait Baha, Hassan, El Mahdaouy, Abdelkader, El Mekki, Abdellah, Berrada, Ismail
Format: Article
Language:English
Subjects:
Annotations
Arabic language
Computational Linguistics
Computer Science
Data collection
Datasets
Deep learning
Dialects
Hate speech
Language and Literature
Linguistics
Machine learning
Natural language processing
Original Paper
Social media
Social Sciences
State-of-the-art reviews
Statistical analysis
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Summary:Offensive content, such as verbal attacks, demeaning comments, or hate speech, has become widespread on social media. Automatic detection of this content is considered an important and challenging task. Although several research works have been proposed to address this challenge for high-resource languages, research on detecting offensive content in Dialectal Arabic (DA) remains under-explored. Recently, the detection of offensive language in DA has gained increasing interest among researchers in Natural Language Processing (NLP). However, only a limited number of annotated datasets have been introduced for single or multiple coarse-grained dialects. In this paper, we introduce Offensive Moroccan Comments Dataset (OMCD), the first dataset for offensive language detection for the Moroccan dialect. First, we present the data collection steps, the statistical analysis, and the annotation guidelines of the introduced dataset. Then, we evaluate several state-of-the-art Machine Learning (ML) and Deep Learning (DL) based models on the OMCD dataset. Finally, we highlight the impact of emojis on the evaluated models for offensive language detection.
ISSN:1574-020X
1574-0218
DOI:10.1007/s10579-023-09663-2