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Classifying offensive language in Arabic: a novel taxonomy and dataset

  • Chaya Liebeskind

    Chaya Liebeskind is a senior lecturer and researcher in the Department of Computer Science at the Jerusalem College of Technology. Her research interests span both Natural Language Processing and data mining. Especially, her scientific interests include Semantic Similarity, Language Technology for Cultural Heritage, Morphologically rich languages (MRL), Multi-word Expressions (MWEs), Information Retrieval (IR), and Text Classification (TC). Much of her recent work has been focusing on analysing offensive language. She has published a variety of studies and a few of her articles are under review or in preparation. She is a member of several international research actions funded by the EU.

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    , Ali Afawi

    Ali Afawi is a student at Shamoon College of Engineering. He studies on the cyber track at the Department of Software Engineering. His research interests are cyber security and NLP applications for Semitic languages.

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    , Marina Litvak

    Marina Litvak is a Senior Lecturer at Shamoon College of Engineering, Department of Software Engineering. Marina’s research focuses mainly on Multilingual Text Analysis, Social Networks, Knowledge Extraction from Text, and Summarization. Marina published over 90 academic papers, including journal and top-level conference publications. She constantly serves on the program committees and editorial boards in multiple journals and conferences and collaborates on different research projects in Israel and abroad. She is a co-organizer of the MultiLing, FNP, Text2Story, and IACT workshops, collocated with top-level conferences.

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    and Natalia Vanetik

    Natalia Vanetik is a senior lecturer and researcher in the Department of Software Engineering at the Shamoon College of Engineering. Her research interests include Natural Language Processing, text mining, and optimization. Specifically, her research covers diverse range of topics in NLP and machine learning, including social media analysis, job vacancy ranking, and the development of evaluation systems for summarization tasks. Her research also extends to graph theory applications in data mining and cross-lingual transfer learning.

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Published/Copyright: December 10, 2024

Abstract

This paper presents a streamlined taxonomy for categorizing offensive language in Arabic, specifically Modern Standard Arabic (MSA) and the Levantine dialect. Addressing a gap in the existing literature, which has mainly focused on Indo-European languages, our taxonomy divides offensive language into seven levels (six explicit and one implicit). We adapted our framework from the simplified offensive language (SOL) taxonomy by (Lewandowska-Tomaszczyk, Barbara, Slavko Žitnik, Anna Bączkowska, Chaya Liebeskind, Jelena Mitrovic & Giedre Valunaite Oleškeviciente. 2021a. Lod-connected offensive language ontology and tagset enrichment. In Shubert R. Carvalho & Renato R. Souza (eds.), Proceedings of the workshops and tutorials held at ldk 2021 co-located with the 3rd language, data and knowledge conference, Vol. 3064, 135–150. CEUR Workshop Proceedings), customizing it to reflect the unique linguistic and cultural nuances of Arabic. To validate this taxonomy, we created a new dataset from various social media platforms, primarily focusing on Twitter. This dataset was manually curated by human annotators and is described in detail within the paper, serving as both a validation tool for our taxonomy and a foundation for future research on offensive language detection in Arabic. Initial analysis of the dataset reveals complex patterns of offensive expressions in MSA and Levantine Arabic, underscoring the need to account for linguistic and cultural variations in studying online abuse. Our taxonomy and dataset are vital for advancing research in Arabic sociocultural studies, natural language processing, and linguistic analysis, and contribute to the study of low-resource languages.


Corresponding author: Chaya Liebeskind, Department of Computer Science, Jerusalem College of Technology, 21 Havaad Haleumi St., P.O.B. 16031, Jerusalem, 9116001, Israel, E-mail:

Award Identifier / Grant number: N\A

About the authors

Chaya Liebeskind

Chaya Liebeskind is a senior lecturer and researcher in the Department of Computer Science at the Jerusalem College of Technology. Her research interests span both Natural Language Processing and data mining. Especially, her scientific interests include Semantic Similarity, Language Technology for Cultural Heritage, Morphologically rich languages (MRL), Multi-word Expressions (MWEs), Information Retrieval (IR), and Text Classification (TC). Much of her recent work has been focusing on analysing offensive language. She has published a variety of studies and a few of her articles are under review or in preparation. She is a member of several international research actions funded by the EU.

Ali Afawi

Ali Afawi is a student at Shamoon College of Engineering. He studies on the cyber track at the Department of Software Engineering. His research interests are cyber security and NLP applications for Semitic languages.

Marina Litvak

Marina Litvak is a Senior Lecturer at Shamoon College of Engineering, Department of Software Engineering. Marina’s research focuses mainly on Multilingual Text Analysis, Social Networks, Knowledge Extraction from Text, and Summarization. Marina published over 90 academic papers, including journal and top-level conference publications. She constantly serves on the program committees and editorial boards in multiple journals and conferences and collaborates on different research projects in Israel and abroad. She is a co-organizer of the MultiLing, FNP, Text2Story, and IACT workshops, collocated with top-level conferences.

Natalia Vanetik

Natalia Vanetik is a senior lecturer and researcher in the Department of Software Engineering at the Shamoon College of Engineering. Her research interests include Natural Language Processing, text mining, and optimization. Specifically, her research covers diverse range of topics in NLP and machine learning, including social media analysis, job vacancy ranking, and the development of evaluation systems for summarization tasks. Her research also extends to graph theory applications in data mining and cross-lingual transfer learning.

Acknowledgments

The authors express their appreciation to Mohamad Abu Jafar and Samer Abo Hasan for their valuable help with data collection and annotation. We also thank Yossef Haim Shrem for his valuable advice and help with taxonomy translation. The subject of this program is the development of a dataset and a language model for identifying offensive language in Hebrew and Arabic.

  1. Research funding: This work was supported by the Israeli Innovation Authority.

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Received: 2024-08-28
Accepted: 2024-11-18
Published Online: 2024-12-10
Published in Print: 2024-12-17

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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