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Turkish Journal of Electrical Engineering and Computer Sciences

Author ORCID Identifier

TOYGAR TANYEL: 0000-0002-2421-6880

BESHER ALKURDI: 0009-0003-9807-9537

SERKAN AYVAZ: 0000-0003-2016-4443

DOI

10.55730/1300-0632.4105

Abstract

With the proliferation of social media, there has been a sharp increase in offensive content, particularly targeting vulnerable groups, exacerbating social problems such as hatred, racism, and sexism. Detecting offensive language use is crucial to prevent offensive language from being widely shared on social media. However, the accurate detection of irony, implication, and various forms of hate speech on social media remains a challenge. Natural language-based deep learning models require extensive training with large, comprehensive, and labeled datasets. Unfortunately, manually creating such datasets is both costly and error-prone. Additionally, the presence of human-bias in offensive language datasets is a major concern for deep learning models. In this paper, we propose a linguistic data augmentation approach to reduce bias in labeling processes, which aims to mitigate the influence of human bias by leveraging the power of machines to improve the accuracy and fairness of labeling processes. This approach has the potential to improve offensive language classification tasks across multiple languages and reduce the prevalence of offensive content on social media.

Keywords

offensive language, deep learning, contextual models, data mining, data-augmentation, linguistics

First Page

829

Last Page

848

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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