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ABMM: Arabic BERT-Mini Model for Hate-Speech Detection on Social Media by Malik Almaliki; Abdulqader M. Almars; Ibrahim Gad; El-Sayed Atlam is a Engineering article available to read on EtoBox.

What is ABMM: Arabic BERT-Mini Model for Hate-Speech Detection on Social Media about?

Hate speech towards a group or an individual based on their perceived identity, such as ethnicity, religion, or nationality, is widely and rapidly spreading on social media platforms. This causes harmful impacts on users of these platforms and the quality of online shared content. Fortunately, researchers have developed different machine learning algorithms to automatically detect hate speech on social media platforms. However, most of these algorithms focus on the detection of hate speech that appears in English. There is a lack of studies on the detection of hate speech in Arabic due to the language’s complex nature. This paper aims to address this issue by proposing an effective approach for detecting Arabic hate speech on social media platforms, namely Twitter. Therefore, this paper introduces the Arabic BERT-Mini Model (ABMM) to identify hate speech on social media. More specifically, the bidirectional encoder representations from transformers (BERT) model was employed to analyze data collected from Twitter and classify the results into three categories: normal, abuse, and hate speech. In order to evaluate our model and state-of-the-art approaches, we conducted a series of exp

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Author

Malik Almaliki; Abdulqader M. Almars; Ibrahim Gad; El-Sayed Atlam

Publisher

MDPI AG

Published

2023

Language

EN

Field

Engineering (Physical Sciences)