Machine Learning for Hate Speech Detection in Arabic Social Media

Boulouard, Zakaria; Ouaissa, Mariya; Ouaissa, Mariyam

doi:10.1007/978-3-030-77185-0_10

Cited by 6 publications

(4 citation statements)

References 18 publications

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“…The results proved that the Precision and the Recall of the BERT-based models are almost alike. This means that these models are not as biased as the baseline models, such as LSTM [43] and LinearSVC [44], and perform equally well for both the positive and the negative comments.…”

Section: Resultsmentioning

confidence: 95%

“…Taking a deeper look at why BERT-based models outperformed other language models such as LSTM [43] and LinearSVC [44], in the past, conventional language models could only interpret text input sequentially -either from right to left or from left to right -but not simultaneously. BERT is unique since it can simultaneously read in both directions.…”

Section: Resultsmentioning

confidence: 99%

“…In the following, the ap will be referred to as "mBERTAR"; • Using AraBERT [30], a BERT-based model trained on Arabic text, on the comments in Arabic. In the following, the approach will be referred to as "Ara In a previous study [43], we compared the performance of the following Shall Deep Learning algorithms:…”

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confidence: 99%

“…In a previous study [43], we compared the performance of the following Shallow and Deep Learning algorithms: In that study, we found that LSTM provided the best performance, so we will keep it as a candidate in this current study.…”

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confidence: 99%

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Detecting Hateful and Offensive Speech in Arabic Social Media Using Transfer Learning

Boulouard

Ouaissa

et al. 2022

Applied Sciences

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The democratization of access to internet and social media has given an opportunity for every individual to openly express his or her ideas and feelings. Unfortunately, this has also created room for extremist, racist, misogynist, and offensive opinions expressed either as articles, posts, or comments. While controlling offensive speech in English-, Spanish-, and French- speaking social media communities and websites has reached a mature level, it is much less the case for their counterparts in Arabic-speaking countries. This paper presents a transfer learning solution to detect hateful and offensive speech on Arabic websites and social media platforms. This paper will compare the performance of different BERT-based models trained to classify comments as either abusive or neutral. The training dataset contains comments in standard Arabic as well as four dialects. We will also use their English translations for comparative purposes. The models were evaluated based on five metrics: Accuracy, Precision, Recall, F1-Score, and Confusion Matrix.

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Section: Resultsmentioning

confidence: 95%

Section: Resultsmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Detecting Hateful and Offensive Speech in Arabic Social Media Using Transfer Learning

Boulouard

Ouaissa

et al. 2022

Applied Sciences

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A comprehensive review on Arabic offensive language and hate speech detection on social media: methods, challenges and solutions

Abdelsamie,

Azab,

Hefny

2024

Soc. Netw. Anal. Min.

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arHateDetector: detection of hate speech from standard and dialectal Arabic Tweets

Khezzar

Moursi

Aghbari

2023

Discov Internet Things

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Hate speech has become a phenomenon on social media platforms, such as Twitter. These websites and apps that were initially designed to facilitate our expression of free speech, are sometimes being used to spread hate towards each other. In the Arab region, Twitter is a very popular social media platform and thus the number of tweets that contain hate speech is increasing rapidly. Many tweets are written either in standard, dialectal Arabic, or mix. Existing work on Arabic hate speech are targeted towards either standard or single dialectal text, but not both. To fight hate speech more efficiently, in this paper, we conducted extensive experiments to investigate Arabic hate speech in tweets. Therefore, we propose a framework, called arHateDetector, that detects hate speech in the Arabic text of tweets. The proposed arHateDetector supports both standard and several dialectal Arabic. A large Arabic hate speech dataset, called arHateDataset, was compiled from several Arabic standard and dialectal tweets. The tweets are preprocessed to remove the unwanted content. We investigated the use of recent machine learning and deep learning models such as AraBERT to detect hate speech. All classification models used in the investigation are trained with the compiled dataset. Our experiments shows that AraBERT outperformed the other models producing the best performance across seven different datasets including the compiled arHateDataset with an accuracy of 93%. CNN and LinearSVC produced 88% and 89% respectively.

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Machine Learning for Hate Speech Detection in Arabic Social Media

Cited by 6 publications

References 18 publications

Detecting Hateful and Offensive Speech in Arabic Social Media Using Transfer Learning

Detecting Hateful and Offensive Speech in Arabic Social Media Using Transfer Learning

A comprehensive review on Arabic offensive language and hate speech detection on social media: methods, challenges and solutions

arHateDetector: detection of hate speech from standard and dialectal Arabic Tweets

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