Hate Speech Detection in Social Media for the Kurdish Language

Saeed, Ari M.; Ismael, Aso N.; Rasul, Danya L.; Majeed, Rayan S.; Rashid, Tarik A.

doi:10.1007/978-3-031-14054-9_24

Cited by 7 publications

(6 citation statements)

References 12 publications

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“…Many papers use the binary classes by only labeling the samples with two labels. Hate or Not hate is the most common binary label used in literature, such as Alshaalan and Al-Khalifa ( 2020 ), Aref et al ( 2020 ), Omar et al ( 2020 ), Romim et al ( 2021 ), Saeed et al ( 2022 ), and Khezzar et al ( 2023 ). Others used different labels for binary classification, such as clean or offensive (Alsafari et al, 2020b ; Alsafari and Sadaoui, 2021 ), hateful or normal (Salomon et al, 2022 ).…”

Section: Literature Reviewmentioning

confidence: 99%

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Hate speech detection in the Arabic language: corpus design, construction, and evaluation

Ahmad,

Azzeh,

Alnagi

et al. 2024

Front. Artif. Intell.

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Hate Speech Detection in Arabic presents a multifaceted challenge due to the broad and diverse linguistic terrain. With its multiple dialects and rich cultural subtleties, Arabic requires particular measures to address hate speech online successfully. To address this issue, academics and developers have used natural language processing (NLP) methods and machine learning algorithms adapted to the complexities of Arabic text. However, many proposed methods were hampered by a lack of a comprehensive dataset/corpus of Arabic hate speech. In this research, we propose a novel multi-class public Arabic dataset comprised of 403,688 annotated tweets categorized as extremely positive, positive, neutral, or negative based on the presence of hate speech. Using our developed dataset, we additionally characterize the performance of multiple machine learning models for Hate speech identification in Arabic Jordanian dialect tweets. Specifically, the Word2Vec, TF-IDF, and AraBert text representation models have been applied to produce word vectors. With the help of these models, we can provide classification models with vectors representing text. After that, seven machine learning classifiers have been evaluated: Support Vector Machine (SVM), Logistic Regression (LR), Naive Bays (NB), Random Forest (RF), AdaBoost (Ada), XGBoost (XGB), and CatBoost (CatB). In light of this, the experimental evaluation revealed that, in this challenging and unstructured setting, our gathered and annotated datasets were rather efficient and generated encouraging assessment outcomes. This will enable academics to delve further into this crucial field of study.

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Section: Literature Reviewmentioning

confidence: 99%

“…Table 2 illustrates information about previous research that collected corpora in low-resource languages, such as Turkish (Beyhan et al, 2022 ; Coban et al, 2023 ), Kurdish (Saeed et al, 2022 ), and Bangali (Romim et al, 2021 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Hate speech detection in the Arabic language: corpus design, construction, and evaluation

Ahmad,

Azzeh,

Alnagi

et al. 2024

Front. Artif. Intell.

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“…The Kurdish language is one of the official languages in Iraq and has regional status in Iran. The language has 40 million speakers [4] . Central Kurdish (Sorani) and Northern Kurdish (Kurmanji) are two of the main dialects of the Kurdish language [5] .…”

Section: Data Descriptionmentioning

confidence: 99%

Kurdish News Dataset Headlines (KNDH) through multiclass classification

Badawi¹,

Saeed

Ahmed

et al. 2023

Data in Brief

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“…Many papers use the binary classes by only labeling the samples with two labels. Hate or Not hate is the most common binary label used in literature, such as Alshaalan and Al-Khalifa (2020), Aref et al (2020), Omar et al (2020), Romim et al (2021), Saeed et al (2022), and Khezzar et al (2023). Others used different labels for binary classi cation, such as clean or offensive (Alsafari et al, 2020b;Alsafari and Sadaoui, 2021), hateful or normal (Salomon et al, 2022).…”

Section: Hate Speech and Related Conceptsmentioning

confidence: 99%

Hate Speech Detection in the Arabic Language: Corpus Design, Construction and Evaluation

Ahmad,

Azzeh,

Alnagi

et al. 2023

Preprint

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Hate Speech Detection in Arabic presents a multifaceted challenge due to the broad and diverse linguistic terrain. With its multiple dialects and rich cultural subtleties, Arabic requires particular measures to address hate speech online successfully. To address this issue, academics and developers have used natural language processing (NLP) methods and machine learning algorithms adapted to the complexities of Arabic text. However, many proposed methods were hampered by a lack of a comprehensive dataset/corpus of Arabic hate speech. In this research, we propose a novel multi-class public Arabic dataset comprised of 403,688 annotated tweets categorized as extremely positive, positive, neutral, or negative based on the presence of hate speech. Using our developed dataset, we additionally characterize the performance of multiple machine learning models for Hate speech identification in Arabic Jordanian dialect tweets. Specifically, the Word2Vec, TF-IDF, and AraBert text representation models have been applied to produce word vectors. With the help of these models, we can provide classification models with vectors representing text. After that, seven Machine learning classifiers have been evaluated: Support Vector Machine (SVM), Logistic Regression (LR), Naive Bays (NB), Random Forest (RF), AdaBoost (Ada), XGBoost (XGB), and CatBoost (CatB). In light of this, the experimental evaluation revealed that, in this challenging and unstructured setting, our gathered and annotated datasets were rather efficient and generated encouraging assessment outcomes. This will enable academics to delve further into this crucial field of study.

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Hate Speech Detection in Social Media for the Kurdish Language

Cited by 7 publications

References 12 publications

Hate speech detection in the Arabic language: corpus design, construction, and evaluation

Hate speech detection in the Arabic language: corpus design, construction, and evaluation

Kurdish News Dataset Headlines (KNDH) through multiclass classification

Hate Speech Detection in the Arabic Language: Corpus Design, Construction and Evaluation

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