Arabic offensive language on twitter: Analysis and experiments

Name: Arabic offensive language on twitter: Analysis and experiments
Author: Mubarak, H., Rashed, Ammar, Darwish, K., Samih, Y., Abdelali, A.

İsim	Arabic offensive language on twitter: Analysis and experiments
Yazar	Mubarak, H., Rashed, Ammar, Darwish, K., Samih, Y., Abdelali, A.
Basım Tarihi:	2021
Basım Yeri	- Association for Computational Linguistics (ACL)
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-195408509-1
Kayıt Numarası	4ba44e73-6907-4603-929c-e8667dbff1c9
Tarih	2021
Örnek Metin	Detecting offensive language on Twitter has many applications ranging from detecting/predicting bullying to measuring polarization. In this paper, we focus on building a large Arabic offensive tweet dataset. We introduce a method for building a dataset that is not biased by topic, dialect, or target. We produce the largest Arabic dataset to date with special tags for vulgarity and hate speech. We thoroughly analyze the dataset to determine which topics, dialects, and gender are most associated with offensive tweets and how Arabic speakers use offensive language. Lastly, we conduct many experiments to produce strong results (F1 = 83.2) on the dataset using SOTA techniques.

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Arabic offensive language on twitter: Analysis and experiments

Yazar Mubarak, H., Rashed, Ammar, Darwish, K., Samih, Y., Abdelali, A.

Basım Tarihi 2021

Basım Yeri - Association for Computational Linguistics (ACL)

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-195408509-1

Kayıt Numarası 4ba44e73-6907-4603-929c-e8667dbff1c9

Tarih 2021

Örnek Metin Detecting offensive language on Twitter has many applications ranging from detecting/predicting bullying to measuring polarization. In this paper, we focus on building a large Arabic offensive tweet dataset. We introduce a method for building a dataset that is not biased by topic, dialect, or target. We produce the largest Arabic dataset to date with special tags for vulgarity and hate speech. We thoroughly analyze the dataset to determine which topics, dialects, and gender are most associated with offensive tweets and how Arabic speakers use offensive language. Lastly, we conduct many experiments to produce strong results (F1 = 83.2) on the dataset using SOTA techniques.