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Arabic offensive language on twitter: Analysis and experiments

Title	Arabic offensive language on twitter: Analysis and experiments
Author	Mubarak, H., Rashed, Ammar, Darwish, K., Samih, Y., Abdelali, A.
Publication Date:	2021
Publication Place	- Association for Computational Linguistics (ACL)
Type	Document
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	978-195408509-1
Record ID	4ba44e73-6907-4603-929c-e8667dbff1c9
Date	2021
Sample Text	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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Özyeğin University

Arabic offensive language on twitter: Analysis and experiments

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

Publication Date 2021

Publication Place - Association for Computational Linguistics (ACL)

Type Document

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 978-195408509-1

Record ID 4ba44e73-6907-4603-929c-e8667dbff1c9

Date 2021

Sample Text 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.