@inproceedings{makram-etal-2022-chillax,
title = "{CHILLAX} - at {A}rabic Hate Speech 2022: A Hybrid Machine Learning and Transformers based Model to Detect {A}rabic Offensive and Hate Speech",
author = "Makram, Kirollos and
Nessim, Kirollos George and
Abd-Almalak, Malak Emad and
Roshdy, Shady Zekry and
Salem, Seif Hesham and
Thabet, Fady Fayek and
Mohamed, Ensaf Hussien",
editor = "Al-Khalifa, Hend and
Elsayed, Tamer and
Mubarak, Hamdy and
Al-Thubaity, Abdulmohsen and
Magdy, Walid and
Darwish, Kareem",
booktitle = "Proceedinsg of the 5th Workshop on Open-Source Arabic Corpora and Processing Tools with Shared Tasks on Qur'an QA and Fine-Grained Hate Speech Detection",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.osact-1.25",
pages = "194--199",
abstract = "Hate speech and offensive language have become a crucial problem nowadays due to the extensive usage of social media by people of different gender, nationality, religion and other types of characteristics allowing anyone to share their thoughts and opinions. In this research paper, We proposed a hybrid model for the first and second tasks of OSACT2022. This model used the Arabic pre-trained Bert language model MARBERT for feature extraction of the Arabic tweets in the dataset provided by the OSACT2022 shared task, then fed the features to two classic machine learning classifiers (Logistic Regression, Random Forest). The best results achieved for the offensive tweet detection task were by the Logistic Regression model with accuracy, precision, recall, and f1-score of 80{\%}, 78{\%}, 78{\%}, and 78{\%}, respectively. The results for the hate speech tweet detection task were 89{\%}, 72{\%}, 80{\%}, and 76{\%}.",
}
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<abstract>Hate speech and offensive language have become a crucial problem nowadays due to the extensive usage of social media by people of different gender, nationality, religion and other types of characteristics allowing anyone to share their thoughts and opinions. In this research paper, We proposed a hybrid model for the first and second tasks of OSACT2022. This model used the Arabic pre-trained Bert language model MARBERT for feature extraction of the Arabic tweets in the dataset provided by the OSACT2022 shared task, then fed the features to two classic machine learning classifiers (Logistic Regression, Random Forest). The best results achieved for the offensive tweet detection task were by the Logistic Regression model with accuracy, precision, recall, and f1-score of 80%, 78%, 78%, and 78%, respectively. The results for the hate speech tweet detection task were 89%, 72%, 80%, and 76%.</abstract>
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%0 Conference Proceedings
%T CHILLAX - at Arabic Hate Speech 2022: A Hybrid Machine Learning and Transformers based Model to Detect Arabic Offensive and Hate Speech
%A Makram, Kirollos
%A Nessim, Kirollos George
%A Abd-Almalak, Malak Emad
%A Roshdy, Shady Zekry
%A Salem, Seif Hesham
%A Thabet, Fady Fayek
%A Mohamed, Ensaf Hussien
%Y Al-Khalifa, Hend
%Y Elsayed, Tamer
%Y Mubarak, Hamdy
%Y Al-Thubaity, Abdulmohsen
%Y Magdy, Walid
%Y Darwish, Kareem
%S Proceedinsg of the 5th Workshop on Open-Source Arabic Corpora and Processing Tools with Shared Tasks on Qur’an QA and Fine-Grained Hate Speech Detection
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F makram-etal-2022-chillax
%X Hate speech and offensive language have become a crucial problem nowadays due to the extensive usage of social media by people of different gender, nationality, religion and other types of characteristics allowing anyone to share their thoughts and opinions. In this research paper, We proposed a hybrid model for the first and second tasks of OSACT2022. This model used the Arabic pre-trained Bert language model MARBERT for feature extraction of the Arabic tweets in the dataset provided by the OSACT2022 shared task, then fed the features to two classic machine learning classifiers (Logistic Regression, Random Forest). The best results achieved for the offensive tweet detection task were by the Logistic Regression model with accuracy, precision, recall, and f1-score of 80%, 78%, 78%, and 78%, respectively. The results for the hate speech tweet detection task were 89%, 72%, 80%, and 76%.
%U https://aclanthology.org/2022.osact-1.25
%P 194-199
Markdown (Informal)
[CHILLAX - at Arabic Hate Speech 2022: A Hybrid Machine Learning and Transformers based Model to Detect Arabic Offensive and Hate Speech](https://aclanthology.org/2022.osact-1.25) (Makram et al., OSACT 2022)
ACL