@inproceedings{alshehri-etal-2020-understanding,
title = "Understanding and Detecting Dangerous Speech in Social Media",
author = "Alshehri, Ali and
Nagoudi, El Moatez Billah and
Abdul-Mageed, Muhammad",
editor = "Al-Khalifa, Hend and
Magdy, Walid and
Darwish, Kareem and
Elsayed, Tamer and
Mubarak, Hamdy",
booktitle = "Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resource Association",
url = "https://aclanthology.org/2020.osact-1.6",
pages = "40--47",
abstract = "Social media communication has become a significant part of daily activity in modern societies. For this reason, ensuring safety in social media platforms is a necessity. Use of dangerous language such as physical threats in online environments is a somewhat rare, yet remains highly important. Although several works have been performed on the related issue of detecting offensive and hateful language, dangerous speech has not previously been treated in any significant way. Motivated by these observations, we report our efforts to build a labeled dataset for dangerous speech. We also exploit our dataset to develop highly effective models to detect dangerous content. Our best model performs at 59.60{\%} macro F1, significantly outperforming a competitive baseline.",
language = "English",
ISBN = "979-10-95546-51-1",
}
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<abstract>Social media communication has become a significant part of daily activity in modern societies. For this reason, ensuring safety in social media platforms is a necessity. Use of dangerous language such as physical threats in online environments is a somewhat rare, yet remains highly important. Although several works have been performed on the related issue of detecting offensive and hateful language, dangerous speech has not previously been treated in any significant way. Motivated by these observations, we report our efforts to build a labeled dataset for dangerous speech. We also exploit our dataset to develop highly effective models to detect dangerous content. Our best model performs at 59.60% macro F1, significantly outperforming a competitive baseline.</abstract>
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%0 Conference Proceedings
%T Understanding and Detecting Dangerous Speech in Social Media
%A Alshehri, Ali
%A Nagoudi, El Moatez Billah
%A Abdul-Mageed, Muhammad
%Y Al-Khalifa, Hend
%Y Magdy, Walid
%Y Darwish, Kareem
%Y Elsayed, Tamer
%Y Mubarak, Hamdy
%S Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection
%D 2020
%8 May
%I European Language Resource Association
%C Marseille, France
%@ 979-10-95546-51-1
%G English
%F alshehri-etal-2020-understanding
%X Social media communication has become a significant part of daily activity in modern societies. For this reason, ensuring safety in social media platforms is a necessity. Use of dangerous language such as physical threats in online environments is a somewhat rare, yet remains highly important. Although several works have been performed on the related issue of detecting offensive and hateful language, dangerous speech has not previously been treated in any significant way. Motivated by these observations, we report our efforts to build a labeled dataset for dangerous speech. We also exploit our dataset to develop highly effective models to detect dangerous content. Our best model performs at 59.60% macro F1, significantly outperforming a competitive baseline.
%U https://aclanthology.org/2020.osact-1.6
%P 40-47
Markdown (Informal)
[Understanding and Detecting Dangerous Speech in Social Media](https://aclanthology.org/2020.osact-1.6) (Alshehri et al., OSACT 2020)
ACL
- Ali Alshehri, El Moatez Billah Nagoudi, and Muhammad Abdul-Mageed. 2020. Understanding and Detecting Dangerous Speech in Social Media. In Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection, pages 40–47, Marseille, France. European Language Resource Association.