Understanding and Detecting Dangerous Speech in Social Media

Ali Alshehri, El Moatez Billah Nagoudi, Muhammad Abdul-Mageed


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.
Anthology ID:
2020.osact-1.6
Volume:
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
Editors:
Hend Al-Khalifa, Walid Magdy, Kareem Darwish, Tamer Elsayed, Hamdy Mubarak
Venue:
OSACT
SIG:
Publisher:
European Language Resource Association
Note:
Pages:
40–47
Language:
English
URL:
https://aclanthology.org/2020.osact-1.6
DOI:
Bibkey:
Cite (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.
Cite (Informal):
Understanding and Detecting Dangerous Speech in Social Media (Alshehri et al., OSACT 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.osact-1.6.pdf