Academy & Industry Research Collaboration Center (AIRCC)

Volume 9, Number 2, February 2019

Detection of Hate Speech in Social Networks: A Survey on Multilingual Corpus


Areej Al-Hassan and Hmood Al-Dossari, King Saud University, Saudi Arabia


In social media platforms, hate speech can be a reason of “cyber conflict” which can affect social life in both of individual-level and country-level. Hateful and antagonistic content propagated via social networks has the potential to cause harm and suffering on an individual basis and lead to social tension and disorder beyond cyber space. However, social networks cannot control all the content that users post. For this reason, there is a demand for automatic detection of hate speech. This demand particularly raises when the content is written in complex languages (e.g. Arabic). Arabic text is known with its challenges, complexity and scarcity of its resources. This paper will present a background on hate speech and its related detection approaches. In addition, the recent contributions on hate speech and its related anti-social behaviour topics will be reviewed. Finally, challenges and recommendations for the Arabic hate speech detection problem will be presented


Text Mining, Social Networks, Hate Speech, Natural Language Processing, Arabic NLP