An Evaluation of the Automatic Detection of Hate Speech in Social Media Networks

Abstract

Numerous approaches have been developed over recent years to detect hate speech on social media networks. Nevertheless, a great deal of what is generally recognized as hate speech cannot yet be detected. There remain many challenges to assuring the effectiveness and reliability of automatic detection systems in different languages, including Arabic. Social media platforms and networks such as Facebook continue to encounter difficulties regarding the automatic detection of hate speech in Arabic content. Given the importance of developing reliable artificial intelligence and automatic detection systems that can reduce the problems and crimes associated with the spread of hate speech on social media platforms, this study is concerned with evaluating the performance of the automatic detection and tracking of hate speech in Arabic content on Facebook. As an example, the study evaluates the period in October 2020 that came to be known as France’s cartoon controversy. Two different corpora were designed. The first corpus comprised 347 posts deleted by Facebook, now known as Meta. The second corpus was composed of 1,856 posts that were randomly selected using the hashtag إلا رسول الله (except the Prophet of Allah). The results indicate that there is a considerable amount of hate speech taken from or influenced by the Islamic religious discourse, but that automatic detection systems are unable to address the peculiar linguistic features of Arabic. There is also a lack of clarity in defining what constitutes “hate speech”. The study suggests that social media networks, including Facebook, need to adopt more reliable automatic detection systems that consider the linguistic properties of Arabic. Political thinkers and religious scholars should be involved in defining what constitutes hate speech in Arabic.