Classification of Different Participating Entities in the Rise of Hateful Content in Social Media

Abstract

Hateful content is a growing concern across different platforms, whether it is a moderated platform or an unmoderated platform. The public expression of hate speech encourages the devaluation of minority members. It has some consequences in the real world as well. In such a scenario, it is necessary to design AI systems that could detect such harmful entities/elements in online social media and take cautionary actions to mitigate the risk/harm they cause to society. The way individuals disseminate content on social media platforms also deviates. The content can be in the form of texts, images, videos, etc. Hence hateful content in all forms should be detected, and further actions should be taken to maintain the civility of the platform. We first introduced two published works addressing the challenges of detecting low-resource multilingual abusive speech and hateful user detection. Finally, we discuss our ongoing work on multimodal hateful content detection.