  This paper envisions a multi-agent system for detecting the presence of hate
speech in online social media platforms such as Twitter and Facebook. We
introduce a novel framework employing deep learning techniques to coordinate
the channels of textual and im-age processing. Our experimental results aim to
demonstrate the effectiveness of our methods for classifying online content,
training the proposed neural network model to effectively detect hateful
instances in the input. We conclude with a discussion of how our system may be
of use to provide recommendations to users who are managing online social
networks, showcasing the immense potential of intelligent multi-agent systems
towards delivering social good.
