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.

中文翻译：

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迈向在线仇恨语音检测的多智能体系统

本文设想了一种多代理系统，用于检测诸如Twitter和Facebook之类的在线社交媒体平台中仇恨言论的存在。我们介绍了一种采用深度学习技术的新颖框架，以协调文本和图像处理的渠道。我们的实验结果旨在证明我们的在线内容分类方法的有效性，训练提出的神经网络模型以有效检测输入中的仇恨实例。最后，我们讨论了如何使用我们的系统向正在管理在线社交网络的用户提供建议，从而展示了智能多代理系统在交付社交产品方面的巨大潜力。