论文标题
对基于机器学习的自动仇恨语音检测的挑战的审查
A Review of Challenges in Machine Learning based Automated Hate Speech Detection
论文作者
论文摘要
仇恨言论在社交媒体领域的传播目前是一个严重的问题。对这些平台上生成的大量信息的不符合访问导致人们发布和反应起源于暴力行为的有毒内容。尽管已经努力在线检测和限制此类内容，但准确识别它仍然具有挑战性。基于深度学习的解决方案一直处于识别可恶内容的最前沿。但是，诸如仇恨言论的上下文依赖性的因素，用户的意图，不希望的偏见等。使这个过程过度批评。在这项工作中，我们通过提出这些问题的层次结构组织来深入探讨自动仇恨言论检测的广泛挑战。我们专注于机器学习或基于深度学习的解决方案所面临的挑战。在顶级，我们区分数据级别，模型级别和人类级别的挑战。我们进一步提供了每个层次结构级别的详尽分析。这项调查将帮助研究人员在仇恨言论检测领域更有效地设计其解决方案。
The spread of hate speech on social media space is currently a serious issue. The undemanding access to the enormous amount of information being generated on these platforms has led people to post and react with toxic content that originates violence. Though efforts have been made toward detecting and restraining such content online, it is still challenging to identify it accurately. Deep learning based solutions have been at the forefront of identifying hateful content. However, the factors such as the context-dependent nature of hate speech, the intention of the user, undesired biases, etc. make this process overcritical. In this work, we deeply explore a wide range of challenges in automatic hate speech detection by presenting a hierarchical organization of these problems. We focus on challenges faced by machine learning or deep learning based solutions to hate speech identification. At the top level, we distinguish between data level, model level, and human level challenges. We further provide an exhaustive analysis of each level of the hierarchy with examples. This survey will help researchers to design their solutions more efficiently in the domain of hate speech detection.
