A Review of Challenges in Machine Learning based Automated Hate Speech Detection
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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.
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