A Survey of Available Techniques for Hate Speech Detection in Social Media

While social media platforms provide a prominent space for users to participate in interpersonal conversations and express their viewpoints, the anonymity and façade afforded by these platforms may enable users to disseminate hate speech and objectionable information. Due to the extensive size of these platforms, there is a need to automatically detect and mark occurrences of hate speech. While there are several approaches available for detecting hate speech, the majority of these methods are intentionally designed to be non-interpretable or unexplainable. In this work, we want to overcome the problem of not being able to understand the results clearly. To do this, we suggest using advanced Large Language Models (LLMs) to extract certain characteristics from the input text. These characteristics, called rationales, will be used to train a basic hate speech classifier. This approach will ensure that the results are easily understandable and accurate. Our system successfully integrates the linguistic comprehension powers of LLMs with the discerning strength of cutting-edge hate speech classifiers to ensure that these classifiers are accurately interpretable.