# Hate Speech Detection by Using Rationales for Judging Sarcasm
###### Abstract
This method not only improves the accuracy of classification but also ensures that each decision made by the model can be traced back to clear, understandable linguistic evidence, thereby enhancing the transparency and accountability of automated hate speech detection systems. Thus, incorporating rationales into the training and operational phases of model bridges the gap between traditional text analysis and a deep, contextual understanding of language used in hate speech. This not only enhances the model's performance but also ensures that each decision made by the model is transparent and explainable, which is crucial for applications in sensitive areas such as online content moderation. While recent studies have highlighted flaws in previous hate speech detection methods, including issues with model performance and datasets, the evolving nature of language and the constant emergence of new ways social media users spread hate speech pose ongoing challenges. Platforms like Twitter or Facebook often inspire users to employ figurative language in conveying their opinions. This includes the concealment of hate comments under the guise of sarcasm [24], a common form of passive-aggressive speech. Therefore, sarcasm represents a crucial aspect for enhancing hate speech detection. However, as far as the knowledge of this study extends, there has not been any previous research in this sector specifically addressing hate concealed under the pretext of sarcasm. As a solution to this problem, this research proposes a new dataset that also focuses on sarcasm alongside hate. For the study, 3199 data points were selected from the benchmark HateXplain dataset [13] and annotators were hired to label them based on sarcasm as well as choose the accompanying rationale. LIME [25] was utilized for generating important tokens as rationale to assist in the classification task. The BERT model, as documented in [26], gives significance to the HateXplain data rationales provided by human annotators,but it tends to misclassify the label (offensive speech--OF). Notably, for this study's data, BERT assigned the label (hate speech--HS). This study aimed to enhance hate speech detection by incorporating a sarcasm-based rationale in addition to the conventional hate/offensive rationale. The main objectives were to develop a novel methodology for integrating these rationales into the existing model frameworks and to assess the impact of this integration on model performance, specifically in terms of classification accuracy and explainability. The growing prevalence of online hate speech necessitates robust detection mechanisms that can effectively handle the nuances of language, including sarcasm, which is often used to disguise hateful intents. By addressing these challenges, the research seeks to contribute to safer online environments and more effective moderation tools. The contributions of this research are as follows:  
1. A new dataset for hate speech detection is presented, comprising two kinds of labeled categories: (i) sarcastic/non-sarcastic; and (ii) hate speech/offensive/normal. Rationale selection is based on whether it fits into the categories of sarcastic and hate/offensive;
2. The sarcasm rationale is incorporated in cases where the data are both sarcastic and hate speech/offensive. This involves preprocessing steps before utilizing it in the model's attention calculation;
3. The inclusion of sarcasm-based rationale alongside hate speech/offensive-based rationale improved both the classification and explainability metrics of the BERT model.