# Hate Speech Detection by Using Rationales for Judging Sarcasm
###### Abstract  
The growing number of social media users has impacted the rise in hate comments and posts. While extensive research in hate speech detection attempts to combat this phenomenon by developing new datasets and detection models, reconciling classification accuracy with broader decision-making metrics like plausibility and faithfulness remains challenging. As restrictions on social media tighten to stop the spread of hate and offensive content, users have adapted by finding new approaches, often camouflaged in the form of sarcasm. Therefore, dealing with new trends such as the increased use of emoticons (negative emoticons in positive sentences) and sarcastic comments is necessary. This paper introduces sarcasm-based rationale (emoticons or portions of text that indicate sarcasm) combined with hate/offensive rationale for better detection of hidden hate comments/posts. A dataset was created by labeling texts and selecting rationale based on sarcasm from the existing benchmark hate dataset, HateXplain. The newly formed dataset was then applied in the existing state-of-the-art model. The model's F1-score increased by 0.01 when using sarcasm rationale with hate/offensive rationale in a newly formed attention proposed in the data's preprocessing step. Also, with the new data, a significant improvement was observed in explainability metrics such as plausibility and faithfulness. hate speech; sarcasm; rationale +
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profanity or derogatory remarks made in a general context without the intention to incite violence or discrimination against a group. In this paper, a clear distinction between these two types of content is maintained to tailor the model's response appropriately. Not only is the presence of hate or offense identified, but also the context and the target, which are considered crucial for effective moderation on social platforms. This approach allows for more nuanced detection and response strategies, which are essential for maintaining the integrity and inclusivity of online communication spaces. Some studies [14; 15; 16; 17] have proposed models for improving automatic hate speech detection. However, these models often lack inductive reasoning due to an overestimation of state-of-the-art approaches for hate speech detection [18; 19]. These advanced models typically rely on triggered vocabularies for predictions, leading to substantial evidence of racial bias in major published hate speech datasets [20; 21]. Moreover, as hate speech detection models become more complex, it becomes increasingly challenging to clarify their decision-making process [22]. To address these concerns, a benchmark dataset named \"HateXplain\" [13] was introduced. This dataset is unique in that each data point is annotated with labels indicating whether the content is hate, offensive, or normal, along with details such as the mentioned target communities and text snippets known as rationales [23]. These rationales, which are identified by human annotators, provide context that is instrumental in teaching the model not only to recognize hate speech but also to understand the context that characterizes such language, enhancing both the robustness and interpretability of the model. During the training phase, these rationales inform a specialized attention mechanism within a neural network architecture, focusing the model's attention more on words and phrases within these rationales. This targeted learning approach helps the model to prioritize the most critical attributes that indicate offensive/hate speech, leading to improved accuracy and reliability in classifications. Furthermore, the use of rationales extends into the operational phase of the model. Here, the trained model applies the patterns it has learned to new, unseen texts. When it identifies words or phrases that mirror the rationales from the training dataset, the model scrutinizes these pieces of text with heightened attention.