# Hate Speech Detection by Using Rationales for Judging Sarcasm
## 2 Related Works  
The proliferation of online platforms has significantly increased incidents of hate speech, impacting vulnerable individuals and communities profoundly [3; 5; 6; 27]. Comprehensive studies [4; 28; 29; 30] have documented these effects, emphasizing the urgent need for effective detection mechanisms. Current research primarily focuses on developing computational models to detect explicit and implicit hate speech [11; 12; 13]. However, traditional methods often overlook nuanced expressions such as sarcasm and indirect speech that can mask hateful intentions [31; 32; 33], a critical gap highlighted in the literature. This study addresses this by enhancing model sensitivity to not only detect overt hate speech but also subtler, sarcastic expressions often overlooked by current methodologies [14; 15; 16; 17; 34; 35]. **Dataset**: Previous datasets designed to detect hate speech primarily focus on expressions related to hate, encompassing abusive comments, hate speech, offensive language, and disrespectful content. A common issue in much of the preceding research is the tendency to conflate hate speech with abusive or offensive language [34]. Furthermore, some studies merged offensive and hate language into a single category, while only a limited number [34; 36] made efforts to distinguish offensive from hate speech. In the published HateXplain dataset, subjectivity is recognized as a pivotal aspect, acknowledging that numerous messages may be offensive without qualifying as hate speech. For example, in the United States, the term \"nigga\" is commonly used in online language by the Black community in a manner that is not necessarily intended to be malevolent toward another individual [35]. Building on the categorization framework that separates hate from offensive language [34], the HateXplain dataset adopts three classes: hate, offensive, and normal. Moreover, HateXplain incorporates the concept of using rationales [23]. Since human annotators are required to highlight a span of text supporting their labeling decision, this enriches the rationale annotation process. As a result, HateXplain provides the first benchmark hate speech dataset with human-level explanations, enhancing interpretability and transparency. Despite the progress made by HateXplain in addressing major issues related to hate speech, a contemporary challenge persists--the use of sarcasm as a disguise for spreading hate speech [24]. Given the non-physical and implicit nature of online violence, it often appears subtle. Social media platforms frequently host stereotypical, racial, and gender jokes, sometimes disguised as sarcasm. While seemingly harmless, the repetition of such jokes can lead to negative psychological effects, resembling a form of cyberbullying [37; 38]. Despite existing research on sarcasm detection [39; 40] and multi-modal detection [41], there is a noticeable gap--no research has explored the connection between hate and sarcasm for automatic hate speech detection. In our work, assuming hate is hidden under the guise of sarcasm, and rationale can provide human-level explanations, we collected data from HateXplain, which is already labeled with rationale based on hate/offensive/normal for new dataset creation. This new dataset is different from existing datasets in that it introduces two categories, and based on each category (sarcasm and hate/offensive), we provide rationale for human-level explanations. **Model:** Despite numerous models claiming to achieve state-of-the-art performance on specific datasets, their ability to generalize is often questionable [18; 19]. Challenges emerge when these models misclassify comments related to identities frequently under attack (e.g., gay, black, and/or Muslim) as toxic even when lacking any malicious intent [42]. The prevalence of biased predictions, stemming from an overemphasis on specific trigger vocabulary, can result in further discrimination against groups already targeted by online abuse [20; 21].