# Hate Speech Detection by Using Rationales for Judging Sarcasm
## 8 Conclusions and Future Work  
This study introduces a novel dataset derived from the established HateXplain dataset, comprising 3199 posts extracted from Gab and Twitter. In the original HateXplain dataset, each post is annotated with labels such as normal, hate, or offensive, along with target communities (e.g., black, Muslim, LGBTQ+) and rationale marked by annotators. The data from HateXplain were re-labeled based on sarcasm by our annotators, with rationales selected based on their identification of sarcastic content. Additionally, a new methodology is proposed for calculating ground truth attention when the data exhibit both toxic (hate/offensive) content and sarcasm, aiming to enhance the decision-making process of models. This approach was tested on the BERT model, which incorporates sarcasm along with hate/offensive data to improve hate speech detection, particularly for toxic comments disguised within sarcasm. The results demonstrate that utilizing sarcasm to identify concealed toxic content led to a slight improvement in the performance metrics, while also providing plausible and faithful rationales that enhanced the model's decision-making capabilities.  
Looking ahead, the plan is to explore the application of other Large Language Models (LLMs) to our methodology. Given the complexity and resource requirements of LLMs, this extension was not feasible within the current study's time frame. Future work will focus on comparing the efficacy and generalizability of our approach using a range of LLMs. This step is essential for verifying our model's robustness across various architectures and settings, providing deeper insights into its adaptability and performance. We also  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & **Text** & & **Label** \\ \hline HateXplain Annotator & user he is infected with jhadi virus & he will spread it to others & HS \\ \hline Our Annotator & user he is infected with jhadi virus & he will spread it to others & SARC \\ \hline Baseline & user he is infected with jhadi virus & he will spread it to others & OF \\ \hline Proposed Method & user he is infected with jhadi virus he will spread it to others & HS \\ \hline \hline \end{tabular}  
* The \
\begin{tabular}{l} **orange highlight** marks what the HateXplain [13] annotators found important for the prediction. The **underlined words**/tokens \\ \end{tabular} were identified by the model as important using LIME [25], and the
\begin{tabular}{l} **line highlight** marks the highest probability \\ tokens from this set for both the baseline and proposed method. (OF—offensive speech; HS—hatespeech; SARC—sarcasm).  
\end{table}
Table 3: Example of the rationales predicted by baseline and proposed method compared to human annotators.  
aim to incorporate context information into data annotation and utilize it in our model training process.  
Conceptualization, M.B.M.; methodology, M.B.M.; software, M.B.M.; validation, M.B.M.; formal analysis, M.B.M.; investigation, M.B.M.; resources, M.B.M.; data curation, M.B.M.; writing--original draft preparation, M.B.M., with suggestions from T.T., M.N. ([PERSON]) and M.N. ([PERSON]); writing--review and editing, M.B.M., T.T., M.N. ([PERSON]) and M.N. ([PERSON]); visualization, M.B.M.; supervision, T.T., M.N. ([PERSON]) and M.N. ([PERSON]); project administration, M.B.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are not publicly available due to privacy or ethical restrictions.  
The authors declare no conflict of interest.