## 3 Materials and Methods
### Explainability Analysis  
In this section, we introduce the symbols and parameters used throughout our explainability methodology, followed by a detailed description of the techniques applied. Table 2 shows the definitions of symbols and parameters used in our model. We utilized SHAP to integrate the explainability component into our methodology. These values are instrumental in understanding the contribution of each token to the model's predictions. SHAP generates scores indicative of the importance of a token in the prediction [35]. SHAP values are based on cooperative game theory and provide a method to attribute the output of the model to its input features. The application of SHAP values allows us to identify the key factors influencing the classification process. By analyzing these values, we can assign scores to the most influential tokens based on their perceived impact, leading to a better understanding of the textual content. The SHAP value for each token is calculated as follows: let \(S_{t}\) denote the SHAP value for token \(t\), \(T\) be the set of all tokens, \(T\setminus\{t\}\) be a subset of tokens excluding \(t\), and \(f(T\setminus\{t\}\cup\{t\})\) and \(f(T\setminus\{t\})\) be the model predictions with and without the token \(t\), respectively. The SHAP value \(S_{t}\) is computed as:  
\[S_{t}=\sum_{T\setminus\{t\}\subseteq T}\frac{|T\setminus\{t\}|!(|T|-|T \setminus\{t\}|-1)!}{|T|!}[f(T\setminus\{t\}\cup\{t\})-f(T\setminus\{t\})] \tag{1}\] \begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Description** \\ \hline \(t\) & Token in the sentences \\ \(S_{t}\) & SHAP value for token \(t\) \\ \(T\) & Set of all tokens \\ \(f(t)\) & Model prediction without the token \(t\) \\ SI\({}_{t}\) & SHAP Importance for token \(t\) \\ \(N_{t}\) & Number of sentences in which token \(t\) appears \\ \(S_{t}(i)\) & SHAP value for token \(t\) in the \(i\)-th sentence \\ \(\mu\) & Mean of the SHAP scores \\ \(\sigma\) & Standard deviation of the SHAP scores \\ IR\({}_{t}\) & Importance Ratio for token \(t\) \\ CI\({}_{k}\) & Cumulative Importance up to the \(k\)-th token \\ \(T_{c}\) & Threshold for cumulative importance, set to 0.95 \\ Label\((i)\) & Modified predicted hard label indicator for sentence \(i\) \\ SS\((i)\) & Sexsim Score for sentence \(i\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Definitions of symbols and parameters. Here, the term \(\frac{|\Gamma\backslash\{t\}|!|(|\Gamma|-|\Gamma\backslash\{t\}|-1)!}{|\Gamma|! }\) represents the weight assigned to the difference in model outputs, ensuring that the contribution of token \(t\) is fairly distributed among all possible combinations of tokens. This weight is derived from the concept of Shapley values in cooperative game theory, which ensures a fair distribution of the total gain (or loss) among all contributors. To determine the most influential tokens based on SHAP values, we first calculate the SHAP importance for each token \(t\) across all samples where the token appears in the set of all tokens \(T\) in the sentences from \(i=1\) to \(N_{t}\), which represent all sentences in the dataset. \(S_{i}(t)\) represents the SHAP value for token \(t\) in the \(i\)-th sentence. The SHAP importance for token \(t\), denoted as \(\text{SI}_{t}\), is computed as:  
\[\text{SI}_{t}=\frac{1}{N_{t}}\sum_{i=1}^{N_{t}}|S_{t}(i)|\cdot\mathbb{I}(y_{i} =\hat{y}_{i}) \tag{2}\]  
In this formula, \(N_{t}\) is the number of sentences in which token \(t\) appears, and \(S_{t}(i)\) is the SHAP value for token \(t\) in the \(i\)-th sentence. The term \(\mathbb{I}(y_{i}=\hat{y}_{i})\) is an indicator function that equals 1 if the predicted class \(\hat{y}_{i}\) matches the true class \(y_{i}\), and 0 otherwise. This ensures that we only consider SHAP values from sentences where the model's prediction is correct, thereby focusing on the tokens that truly influence accurate predictions. We remove data points that fall outside 99.7% of the SHAP value distribution to exclude outliers.