## 3 Materials and Methods
### Data Preparation and Augmentation  
The first step in the data-preparation process was to convert all of the text to lowercase so that the analysis could recognize words uniformly. Additionally we removed URLs, special characters and punctuation. Then, we applied tokenization and lemmatization to divide the text into units.  
We applied various augmentation strategies due to limited data availability in certain classes and the necessity to develop more robust models. Using techniques like 'random deletion' to remove random words trained the model to understand incomplete data, and techniques such as'synonym replacement' for word and phrase replacement expanded the model's understanding of context. Different spellings were also used to simulate errors found in the real world. The dataset was significantly imbalanced, particularly in Task A, where 76% of the training data was 'Not sexist' (10,602 instances) and only 24% was 'Sexist' (3398 instances). This imbalance was similarly reflected in the test data and persisted across Task B and Task C categories. For example, some classes in Task C, like 'Threats of harm', had only 56 training instances, representing a mere 2% of the data. The training dataset contained 14,000 records. After augmentation, this number increased to 42,000 records, significantly enhancing the data volume available for training.  
To further address the class imbalance, we used back translation to increase the number of'sexist' texts. This means we first translated the'sexist' texts into Dutch and then back into English. Dutch was selected for this purpose based on previous research indicating its efficacy for such tasks and due to its linguistic similarity to English, as both languages belong to the West Germanic language family [40]. Figure 1 illustrates an example of our back translation augmentation approach.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Category** & **Records** \\ \hline
**Task C** & \\
1.1 Threats of harm & 56 \\
1.2 Incretement and encouragement of harm & 254 \\
2.1 Descriptive attacks & 717 \\
2.2 Aggressive and emotive attacks & 673 \\
2.3 Dehumanising attacks and overt sexual objectification & 200 \\
3.1 Casual use of gendered slurs, profanities, and insults & 637 \\
3.2 Immutable gender differences and gender stereotypes & 417 \\
3.3 Backhanded gendered compliments & 64 \\
3.4 Condescending explanations or unwelcome advice & 47 \\
4.1 Supporting mistreatment of individual women & 75 \\
4.2 Supporting systemic discrimination against women as a group & 258 \\
**Total** & **3398** \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: Back translation method for data augmentation (English \(\leftrightarrow\) Dutch).  
To further address the class imbalance, we used some other techniques like stratified K-fold cross-validation ([[https://scikit-learn.org/stable/modules/generated/sklearn.model_selection_StratifiedKFold.html](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection_StratifiedKFold.html)]([https://scikit-learn.org/stable/modules/generated/sklearn.model_selection_StratifiedKFold.html](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection_StratifiedKFold.html)), (accessed on 15 May 2024)) method to ensure an accurate class distribution throughout the data segments. Strategies including RandomOverSampler, SMOTE ([[https://imbalanced-learn.org/stable/references/generated/imblearn.over_sampling.SMOTE.html](https://imbalanced-learn.org/stable/references/generated/imblearn.over_sampling.SMOTE.html)]([https://imbalanced-learn.org/stable/references/generated/imblearn.over_sampling.SMOTE.html](https://imbalanced-learn.org/stable/references/generated/imblearn.over_sampling.SMOTE.html)), (accessed on 15 May 2024)), and differential class weighting were used during training to address the class imbalance [41].