# Leveraging Advanced NLP Techniques and Data Augmentation to Enhance Online Misogyny Detection
## 2 Related Work  
The following section will provide an overview of related research on online misogyny, including methods used. In addition, the limitations of these approaches will be highlighted. Many recent studies used different deep-learning methods to detect online misogyny. A multimodal framework for detecting sexist and misogynistic content on social media is presented in [19]. The graph neural networks and attention processes consist of the Multimodal Attention module (MANM), the Graph-based Feature Reconstruction module (GFRM), and the Content-specific Features Learning module (CFLM), with the use of graphs for feature refinement and specialized modules for learning modality-specific aspects like toxicity and caption features in texts and images, it uses context-aware attention to concentrate on pertinent features across text and visual modalities. Two datasets, MAMI and MMHS150K, were used to evaluate the framework's performance. The suggested approach significantly outperformed current methods in terms of macro-Fl scores, achieving 11.87% on MAMI and 10.82% on MMHS150K; however, the study has some limitations, including the difficulty of interpreting irony or sarcasm in sexist content and the possible requirement to adjust to new lexicons or idioms that develop over time. The authors in [20] proposed a novel neural architecture for the multi-label classification of sexism and misogyny on social media. Their work differentiates between various forms of sexism and misogyny by deploying a combination of sentence representations derived from word embeddings and general-purpose pre-trained models such as BERT, utilizing techniques like convolutional and recurrent neural operations. They addressed the multi-label nature of sexism by experimenting with different loss functions and problem transformation techniques, leading to improved detection rates compared to traditional machine-learning and baseline deep-learning models. In [21], the study explored methods for detecting misogynistic language in Urban Dictionary entries by comparing various machine-learning approaches. It employed two advanced deep-learning models--Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU)--and evaluated their effectiveness against more traditional techniques like Logistic Regression, Naive Bayes, and Random Forest. The findings demonstrated that deep-learning models--BiGRU in particular--performed noticeably better, attaining the highest rates of accuracy and sensitivity. However, the study highlighted notable challenges, such as the complexity of identifying subtle, context-dependent misogynistic language, which can reduce the effectiveness of machine learning models. The authors in [22] introduced a MULTILATE, a multimodal hate detection system aimed at identifying digital misogyny, especially from social media texts and memes. Their research leverages Convolutional Neural Network (CNN) paired with Bidirectional Long Short-Term Memory (BiLSTM) for textual analysis and Residual Network 50 (ResNet50) combined with text-based analysis for detecting sexist elements in memes. Their findings from testing on the EXIST 2024 dataset show that combining these methodologies improves the precision and effectiveness of detecting sexist content. However, the study highlighted the difficulties in reading complex multimodal data, as well as the necessity for substantial model training and tweaking to manage the subtle presentations of sexism across different media forms. While the majority of previous works focused on deep-learning models, others used traditional machine learning or a combination of deep learning and machine learning. The authors in [23] examine several automated methods for identifying posts that are sexist on social media, especially Twitter. They concentrate on deep-learning methods, including Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN), as well as traditional machine-learning models like Support Vector Machines, Naive Bayes, andLogistic Regression.