# Leveraging Advanced NLP Techniques and Data Augmentation to Enhance Online Misogyny Detection
## 2 Related Work
The findings show that using these technologies to filter and maybe stop hate speech online is feasible. The difficulty of differentiating between contextually sophisticated misogynistic expressions and non-misogynistic use of similar language is one of the research's drawbacks. Given the relatively short text sequences in our dataset, such as Twitter posts, LSTM models are well-suited for our task despite their known limitations in handling long-term dependencies. While LSTMs can struggle with retaining information over extremely long sequences due to issues like vanishing gradients, memory saturation, and inefficient learning of long-range dependencies [28]. Other studies used Transformer models for the detection of online misogyny. The authors in [29] explored the use of BERT for detecting aggressive and misogynistic content in social media. They employed a multi-task learning approach that tackles both aggression identification and misogynistic aggression identification simultaneously. For aggression, texts were classified into three categories--Not Aggressive, Covertly Aggressive, and Overtly Aggressive. For misogyny, texts were categorized as either Gendered or Non-Gendered. The proposed method achieved a weighted F1-measure of 0.8579 on the English misogynistic. However, the study acknowledged limitations in distinguishing between aggression types, particularly between Covertly Aggressive and Not Aggressive content, due to the subtlety and indirectness of covert aggression. The work done by [30] explored the potential of large language models (LLMs), specifically GPT-3.5, to assist in assessing online misogyny. Their research utilized a prompt-based learning approach to analyze a dataset of Reddit posts. This method involved designing and testing different prompt structures to examine response stability, classification accuracy, and inter-annotator agreement. The study demonstrated that zero-shot detection with GPT-3.5 could outperform supervised baselines, with accuracy enhancements observed when ensembling multiple prompts. However, the research also highlighted limitations such as the variation in response due to different prompt structures and the challenges associated with achieving consistent human-like annotations, especially in ambiguous or context-dependent instances of misogynistic content. The studies outlined provide valuable insights into the use of Transformer models, large language models, and machine-learning approaches to detect misogynistic content on digital platforms. These techniques demonstrate a broad range of capabilities, from achieving high accuracy in identifying explicit instances of misogyny to advancing the detection of more nuanced and context-dependent expressions. However, significant limitations arise from these investigations, raising concerns about the efficacy and scalability of these technologies. Key challenges include biases in datasets, the evolving nature of language on social media, and the substantial volume of training data required for effective model development. Additionally, linguistic and cultural variations in the expression of misogyny often restrict the generalizability of these models across diverse contexts.