P o S h at S em E val-2023 Task 10: Explainable Detection of Online Sexism (2023)

Abstract: To precisely identify the different forms of online sexism, we utilize several sentence transformer models such as ALBERT, BERT, RoBERTa, DistilBERT, and XLNet. By combining the predictions from these models, we can generate a more comprehensive and improved result. Each transformer model is trained after pre-processing the data from the training dataset, ensuring that the models are effective at detecting and classifying instances of online sexism. For Task A, the model had to classify the texts as sexist or not sexist. We implemented ALBERT, an NLP-based sentence transformer. For task B, we implemented BERT, RoBERTa, DistilBERT and XLNet and took the mode of predictions for each text as the final prediction for the given text. For task C, we implemented ALBERT, BERT, RoBERTa, DistilBERT and XLNet and took the mode of predictions as the final prediction for the given text.

## introduction in today's technology-driven world, it has become increasingly common for users to utilize social media platforms to spread hatred and engage in sexist behaviour. therefore, addressing online sexism is a critical way to create a safe online space for everyone, where people can express their opinions without fear of discrimination or harassment. online sexism is a form of gender-based discrimination or harassment that occurs in online spaces, including social media platforms, online forums, chat rooms, and messaging apps. it can take many forms, from overly sexist comments, hashtags @xcite and slurs in the forms of discrimination, such as dehumanizing language, gender-based stereotypes, and microaggressions. further, it can have a significant impact on the mental and emotional status of individuals who experience it. also, it can lead to feelings of shame, anxiety, and depression, as well as physical symptoms such as headaches and insomnia. online sexism can be directed at individuals or groups of people based on their gender, sexual orientation, race, religion, or other personal characteristics. women are particularly vulnerable to online sexism as they are more likely to be targeted with derogatory comments and threats of violence, and overall experience higher levels of online harassment. given the rising issue of online sexism, it is essential to develop effective strategies for detecting and addressing the same. one promising approach is to use natural language processing (nlp) transformers to analyze social media data and identify instances of online sexism. we have participated and submitted our solutions for all three subtasks. the goal of these tasks is to enhance the accuracy and explainability of english-language models for detecting online sexism. the main focus is to create fine-grained classifications for sexist content on gab and reddit (based on training data). by developing more accurate and explainable models, we can improve our ability to identify, address and prohibit online sexism, creating safer and more inclusive online exposure. our main strategy for detecting online sexism was to utilize various nlp sentence transformers, including albert, bert, roberta, distilbert, and xlnet. the first step of our approach involved cleaning the dataset of all unwanted characters. after filtering out the data, it was used as input for each of the transformer models. to generate comprehensive and accurate results, we combined each of the predictions from all the transformer models. this enabled us to identify instances of online sexism with greater accuracy and provide a more nuanced understanding of its different forms. overall, participating in this task highlights the need for continued research and development of automated tools to effectively detect the slightest forms of online sexism. our paper is structured as follows: in section 2, we provide detailed background work on the problem of online sexism and existing research on the topic. in section 3, we describe the nlp sentence transformer models we utilized in our approach. section 4 presents the experimental setup we used to evaluate the performance of our models, also providing a comparative analysis of our results and discussing the strengths and limitations of our approach. finally, in section 5, we conclude our work by summarizing our findings and highlighting the implications of our work. ## results this section presents the analysis of the results and submitted official results. ## conclusion this paper presents the submitted runs for the task 'explainable detection of online sexism (edos)' in semeval 2023. we experimented with different approaches such as a bert model, albert, roberta base, distilbert, and xlnet and ensemble models. our team's submission had macro f1 scores of 0.7937 for subtask a, 0.5284 for subtask b and 0.2674 for subtask c. our team placed 66th on the leaderboard for task a, 57th for task b and 53rd for task c. for future work, we will explore other ensembling techniques like averaging the probabilities of the models. additionally, we will experiment with external resources and employ different data augmentation techniques to enhance the performance of our model.