K ingsman T rio at S em E val-2023 Task 10: Analyzing the Effectiveness of Transfer Learning Models for Explainable Online Sexism Detection (2023)

Abstract: Online social platforms are now propagating sexist content endangering the involvement and inclusion of women on these platforms. Sexism refers to hostility, bigotry, or discrimination based on gender, typically against women. The proliferation of such notions deters women from engaging in social media spontaneously. Hence, detecting sexist content is critical to ensure a safe online platform where women can participate without the fear of being a target of sexism. This paper describes our participation in subtask A of SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS). This subtask requires classifying textual content as sexist or not sexist. We incorporate a RoBERTa-based architecture and further finetune the hyperparameters to entail better performance. The procured results depict the competitive performance of our approach among the other participants.

## introduction the free flow of information is the most fascinating feature of social media that has enabled sharing of information, thoughts, and opinions across the world. but this very feature is now used as a tool to incite hatred and threats against individuals or communities. our society has always exploited women with prejudice, stereotype, and discrimination. the creation and proliferation of social media have provided our society with a more extensive platform to induce such unjust treatment against women. people can post sexist content anonymously using fake and untraceable identities without the fear of accountability. moreover, the hatred can be spread to the wider community impeding the integration of women into the social network. so it is essential to detect such sexism in social media content so that women can partake and collaborate in social media without being mistreated and threatened. with all of the authors have equal contributions. ## proposed framework for the aim of detecting explainable online sexism, we described our tested models in this part. various transfer learning and deep learning models were explored in this aspect. roberta-sexism-classifier: the roberta-sexism-classifier is a well-liked and adaptable architecture that makes use of an enhanced bidirectional encoder mechanism. it can understand more complex intricate relationships between words and phrases because it was trained on a broader and more diverse corpus. we fine-tuned the roberta pre-trained transformer-based architecture with the explainable online sexism dataset to achieve our goal. the overall fine-tuned process is depicted in figure 1 . an input text is initially tokenized using the robertatokenizer @xcite . all of the tokens were padded to the same sequence length. it is then fed to the first transformer layer, and the output of that layer is fed to the second. this procedure continues until the transformer layer 24's output is obtained. we used the robertaforse-quenceclassification @xcite technique to obtain the final prediction. ## discussion we explored numerous deep learning and transfer learning models in order to estimate and identify the most effective strategy. in we look into the cause of our suggested system's incorrect predicted labels further. our proposed model was able to identify the fact that the first example used derogatory language against women and the use of the word "bitch" is a form of gender-based harassment towards women, which contributes to a culture of sexism and misogyny. however, in the second example, the text makes a generalization using a double standard meaning that women are not treated equally when it comes to speaking about men versus women. here, the term "chauvinist asshole" further reinforces the sexist nature of the text. but, because our system was unable to comprehend the significance of this double standard, it made a false forecast. even though the third example has the gold label "not sexist," our methodology predicts "sexist" due to its rude and demeaning character towards a certain gender. the term "only two genders" can be seen as being dismissive and exclusionary since it suggests that non-binary people do not exist. also violent and unpleasant is the idea that someone is forced to drink entire bottles of coke. to elaborate further, we can say that our method failed to detect 123 out of 486 sexist posts. this may be attributed to the fact that there is an insufficient number of sexist posts in the training dataset. ## conclusion and future notion in this paper, we traversed several transformerbased architectures and deep learning models. we further conducted a performance analysis of these models and investigated the mispredictions of the top-performing model. in the future, we intend to incorporate domain-specific features along with other transformer architectures. moreover, as social media content are not limited to text only, we aspire to detect sexism in other modalities too.