ACSMKRHR at S em E val-2023 Task 10: Explainable Online Sexism Detection( EDOS ) (2023)

Abstract: People are expressing their opinions online for a lot of years now. Although these opinions and comments provide people an opportunity of expressing their views, there is a lot of hate speech that can be found online. More specifically, sexist comments are very popular affecting and creating a negative impact on a lot of women and girls online. This paper describes the approaches of the SemEval-2023 Task 10 competition for Explainable Online Sexism Detection (EDOS). The task has been divided into 3 subtasks, introducing different classes of sexist comments. We have approached these tasks using the bert-cased and uncased models which are trained on the annotated dataset that has been provided in the competition. Task A provided the best F1 score of 80% on the test set, and tasks B and C provided 58% and 40% respectively.

## introduction sexism is prejudice or discrimination based on one's sex or gender. sexism can affect anyone, but it primarily affects women and girls who face negative sentiment or abuse based on their gender combined with one or more other identity attributes (e.g. black women, muslim women, trans women). it has been linked to stereotypes and gender roles and may include the belief that one sex or gender is intrinsically superior to another(wikipedia contributors, 2023). social media platforms have promoted the spread of hate speech through anonymization and accessibility, spurring greater study into developing automatic algorithms to recognize these sorts of writings. according to a study @xcite on online harassment, women experience harassment due to their gender twice as often as males do. bullying online can lead to depression, moreover, a study @xcite related to rape cases found a connection between the number of sexist tweets and the number of rapes in the united states of america. an online poll conducted by amnesty international across eight high-income countries in 2017 revealed that 23% of women had experienced some form of abuse or harassment on social media platforms(center on gender equity and health (geh) -uc san diego, 2023). it can cause harm to targeted women, render online places inhospitable and inaccessible, and maintain societal inequities and asymmetries. social media text can be inconsistent and mixed with sarcasm often times which can make it challenging to detect sexist texts. the categorization of sexism differs from and may be complemented by the detection of hate speech. although sexist comments may be regarded as hate speech and there is a lot of research conducted regarding hate speech detection, sexism sentences have a broader aspect, and studies on sexism detection have a huge scope. despite using various automated tools in the digital space to identify and flag high-level sexist content, however, they fail to recognize potential content and explain the cause behind it. the capacity to identify sexist content and to explain why it is sexist enhances the interpretability, trust, and comprehension of the choices made by automated systems, giving users and moderators more control. it is crucial to be able to improve the automatic detection and classification of sexism. it may aid in the analysis of sexism in order to enhance sensitization campaigns and implement other countermeasures towards this oppression. this research is based on the classification of sexist data and its different categories. firstly, we target a binary classification of a sentence indicating whether it is sexist. secondly, if it is classified as sexist, we further look into fine-grained sexism categories. we have dealt with both labeled (14k) and large amounts of unlabelled data(2m) and used the bert transformer model to classify and categorize the texts. ## results if we compare the results based on the f1 score of from the we can see the detailed comparison of the result between the model trained on the raw dataset and the model trained on the active learning labeled dataset in the figures 4. here, we compared the confusion matrix of the best-performing model among confidence scores 85, 90, and 95 using active learning labeled dataset and model using raw dataset. we could see from figure 4 shows confusion matrices for task a. figure 4(a) shows that the model trained on the raw dataset predicts 1379 non-sexist and 352 sexist data correctly. however, figure 4(b) shows the model trained on the active learning labeled dataset predicted 1371 non-sexist and 363 sexist data cor-rectly. compared to the model trained with raw data it predicts the sexist data more correctly than the non-sexist data. the results of task b can be seen in figure 5 where in figure 5(a) the raw dataset trained model, the derogation class classifies 162 data correctly but misclassifies some into the animosity class. the animosity class also correctly predicted 94 data which is notable. the other two classes were mostly correctly classified. for active learning labeled data trained model in figure 5(b) shows that the derogation and animosity classes are mostly wrongly classified which are 43 and 61 respectively. both of the models get confused between these two classes. however, the model using the active learning labeled dataset misclassifies every class item more than the model trained on the raw dataset. from the confusion matrix shown in figure 6 for task c in figure 6(a) , we can see class-2, 3, 4, and 6 are somewhat classified correctly. classes 1, 8, 9, and 11 are mostly misclassified. significantly, the model fails to classify class 8, the "condescending explanations or unwelcome advice" class and classifies most of them to other classes. moreover, class 9 'supporting mistreatment of individual women' classifies the data as another class instead of its own class label. we can see that in figure 6(b) , the correct classification data number for the model using active learning labeled data is less than the model using raw data. for example, for class 3 the raw data model predicts 65 correct data whereas the other predicts only 55 correct. it mostly misclassified them to class 7 and class 9. but the raw data model misclassified them into class 4 mostly. therefore, it is seen that the model using the raw data performs well than the model using active learning labeled data in task c too. however, due to the fine-grained classification in task c, our main model fails to distinguish them with better results leaving room for improvement. ## conclusion in conclusion, this paper describes our approach for the shared task on explainable detection of online sexism (edos) by semeval 2023. a largely finegrained classified labeled data imposed challenges that we tried to resolve using existing nlp models. although there is room for improvement and further experiment, using a pre-trained multilingual-bert model our approach obtained promising results. in the future, additional pre-processing steps might also improve the results along with further experimenting with different recurrent models and tuning the hyperparameters with different settings.