AU _ NLP at S em E val-2023 Task 10: Explainable Detection of Online Sexism Using Fine-tuned R o BERT a (2023)

Abstract: Social media is a concept developed to link people and make the globe smaller. But it has recently developed into a center for sexist memes that target especially women. As a result, there are more events of hostile actions and harassing remarks present online. In this paper, we introduce our system for the task of online sexism detection, a part of SemEval 2023 task 10. We introduce fine-tuned RoBERTa model to address this specific problem. The efficiency of the proposed strategy is demonstrated by the experimental results reported in this research.

## introduction discriminatory ideas and sentiments, particularly those directed at women, are frequently found online and on social media. this typically indicates the presence of additional hazardous content types like hate speech @xcite or misinformation @xcite . it might be difficult to identify such remarks because sexism and misogyny can take many different forms and vary across linguistic and cultural boundaries. sexism is one of the most critical difficulties because it may greatly harm users. as an illustration, objectification, a kind of sexism, has been linked to serious risks for eating disorders, unipolar depression, and sexual dysfunction @xcite . a study published by fox et al. @xcite requested participants to retweet or post tweets with sexist content before conducting a set of activities meant to uncover sexist behaviors. additionally, since the anonymity of twitter can encourage people to demonstrate an even greater sexist behavior, the authors came to the conclusion that the users who were protected by the anonymity of a social media profile were more likely than non-anonymous users to exhibit aggressive sexism. sexism is defined by the oxford english dictionary as prejudice, stereotyping or discrimination, typically against women, on the basis of sex. foot_0 sexist attitudes and languages undervalue the contribution of women. furthermore, considering that a sizeable portion of internet users, particularly those who utilize social networks, are teenagers, the rise in online sexism necessitates urgent research and social debate that results in action @xcite . however, detecting online sexism may be difficult, as it may be expressed in very different forms. in this study, we focus mainly on using natural language processing (nlp) methods and state-ofthe-art models for two important tasks: (i) binary sexism detection, which aims to determine whether a given sentence contains any sexist content; and (ii) fine-grained sexism classification, which aims to further identify which class a sexist sentence belongs to. in the specific context of this work, we seek to solve the issue of sexism identification and classification in social media posts using a fully autonomous, nlp technique. this is a pressing issue that has not been adequately addressed previously @xcite @xcite , and the suggested solution is scalable and does not rely on inputs from humans directly. the structure of this work is as follows: section 2 lists some significant research in the detection and classification of sexism; section 3 describes the task; section 4 describes the details of the dataset used here; section 5 describes the fundamental architecture of transformer models and how they are used for identifying and categorizing sexism as well as the key steps of the methodology used; sec-tion 6 contains the primary findings and analyses of this study and in section 7 the work is concluded, providing suggestions for future works. ## related work a lot of work has been done in recent years to identify hate speech, including tasks like identifying racist or xenophobic content, but very few of these studies have addressed sexism detection, and in particular, they have dealt with sexism as the identification of hate speech against women @xcite . however, several concepts and methods from the identification of hate speech may be applicable to our issue @xcite . so, in this section, we briefly discuss related research in the realm of hate speech as well as earlier studies on the identification of sexism and misogyny. there have been several approaches to classify hate speech spreaders online. bag-of-words (bow) techniques were the foundation for the first efforts on hate speech identification @xcite @xcite . one of the earliest studies, published in 2012, used machine learning-based classifiers @xcite rather than pattern-based techniques @xcite for detecting abusive language. to identify hate speech, many traditional machine learning techniques have been used, including decision trees @xcite , logistic regression @xcite , and support vector machines. recently das et al. @xcite used bert-tfidf based approach for profiling irony and stereotype spreading authors. there are various methods that use specific types of sentiment data as features. peter burnap and colleagues utilized a dictionary-based method to find cyberhatred on twitter. they employed an n-gram feature engineering method to create the numeric vectors using a specified vocabulary of offensive words. njagi dennis et al. @xcite used an ml-based classifier to categorize hate speech in internet forums and blogs. the authors choose to build the master feature vector using a dictionarybased method. the use of emotive language as well as semantic and subjective components were influenced by the focus on hate speech @xcite . the resulting feature vector was then input to a rule-based classifier. misogyny typically connotes the display of anger and hatred toward women, albeit it is not always the same as sexism @xcite . the term 'expressions of hate towards women' is misogyny @xcite , although the term 'sexism' also refers to more subtly veiled implicit forms of abuse and prejudice that can nevertheless significantly affect women. when defining sexism, glick and fiske @xcite ) distinguish between two types: benevolent sexism and aggressive sexism. benevolent sexism is more subtle with traits that appear to be beneficial, whereas aggressive sexism is characterized by an overtly negative attitude toward women. sexism can take many different forms, such as direct, indirect, descriptive, or recorded behavior (such as stereotyping, ideological disagreements, sexual aggression, etc.) @xcite . misogyny is thus just one example of sexism @xcite . the majority of earlier research has focused more on identifying hostile and explicit sexism while ignoring covert or implicit sexism @xcite @xcite . as a result, it is important to deal with the identification of sexism in different sexist attitudes and behaviors because these are the most prevalent and harmful to society @xcite . hate speech detection is related to recent studies on the detection of sexism. one of the first datasets was created to investigate the relationship between sexism and racism @xcite . however, this dataset ignores other forms of sexism and only includes instances of hatred or aggressive sexism directed towards women. a classification of sexism by sharifirad and jacovi @xcite included direct, sexual, and physical forms of sexism. a more recent study by @xcite aims to classify sexism reports. other jobs to protect women from hatred on the internet have emerged as a result of the increased interest in hate detection towards women. for instance, sexist meme detection @xcite and sexist advertisement classification @xcite are two examples. the use of neural models to detect hate speech has drawn interest in recent years. these models frequently employ deep learning techniques like convolutional neural networks (cnns) and long short-term memory networks (lstms), achieving outstanding results in a variety of natural language processing applications (pitsilis et al., 2018; badjatiya et al., 2017; zimmerman et al., 2018; park and fung, 2017). rahgouy et al. (rahgouy et al., 2022) recently used stylistically fused contextualized representation and deep learning for sarcasm detection. in this study, we combine several of these approaches to address the issue. ## task the task @xcite deals with identifying online sexism in english and offers a system to evaluate the effectiveness of the automated solutions. the task contains three subtasks: task a, task b and task c, explained in figure 1 . 1. task a -binary sexism detection: at the first level, the taxonomy divides content into two categories, sexist and non-sexist. any maltreatment of women based on their gender or on the combination of their gender with one or more additional identification features (such as black women or muslim women), whether explicit or implicit, is referred to as sexist content. the dataset with labels had 20,000 entries, 10,000 of them were drawn from reddit and 10,000 from @xcite . for labeling them, three trained annotators first labeled each entry, and one of two experts then decided on any differences. the task contains three tasks: task a, task b and task c. when all of the annotators agreed on one label for task a, that label is considered to be the gold label. one of the experts evaluated the entry and chose the gold label if there was any disagreement. when two or more annotators agreed on a label for tasks b and c, the label was considered the gold label; however, if there was a three-way tie, one of the experts determined the gold label. the experts and annotators were all self-identified women. task a is a classification task between 'sexist' and 'non sexist' posts. for task a, 14000 posts for training, 2000 for validation and 4000 for testing were used. the training dataset was imbalanced. out of 14000 posts used for training, 3398 posts were labeled as 'sexist', remaining 10602 as 'non sexist'. for task b, the 'sexist' posts were further classified into four sub categories and for task c, these four categories were further classified into tables 1, 2 and 3 show the dataset splits for tasks a, b and c respectively. ## system overview recently, natural language processing has seen a rise in popularity of pretrained language models (lms). a pre-trained language model has the drawback of taking a long time during sentence pair regression processes like clustering and sentence similarity analysis @xcite . the sentence can be embedded to remedy the issue. recently, many sentence embedding techniques with varied generating mechanisms have been proposed. in this study, we have implemented some models that are listed below. ## conclusion in this paper we presented our method to the se-meval 2023 explainable detection of online sexism task to address the sexism detection problem on gab and reddit data. the task contained three subtasks -one for binary sexism classification, one for category of sexism classification and one for finegrained vector of sexism classification. to address the tasks, we implemented fine tuned roberta model and compared it to several other models like bert, sbert, bert combined with tfidf etc. the results show that our model gave the best macro-f1 score. to conclude, we have shown some a very useful technique for online sexism detection. how this model behaves to a different type of dataset, will be a future direction to explore.