J udith J eyafreeda at S em E val-2023 Task 10: Machine Learning for Explainable Detection of Online Sexism (2023)

Abstract: The rise of the internet and social media platforms has brought about significant changes in how people interact with each another. For a lot of people, the internet have also become the only source of news and information about the world. Thus due to the increase in accessibility of information, online sexism has also increased. Efforts should be made to make the internet a safe space for everyone, irrespective of gender, both from a larger social norms perspective and legal or technical regulations to help alleviate online gender-based violence. As a part of this, this paper explores simple methods that can be easily deployed to automatically detect online sexism in textual statements.

## introduction the internet is not an equal space. over the past few years there has been a rise in concerns about the disproportionate levels of abuse experienced by women in social media platforms. online abuse can take different forms including bullying, stalking, impersonation, non-consensual pornography, revenge porn or image-based sexual abuse/exploitation, and most commonly, hate speech against women or online misogyny @xcite ). a study @xcite ) has shown that women who experience online abuse often adapt their online behaviour, self-censor the content they post and limit interactions on the platform out of fear of violence and abuse however, despite these there still exists a gap to bridge. @xcite explains the need for identifying and stopping online sexism. one of the reason being: online gender-based violence can have significant psychological, social, and economic impacts. most directly, it affects women's freedom of expression. thus in this task we aim at automatically identifying online sexism (gender based abusive statements) by taking advantage of machine learning methods. in particular, this work explores certain machine learning methods to identify and classify sexist statements in texts into predefined categories. this constitutes a multiclass classification problem.3. ## related work sexism detection can be characterized as hate speech detection. several works have been done in this area of research @xcite , @xcite , @xcite , @xcite ]. there are several accounts of sexist content on major platforms such as twitter, motivating the development of models for better detecting and classifying social media posts. thus, most works focus on developing methods for identifying and classifying text in social media platforms @xcite , @xcite , @xcite ]. most state-of-the-art methodologies can be summarized with the following methods: (i) methods using lexicons (ii) deep learning methods, which are more generic but lack domain knowledge (iii) a combination of lexicons and deep learning methods, which is a hybrid method. over the years, several research work have been conducted on this topic using transformers based models. bert @xcite roberta @xcite , electra @xcite and gpt2 @xcite are few of such models that have achieved good results in this area. the authors of @xcite ) develop a multi class classification model of sexist content. this is similar to the second and third sub tasks discussed in this paper. the authors propose a model that uses both the outputs of a bert model and linguistic word embeddings. the authors of (de paula and da silva, 2022), use transformers for multilingual classification of sexist content. the authors build on the work of @xcite . the models are developed and tested for the datasets provided by @xcite , where the models were developed for both english and spanish languages. the english language models have a high f1 score, while the spanish model hasn't fared well. in this work, several machine learning techniques have been tried using the training and development sets. the algorithm that has the highest accuracy on the development test is used for the test set. this has been previously experimented in (andrew, 2021a), where the authors attempt to classify youtube comments in the dravidian languages of tamil, malayalam and kannada. the authors come to a conclusion svm models perform well for two out of three languages. in this paper, the 6 algorithms in @xcite are trained for the task at hand. in this paper, the stanford sentiment treebank is incorporated the algorithms in @xcite . @xcite describes the stanford sentiment treebank which includes a total of 215,154 unique phrases from those parse trees, each annotated by 3 human judges. however the labels from this treebank are different from the ones expected in the task. but these labels can help understand the premise of the sentiment. thus these are incorporated as features for classification. the authors of @xcite introduce a sentiment treebank with recursive neural networks for fine grained labelling tasks. in this work, simple machine learning algorithms are used with the sentiment treebank to explore the extent to which simple techniques can help identify online sexism. ## task the task in semeval 2023 -task 10 -explainable detection of online sexism (edos) @xcite is to indentify and classify sexist statements. sub task a aims at classifying statements into two categories: sexist and non sexist. sub task b aims at classifying the sexist comments from sub task a into 4 classes -(1) threats, (2) derogation, (3) animosity, (4) prejudiced discussions. sub task c aims at further classification of statements within the class "threats" from sub task b into threats of harm and incitement, encouragement of harm; statements within the class "derogation" from sub task b into descriptive attacks, aggressive and emotive attacks, dehumanisation and overt sexual objectification; statements within the class "animosity" from sub task b into casual use of gendered slurs, profanities insults, immutable gender stereotypes, backhanded gendered compliments, condescending explanations or unwelcome advice; state-ments within the class "prejudiced discussions" from sub task b into supporting mistreatment of individual women, supporting systemic discrimination against women. figure 1 shows the classes for each sub task. 4 data the data contains contains hateful and sexist statements. the following tables shows some statistics on the number of statements for each sub tasks. every statement that is classified as "non-sexist" is eliminated from sub tasks b and c. train dev test 14000 2000 4000 ## data representation the bag of words approach is used for text representation. the term frequency, inverse document frequency (tf-idf) measure is calculated for each term in the dataset. however, in adiition to this representation, the stanford sentiment tree bank is used to generate an extra feature. @xcite describes the stanford sentiment treebank which includes a total of 215,154 unique phrases from those parse trees, each annotated by 3 human judges. the stanford sentiment treebank includes labels for every syntactically plausible phrase. this allows to identify intricate sentiments in texts. in this task, the sentiment output for each statement from the treebank is considered as a feature. firstly, each statement is run through the stanford sentiment treebank, the classification given by the treebank is then converted to a vector representation and added as a feature to the tf-idf representation of the text. this helps to understand the premise of the statement before classifying into the fine grained classes of the task by the following machine learning algorithms. ## machine learning models in this section, several machine learning methods are designed for the task at hand. theses are explained in @xcite . the models are logistic regression, naïve bayes, support vector machines and random forests. the algorithms are used on the training and development sets. the accuracy on the development sets are taken into account. the algorithm with the highest accuracy for ## results from