hhu EDOS at S em E val-2023 Task 10: Explainable Detection of Online Sexism ( EDOS ) Binary Sexism Detection (Subtask A) (2023)

Abstract: In this paper, we describe SemEval-2023 Task 10, a shared task on detecting and predicting sexist language. The dataset consists of labeled sexist and non-sexist data targeted towards women acquired from both Reddit and Gab. We present and compare several approaches we experimented with and our final submitted model. Additional error analysis is given to recognize challenges we dealt with in our process. A total of 84 teams participated. Our model ranks 55th overall in Subtask A of the shared task.

## introduction online sexism is a consistently growing problem on the internet especially on social media platforms such as reddit and twitter. when women are the target, it creates a hostile environment and perpetuates sexist beliefs and social stigmas @xcite . investigating the data gathered from these platforms to not only specifically detect sexist content and certain patterns, but rather explain why it is sexist, gives us a better understanding of the impact it has on women, enhances its interpretability, and helps create a much more welcoming environment. this paper describes our group's approach for semeval-2023 task 10-a @xcite , which deals with binary sexism classification of sexist and non-sexist content. in order to solve this problem, we worked on fine-tuning transformer language models and on using the provided and additional non-labelled data for an semi-supervised extension of the training data set. additionally, we compared our results with traditional machine learning classification methods such as a random forest or k-nearest neighbours classifier. in view of the fact that the provided data had significantly less sexist entries and recognizing the conflict between sexist data and data that is clearly offensive yet non-sexist, we also experimented with data augmentation to overcome the unbalance in the data. it turns out that our best performing model is a bert model that is fine-tuned with the manually labeled training data as well as with the automatically labeled additional data that was provided by the organizers. we present the task and background in section 3, our approaches in section 4 and our error analysis in section 5, respectively. ## related work the detection of hate speech on online platforms has been gaining popularity within the natural language processing community in recent years, focusing on tasks in sub-areas such as identifying racist or abusive content (for literature reviews see @xcite . however, fewer studies have concentrated on hate speech specifically targeted at women (e.g., @xcite . classical machine learning techniques based on manual feature engineering use a range of features for hate speech detection (for an overview see @xcite , of which word and character n-grams belong to the most indicative ones @xcite . @xcite found that automatic feature selection outperforms models with carefully engineered features. using deep learning ensembles, @xcite report an improvement in f-measure of nearly 5 points compared to classical feature based techniques. leaderboards from recent shared tasks in offensive speech detection show that transformer architectures perform best @xcite . however, @xcite found that the current state-of-the-art methods for hate speech detection have overestimated performance claims due to methodological issues such as data overfitting and sampling issues. nowadays, a variety of datasets is available to train hate speech detection models on (for an overview see @xcite . however, as @xcite and @xcite point out for racist language, annotations may be influenced by annotators racist stereotypes. in recent times, there has been an increase in research aimed at gaining a deeper understanding of various characteristics of sexism. it is particularly important to distinguish between the various different types and forms of sexism in hate speech such as misogyny, hostile or benevolent sexism @xcite , subtle or overt sexism, positive sounding stereotypes etc. and how it manifests itself on social media to help identify and classify sexist content @xcite as well as acknowledge the impact and hurt it causes towards women @xcite . the semeval-2023 task 10 tackles this problem @xcite . ## background semeval-2023 task 10 provides participants with a dataset labelled by trained annotators and experts consisting of a total of 20,000 entries,10,000 of which are extracted from the social networking platforms gab foot_0 and the remaining 10,000 from reddit foot_1 @xcite . gab is especially well-known for not restricting users' content by moderation and for attracting users from the 'alt-right'-or the 'incel'-movement for example that express their misogynistic attitude in the form of hate speech @xcite . the training data is comprised of 14,000 entries where each entry is separated into columns describing a unique identifier for each entry as "rewire-id", the input text as "text", the labels for subtask a ('binary sexism detection') as "label-sexist", the labels for subtask b ('category of sexism') as "label-category" and lastly the labels for subtask c ('fine-grained vector of sexism') as "label-vector". for the development phase 2,000 entries are provided the labels of which have been made available before the final test phase. in the final test phase 4,000 entries are to be labeled by the submitting teams. the distribution of the labels 'sexist' and 'not sexist' in the three data sets ('train', 'dev', 'test') is comparable. about 24% of the entries is labeled 'sexist'. additional data from gab and reddit with 1,000,000 entries each is provided to encourage further training and innovative techniques. the goal of subtask a is to build a system that classifies and predicts whether an entry is sexist or not sexist. the results are then used for subtask b and c that delves into each individual sexist classified entry in depth and categorizes as well as describes the sexist content in a fine-grained manner. this helps thoroughly understand the content and the harm it causes women at such a grand scale. ## approaches experimented with we tackle the classification problem by (a) finetuning a pretrained language model (sec. 4.1) and by (b) classical machine learning methods with feature extraction (sec. 4.2). 3 the training data provided by the task organizers is split into 1,400 held-out test entries and 12,600 actual training entries for all experiments described in the current section. splitting occurs as a constant over all experiments. ## error analysis and conclusion we conduct a brief analysis of our submitted model on the test set. to get a better understanding of how 'confident' our model is, when it makes predictions, figure 2 plots the model's softmax scores for the test data. from figure 2 we can observe that the false positives have an on average lower prediction score for 'sexist' entries than the true positives. similarly, the false negatives have a higher prediction score for 'sexist' entries compared to the true negatives. however, figure 2 also shows that there is no clear boundary line between the true/false positives and the true/false negatives. to gain a better understanding of our models' weaknesses, we analyzed our results using the more wkuhdwv ghurjdwlrq dqlprvlw\ suhmxglfh odehofdwhjru\ ghqvlw\ wuxhsrvlwlyh idovhqhjdwlyh figure 3: distribution of false negatives and true positives over the four classes of subtask b by a manual inspection, we have observed that our model frequently makes incorrect predictions when presented with data containing non-sexist but offensive language. the opposite situation also occurs, when the model falsely predicts a data point/text as 'not sexist' where sexist but nonovertly offensive language is used. the ability of the model to detect sexist content appears to be impacted by the lack or presence of overtly offensive language e.g. use of slurs. the following two examples from the data exemplify this behavior of our model. the first example is a false negative with a softmax score of 0.48, that lacks sexist vocabulary: a slow transition starting w/ feminism, false rape reports on campus and the complete indifference in society for the suffering of those men which led me to the men's rights movement, than mgtow youtubers and trp. and the second example is a false positive with a softmax score of 0.94, that uses offensive but not explicitly sexist vocabulary: you seem to care enough to threaten him. you are a degenerate shabbos cock sucker who puts a group of people that hates you simply for existing over your white brothers and sisters. you are a disgrace. as our model performs worse on the 'sexist class' (f1 score: 0.72) compared to the 'not sexist' class (f1 score: 0.91) and as the recall for the 'sexists' class differs significantly between subclasses defined for subtask b (see figure 3 ) it could be worth to try to combine a classifier for subtask b with one for the current subtask a.