Users Hate Blondes: Detecting Sexism in User Comments on Online R omanian News (2022)

Abstract: Romania ranks almost last in Europe when it comes to gender equality in political representation, with about 10% fewer women in politics than the E.U. average. We proceed from the assumption that this underrepresentation is also influenced by the sexism and verbal abuse female politicians face in the public sphere, especially in online media. We collect a novel dataset with sexist comments in Romanian language from newspaper articles about Romanian female politicians and propose baseline models using classical machine learning models and fine-tuned pretrained transformer models for the classification of sexist language in the online medium.

## introduction while considerable progress has been made to combat sexism in the domain of political power, we are still a long way from achieving gender balance, especially in eastern european countries like romania. according to the gender equality index published by the european institute for gender equality in 2021, in romania, only 25.8% of ministers, 19.7% of members of parliament, and 18.4% of the members of general assemblies are women @xcite . romania ranks third to last (above hungary and malta by a small margin) regarding gender balance in political representation. it is much lower than the european union's average (30.7% ministers, 31.5% members of parliament, and 29.3% members of general assemblies). furthermore, women in leadership positions face discrimination and social pressure to conform to gender roles. in the public sphere, female politicians in romania face verbal abuse even from their peers. this abuse becomes even more prominent online, where warning: this paper contains sexist language. the so-called disinhibition effect leads to exaggerated behaviours, from increased self-disclosure to increased verbal violence @xcite @xcite @xcite . because the online environment offers anonymity and therefore lack of consequences for violent language, a variety of sexual slurs and sexist language appear online. this is especially true in the comment section of popular online newspapers when the topic of the articles are prominent women. the present paper uses these comments to build a dataset of sexist and non-sexist texts and experiment with several baselines models to detect the sexist language in romanian automatically. the automatic detection of sexism could aid efforts to filter sexist texts, to encourage the prevention of, sensitization to, and sanctioning of such language. there are other similar approaches analysing how sexism manifests in different environments: at the workplace @xcite , in the gaming communities @xcite , in politics @xcite . several shared tasks are dedicated to the identification of online sexism and misogyny: sexism identification in social networks (exist) @xcite , automatic misogyny identification at ibereval @xcite and evalita @xcite and multimedia automatic misogyny identification (mami) @xcite . this paper proposes a novel dataset for sexist language identification in romanian language by collecting and annotating a corpus of online comments from newspaper articles about romanian female politicians. furthermore, we perform experiments on this corpus using classical machine learning models and fine-tuned pre-trained transformer models, thus providing baselines for comparison in future work. ## rofempol corpus this data used in this study is part of the rofem-pol corpus 1 , which was compiled from online romanian-language comments about romanian female politicians. ten of the most prominent female romanian politicians were chosen: clotilde armand (usr 2 ), viorica dȃncilȃ (psd 3 ), gabriela firea (psd), monica anisie (pnl 4 ), maria grapini (psd), elena udrea (pdl 5 ), diana s , os , oacȃ (aur 6 ), carmen dan (psd), lia olgut , a vasilescu (psd). we have also included the prosecutor laura codrut , a kovesi (dna 7 / eppo 8 ) as, although she is not technically a politician, she was an influential figure in the romanian political scene. ## baseline methods in this section, we propose several baseline methods for the binary classification of sexist language using rofempol. we explore several encoding methods for the romanian text data, such as bagof-words and bert-based sentence representations. alongside classical machine learning models (i.e., logistic regression, svm, random forest), we also explore fine-tuning pre-trained transformer models for the romanian language. ## experiments and results in this section, we describe the classification experiments performed for the detection of sexist language in romanian text and report the obtained results.