“Hold on honey, men at work”: A semi-supervised approach to detecting sexism in sitcoms

Abstract

Television shows play an important role in propagating societal norms. Owing to the popularity of the situational comedy (sitcom) genre, it contributes significantly to the overall development of society. In an effort to analyze the content of television shows belonging to this genre, we present a dataset of dialogue turns from popular sitcoms annotated for the presence of sexist remarks. We train a text classification model to detect sexism using domain adaptive learning. We apply the model to our dataset to analyze the evolution of sexist content over the years. We propose a domain-specific semi-supervised architecture for the aforementioned detection of sexism. Through extensive experiments, we show that our model often yields better classification performance over generic deep learning based sentence classification that does not employ domain-specific training. We find that while sexism decreases over time on average, the proportion of sexist dialogue for the most sexist sitcom actually increases. . A quantitative analysis along with a detailed error analysis presents the case for our proposed methodology.

ASRtrans at SemEval-2022 Task 5: Transformer-based Models for Meme Classification

“…We took 624 comments labeled 'sexist' out of 1137 comments present in this dataset. (Singh et al, 2021) presented a dataset to determine the use of sexism in English sitcoms from which, we extracted 1631 sexist text instances. From EXIST2021 dataset (Rodríguez-Sánchez et al, 2021-09), sexist text instances in English language are extracted.…”