“Call me sexist, but...” : Revisiting Sexism Detection Using Psychological Scales and Adversarial Samples

Abstract

Research has focused on automated methods to effectively detect sexism online. Although overt sexism seems easy to spot, its subtle forms and manifold expressions are not. In this paper, we outline the different dimensions of sexism by grounding them in their implementation in psychological scales. From the scales, we derive a codebook for sexism in social media, which we use to annotate existing and novel datasets, surfacing their limitations in breadth and validity with respect to the construct of sexism. Next, we leverage the annotated datasets to generate adversarial examples, and test the reliability of sexism detection methods. Results indicate that current machine learning models pick up on a very narrow set of linguistic markers of sexism and do not generalize well to out-of-domain examples. Yet, including diverse data and adversarial examples at training time results in models that generalize better and that are more robust to artifacts of data collection. By providing a scale-based codebook and insights regarding the shortcomings of the state-of-the-art, we hope to contribute to the development of better and broader models for sexism detection, including reflections on theory-driven approaches to data collection.

Large scale analysis of gender bias and sexism in song lyrics

“…Part of our work furthermore builds on the results of a sexism classifier developed by Samory et al [19]. Since the exact definition of misogyny and sexism may be under discussion [26], the authors of [19] took into account different dimensions of sexism to increase model validity and furthermore improved the model reliability through including adversarial examples.…”

“…Part of our work furthermore builds on the results of a sexism classifier developed by Samory et al [19]. Since the exact definition of misogyny and sexism may be under discussion [26], the authors of [19] took into account different dimensions of sexism to increase model validity and furthermore improved the model reliability through including adversarial examples. Other noteworthy approaches to sexism detection use support vector machines, sequence-to-sequence models and a FastText classifier [27]; a BERT-based architecture to detect misogyny and aggression simultaneously on social media [28] or compare different models for identifying misogyny across languages and domains on Twitter data [29].…”

“…We fine tuned a BERT 3 classifier to detect sexist passages in texts using the dataset and the code provided by Samory et al [19]. The dataset contains texts manually labeled following a scale-based codebook that operationalizes the concept of sexism.…”

“…This makes the classifier perform well on out-of-domain data, also thanks to the inclusion of human-written adversarial examples. We refer to Samory et al [19] for further details on the codebook.…”

“…Then, we apply and adapt the sexism classifier of Samory et al [19] to explore the presence of sexism in song lyrics. This approach allows to analyze the evolution of sexism in song lyrics of the WASABI dataset across time.…”