# Multi-label Categorization of Accounts of Sexism using a Neural Framework

arXiv: https://arxiv.org/abs/1910.04602 · HTML: https://mlsanity.com/paper/1910.04602/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2019.

## Built on by (most cited first, 19 of 19)

- [SWSR: A Chinese Dataset and Lexicon for Online Sexism Detection](https://mlsanity.com/paper/2108.03070/) (2021)
- ["Subverting the Jewtocracy": Online Antisemitism Detection Using Multimodal Deep Learning](https://mlsanity.com/paper/2104.05947/) (2021)
- [Dataset for Identification of Homophobia and Transophobia in Multilingual YouTube Comments](https://mlsanity.com/paper/2109.00227/) (2021)
- [CORGI-PM: A Chinese Corpus For Gender Bias Probing and Mitigation](https://mlsanity.com/paper/2301.00395/) (2023)
- [Enabling Classifiers to Make Judgements Explicitly Aligned with Human Values](https://mlsanity.com/paper/2210.07652/) (2022)
- [Re-examining Sexism and Misogyny Classification with Annotator Attitudes](https://mlsanity.com/paper/2410.03543/) (2024)
- [Sexism Identification in Tweets and Gabs using Deep Neural Networks](https://mlsanity.com/paper/2111.03612/) (2021)
- [SexWEs: Domain-Aware Word Embeddings via Cross-lingual Semantic Specialisation for Chinese Sexism Detection in Social Media](https://mlsanity.com/paper/2211.08447/) (2022)
- [NLP-LTU at SemEval-2023 Task 10: The Impact of Data Augmentation and Semi-Supervised Learning Techniques on Text Classification Performance on an Imbalanced Dataset](https://mlsanity.com/paper/2304.12847/) (2023)
- [Divided by discipline? A systematic literature review on the quantification of online sexism and misogyny using a semi-automated approach](https://mlsanity.com/paper/2409.20204/) (2024)
- [A Survey of Machine Learning Models and Datasets for the Multi-label Classification of Textual Hate Speech in English](https://mlsanity.com/paper/2504.08609/) (2025)
- [SSS at SemEval-2023 Task 10: Explainable Detection of Online Sexism using Majority Voted Fine-Tuned Transformers](https://mlsanity.com/paper/2304.03518/) (2023)
- [Misogyny classification of German newspaper forum comments](https://mlsanity.com/paper/2211.17163/) (2022)
- [CL-UZH at SemEval-2023 Task 10: Sexism Detection through Incremental Fine-Tuning and Multi-Task Learning with Label Descriptions](https://mlsanity.com/paper/2306.03907/) (2023)
- [A Study on Bias Detection and Classification in Natural Language Processing](https://mlsanity.com/paper/2408.07479/) (2024)
- [Improving User Behavior Prediction: Leveraging Annotator Metadata in Supervised Machine Learning Models](https://mlsanity.com/paper/2503.21000/) (2025)
- [Overview of the NLPCC 2025 Shared Task: Gender Bias Mitigation Challenge](https://mlsanity.com/paper/2506.12574/) (2025)
- [An Adaptive Supervised Contrastive Learning Framework for Implicit Sexism Detection in Digital Social Networks](https://mlsanity.com/paper/2507.05271/) (2025)
- [Can NLP Tackle Hate Speech in the Real World? Stakeholder-Informed Feedback and Survey on Counterspeech](https://mlsanity.com/paper/2508.04638/) (2025)

## Abstract (arXiv)

Sexism, an injustice that subjects women and girls to enormous suffering, manifests in blatant as well as subtle ways. In the wake of growing documentation of experiences of sexism on the web, the automatic categorization of accounts of sexism has the potential to assist social scientists and policy makers in studying and countering sexism better. The existing work on sexism classification, which is different from sexism detection, has certain limitations in terms of the categories of sexism used and/or whether they can co-occur. To the best of our knowledge, this is the first work on the multi-label classification of sexism of any kind(s), and we contribute the largest dataset for sexism categorization. We develop a neural solution for this multi-label classification that can combine sentence representations obtained using models such as BERT with distributional and linguistic word embeddings using a flexible, hierarchical architecture involving recurrent components and optional convolutional ones. Further, we leverage unlabeled accounts of sexism to infuse domain-specific elements into our framework. The best proposed method outperforms several deep learning as well as traditional machine learning baselines by an appreciable margin.