Year: 2025
Type: conference-paper
Abstract: The detection of sexism in online content remains an open problem, as harmful language disproportionately affects women and marginalized groups.While automated systems for sexism detection have been developed, they still face two key challenges: data sparsity and the nuanced nature of sexist language.Even in large, well-curated datasets like the Explainable Detection of Online Sexism (EDOS), severe class imbalance hinders model generalization.Additionally, the overlapping and ambiguous boundaries of fine-grained categories introduce substantial annotator disagreement, reflecting the difficulty of interpreting nuanced expressions of sexism.To address these challenges, we propose two prompt-based data augmentation techniques: Definition-based Data Augmentation (DDA), which leverages categoryspecific definitions to generate semanticallyaligned synthetic examples, and Contextual Semantic Expansion (CSE), which targets systematic model errors by enriching examples with task-specific semantic features.To further improve reliability in fine-grained classification, we introduce an ensemble strategy that resolves prediction ties by aggregating complementary perspectives from multiple language models.Our experimental evaluation on the EDOS dataset demonstrates state-of-the-art performance across all tasks, with notable improvements of macro F1 by 1.5 points for binary classification (Task A) and 4.1 points for fine-grained classification (Task C) 1 .Warning: This paper includes examples that might be offensive and upsetting.1 Code and resources publicly available at: https://github.com/Sahrish42/explaining_matters_ sexism_detection_acl2025 Variation 1: "They lack the mental toughness to handle high-stakes leadership roles" Variation 2: "You can't trust women with important decisions?they're too caught up in their emotions."Variation 3: "Women just let their feelings get in the way of making logical choices." VariationsAs part of a fine-grained sexism detection project aimed at categorizing harmful language into distinct sexist categories: Ø Generate 3 Variations of the following Example Ø Reflect the Sexist Intent: Ensure each variation clearly embodies the sexist intent and nature described in the vector and definition.Ø Match the Tone: The tone should align with the emotional intensity and aggression described in definition.Ø Mimic Informal Social Media Language: Craft statements that resemble informal language typical of social media platforms like Reddit or Gab, using slang, abbreviations, or casual expressions as appropriate.• Vector: 2.1 Descriptive Attacks• Definition: Characterizing or describing women in a derogatory manner.This could include negative generalizations about women's abilities, appearance, sexual behavior, intellect, character, or morals.• Example: "Women are too soft and emotional to make rational decisions''
Authors: Sahrish Khan, Arshad Jhumka, Gabriele Pergola
Institutions: University of Warwick, University of Leeds
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4412945106