An Adaptive Supervised Contrastive Learning Framework for Implicit Sexism Detection in Digital Social Networks

TL;DR

This paper presents ASCEND, an adaptive supervised contrastive learning framework that improves implicit sexism detection in social media by using threshold-based contrastive learning, multi-faceted textual features, and joint optimization, outperforming existing methods.

Contribution

The paper introduces a novel threshold-based contrastive learning approach for implicit sexism detection, enhancing embedding quality and detection accuracy over prior methods.

Findings

ASCEND outperforms existing methods with significant F1 score improvements.

Incorporating sentiment, emotion, and toxicity features boosts detection performance.

Threshold-based contrastive learning effectively refines semantic representations.

Abstract

The global reach of social media has amplified the spread of hateful content, including implicit sexism, which is often overlooked by conventional detection methods. In this work, we introduce an Adaptive Supervised Contrastive lEarning framework for implicit sexism detectioN (ASCEND). A key innovation of our method is the incorporation of threshold-based contrastive learning: by computing cosine similarities between embeddings, we selectively treat only those sample pairs as positive if their similarity exceeds a learnable threshold. This mechanism refines the embedding space by robustly pulling together representations of semantically similar texts while pushing apart dissimilar ones, thus reducing false positives and negatives. The final classification is achieved by jointly optimizing a contrastive loss with a cross-entropy loss. Textual features are enhanced through a word-level attention module. Additionally, we employ sentiment, emotion, and toxicity features. Evaluations on the EXIST2021 and MLSC datasets demonstrate that ASCEND significantly outperforms existing methods, with average Macro F1 improvements of 9.86%, 29.63%, and 32.51% across multiple tasks, highlighting its efficacy in capturing the subtle cues of implicit sexist language.