A multitask learning framework for leveraging subjectivity of annotators to identify misogyny

TL;DR

This paper introduces a multitask learning framework that leverages annotator subjectivity, including demographic diversity, to improve AI systems in identifying misogyny in online content, addressing the challenge of interpretative variability.

Contribution

The study presents a novel multitask learning approach that incorporates diverse annotator perspectives, enhancing misogyny detection in tweets compared to traditional models.

Findings

Incorporating diverse viewpoints improves model accuracy.

Multiple model designs were tested, showing consistent performance gains.

Subjectivity-aware models better interpret various forms of misogyny.

Abstract

Identifying misogyny using artificial intelligence is a form of combating online toxicity against women. However, the subjective nature of interpreting misogyny poses a significant challenge to model the phenomenon. In this paper, we propose a multitask learning approach that leverages the subjectivity of this task to enhance the performance of the misogyny identification systems. We incorporated diverse perspectives from annotators in our model design, considering gender and age across six profile groups, and conducted extensive experiments and error analysis using two language models to validate our four alternative designs of the multitask learning technique to identify misogynistic content in English tweets. The results demonstrate that incorporating various viewpoints enhances the language models' ability to interpret different forms of misogyny. This research advances content moderation and highlights the importance of embracing diverse perspectives to build effective online moderation systems.