  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.
