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.

使用人工智能技术识别厌女症是一种打击网络对女性的有害言论的方式。本文中，我们提出了一种多任务学习方法，利用主体性解释厌女症的主题来提高识别系统性别歧视内容的性能。通过结合不同注释者的多样性观点，考虑六个配置文件组的性别和年龄，在模型设计方面进行了广泛实验和错误分析，并使用两种语言模型验证了我们提出的四种替代的多任务学习技术设计来识别英文推文中存在的厌女症内容。结果表明，结合多样化的观点增强了语言模型解释不同形式厌女症的能力。此研究推进了内容辅助管理，并强调了拥抱多元观点来构建有效的在线管理系统的重要性。