The prevalence and impact of toxic discussions online have made content moderation crucial.Automated systems can play a vital role in identifying toxicity, and reducing the reliance on human moderation.Nevertheless, identifying toxic comments for diverse communities continues to present challenges that are addressed in this paper.The two-part goal of this study is to(1)identify intuitive variances from annotator disagreement using quantitative analysis and (2)model the subjectivity of these viewpoints.To achieve our goal, we published a new dataset\footnote{\url{https://github.com/XXX}} with expert annotators' annotations and used two other public datasets to identify the subjectivity of toxicity.Then leveraging the Large Language Model(LLM),we evaluate the model's ability to mimic diverse viewpoints on toxicity by varying size of the training data and utilizing same set of annotators as the test set used during model training and a separate set of annotators as the test set.We conclude that subjectivity is evident across all annotator groups, demonstrating the shortcomings of majority-rule voting. Moving forward, subjective annotations should serve as ground truth labels for training models for domains like toxicity in diverse communities.

在线毒性讨论的普及和影响使内容管理至关重要。自动化系统在识别有毒评论和减少对人工管理的依赖方面起着至关重要的作用。然而，识别多样化社区的有毒评论仍然存在挑战，本研究对此进行了探讨。

跨多元社群中有毒评论识别的主观建模（通过模仿标注者注释）