  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.
