Modeling subjectivity (by Mimicking Annotator Annotation) in toxic comment identification across diverse communities

• URL: http://arxiv.org/abs/2311.00203v1
• Date: Wed, 1 Nov 2023 00:17:11 GMT
• Title: Modeling subjectivity (by Mimicking Annotator Annotation) in toxic comment identification across diverse communities
• Authors: Senjuti Dutta (1), Sid Mittal (2), Sherol Chen (2), Deepak Ramachandran (2), Ravi Rajakumar (2), Ian Kivlichan (2), Sunny Mak (2), Alena Butryna (2), Praveen Paritosh (2) ((1) University of Tennessee, Knoxville, (2) Google LLC)
• Abstract summary: This study aims to identify intuitive variances from annotator disagreement using quantitative analysis. We also evaluate the model's ability to mimic diverse viewpoints on toxicity by varying size of the training data. We conclude that subjectivity is evident across all annotator groups, demonstrating the shortcomings of majority-rule voting.
• Score: 3.0284081180864675
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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