Content Moderation in Online Platforms: A Study of Annotation Methods for Inappropriate Language
Abstract
AbstractDetecting inappropriate language in online platforms is vital for maintaining a safe and respectful digital environment, especially in the context of hate speech prevention. However, defining what constitutes inappropriate language can be highly subjective and context-dependent, varying from person to person. This study presents the outcomes of a comprehensive examination of the subjectivity involved in assessing inappropriateness within conversational contexts. Different annotation methods, including expert annotation, crowd annotation, ChatGPT-generated annotation, and lexicon-based annotation, were applied to English Reddit conversations. The analysis revealed a high level of agreement across these annotation methods, with most disagreements arising from subjective interpretations of inappropriate language. This emphasizes the importance of implementing content moderation systems that not only recognize inappropriate content but also understand and adapt to diverse user perspectives and contexts. The study contributes to the evolving field of hate speech annotation by providing a detailed analysis of annotation differences in relation to the subjective task of judging inappropriate words in conversations.