Detecting 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.

In this paper, we discuss an interpretable framework to integrate toxic language annotations. Most data sets address only one aspect of the complex relationship in toxic communication and are inconsistent with each other. Enriching annotations with more details and information is however of great importance in order to develop high-performing and comprehensive explainable language models. Such systems should recognize and interpret both expressions that are toxic as well as expressions that make reference to specific targets to combat toxic language. We therefore created a crowd-annotation task to mark the spans of words that refer to target communities as an extension of the HateXplain data set. We present a quantitative and qualitative analysis of the annotations. We also fine-tuned RoBERTa-base on our data and experimented with different data thresholds to measure their effect on the classification. The F1-score of our best model on the test set is 79%. The annotations are freely available and can be combined with the existing HateXplain annotation to build richer and more complete models.