Unraveling the Complexities of Offensive Language: A Detailed Analytical Framework for Understanding Offensive Communication Dynamics

Anonymous

Abstract: Offensive online content can marginalize and cause harm to groups and individuals. To prevent harm while ensuring speech rights, fair and accurate detection is required. However, current models and data struggle to distinguish offensive language from acceptable, non-toxic language variations related to culture or subjective interpretation. This study presents a comprehensive toxicity assessment with two annotated datasets focusing on nuances of human interpretation with objective evaluation. The substantial increase in inter-annotator agreement indicates the effectiveness of structured guidelines at controlling subjective variability and strengthening result consistency. Additionally, we explore the effectiveness of in-context learning with few-shot examples to improve toxicity detection from large language models (LLMs), GPTs specifically, finding that explicit assessment criteria significantly improve agreement between automated and human evaluations of offensive content. The feasibility of criteria-based automatic annotations is evidenced by the better performance of smaller models fine-tuned on 10 times less auto-annotated data with multi-language variations. The findings demonstrate notable efficiency in combining contextual understanding of LLMs with criterion-guided in-context learning with limited data size and heterogeneous language types.Content Warning: This article only analyzes offensive language for academic purposes. Discretion is advised.

Paper Type: long

Research Area: Computational Social Science and Cultural Analytics

Contribution Types: NLP engineering experiment, Approaches to low-resource settings, Approaches low compute settings-efficiency, Publicly available software and/or pre-trained models, Data resources, Data analysis, Surveys

Languages Studied: English