A Grice-ful Examination of Offensive Language: Using NLP Methods to Assess the Co-operative Principle

Natural Language Processing (NLP) can provide tools for analyzing specific intricate language phenomena, such as offensiveness in language. In this study, we employ methods from pragmatics, more specifically Gricean theory, as well as NLP techniques, to analyze instances of online offensive language. We present a comparative analysis between offensive and non-offensive instances with regard to the degree to which the 4 Gricean Maxims (Quality, Quantity, Manner, and Relevance) are flouted or violated. To facilitate our analysis, we employ NLP tools to filter the instances and proceed to a more thorough qualitative analysis. Our findings reveal that offensive and non-offensive speech do not differ significantly when we evaluate with metrics that correspond to the Gricean Maxims, apart from some aspects of the Maxim of Quality and the Maxim of Manner. Through this paper, we advocate for a turn towards mixed approaches to linguistic topics by also paving the way for a modernization of discourse analysis and natural language understanding that encompasses computational methods. Warning: This paper contains offensive language that might be triggering for some individuals.