A G rice-ful Examination of Offensive Language: Using NLP Methods to Assess the Co-operative Principle (2024)

Abstract: 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.

## introduction natural language processing (nlp) is characterized by creating applications adept at addressing real-world challenges. among those applications, we find machine translation, text summarization, coreference resolution, and part-of-speech-tagging, to name a few @xcite . these rapid technological developments, along with the recent emergence of large language models (llms), have brought to the fore the question of how linguistics could benefit from such advancements and contribute to the current wave. this issue is discussed in a recent nature editorial, where it is illustrated that there is a distinction between nlp and computational linguistics, with the latter focusing more on the two aforementioned questions. more specifically, "computational linguistics traditionally uses computational models to address questions in linguistics and borders the field of natural language processing, which in turn builds models of language for practical applications" @xcite . dupre @xcite poses an opposite opinion, claiming that deep learning techniques cannot illuminate linguistic theory, as the former focuses on language performance, while the latter on language competence, which are arbitrarily different. in this paper, we draw from the distinction between computational linguistics and nlp, and we use nlp methods as tools for discourse analysis. despite the argument that, at least current deep learning techniques are pertinent to theoretical insights in linguistics @xcite , we believe that deep learning tools can facilitate linguistic analysis. we exemplify this in our paper, by analyzing the structure of offensive language. offensive language detection is a popular topic in nlp, as its intricate nature, lying within the borders of linguistics, psychology, sociology, and law studies, makes it hard for current models to identify positive instances adequately @xcite . current approaches in nlp view offensive language as a detection task without delving further into the intricate dynamics of an offensive conversation. we believe that a thorough analysis of offensive language requires a pragmatic approach. by examining contextual factors, such as speech acts, perlocutionary effects, politeness strategies, and cultural norms, we can gain a deeper understanding of how and why language becomes offensive. ignoring these pragmatic aspects would result in an incomplete and potentially flawed analysis of offensive language. in order to perform discourse analysis on online offensive language from a pragmatic perspective, we employ part of the gricean theory @xcite , which outlines four conversational principles -quality, quantity, relevance, and manner-which ensure that speakers provide truthful, informative, relevant, and clear contributions to conversations. we argue that there is a pattern in the flouting/violation of the maxims when it comes to online offensive language. the most obvious assumption is that offensive language flouts the maxim of manner as this type of discourse is inherently not in accordance with this maxim. in particular, offensive language uses an inappropriate lexicon that is unsuitable for any occasion, leading to uncooperative conversations. on a similar note, pasa et al. @xcite have shown that the sarcasm of hate speech in instagram comments flouts all four maxims. the authors hypothesize that the main factors driving these violations are the lack of concise and clear information in comments, the cultural value in western countries that emphasizes the right to free speech, the tendency to seek excessive attention from others, and the ego that boosts self-importance while devaluing others. our contribution is two-fold. inspired from previous endeavors @xcite , we first translate the maxims into actual metrics using nlp methods, thus bridging the gap between theoretical and computational linguistics. secondly, we offer a computationally facilitated discourse analysis on offensive language, showing that such analyses can be semi-automated as the data can be filtered faster, allowing for a more precise examination of specific instances. to our knowledge, this is the first study that equips gricean theory through computational methods to analyze offensive language. our findings indicate that violations or floutings of the maxims do not differ when comparing offensive and non offensive online dis- ## theoretical background one central point in pragmatics is the work of hp grice, who formulated several pragmatic theories applicable today to conversation and discourse analysis. these include the cooperative principle, according to which the contribution of the conversation "must be such as is required, at the stage at which it occurs, by the accepted purpose or direction of the talk exchange in which you are engaged" @xcite . the cooperative principle outlines the fundamental principle guiding communication and it is broken down into multiple sub-principles, or 'maxims'. more specifically, it suggests that in conversation, participants generally adhere to four maxims: the maxim of quantity (provide just enough information), quality (speak truthfully), relevance (be relevant), and manner (be clear and concise). more information about the maxims and their explanations, often referred to as submaxims, can be found in another essential concept in pragmatics, and upon which we touch in this study, is implicature. implicature occurs when speakers convey meaning beyond the literal interpretation of their words, relying on context and shared understanding. implicatures can be further divided into those that are explicitly conveyed (explicature) and those inferred by the listener. ## related work this section reviews key contributions in computational pragmatics, highlighting advancements in pragmatic inference, model evaluation, and the integration of pragmatic principles into nlp systems. we conclude by providing an overview of existing pragmatic approaches for offensive language detection and analysis. ## methodology for the purposes of this study, we use metrics and nlp tools for each of the 4 maxims. in this way, we attempt to filter different instances that will be used for a qualitative analysis in a more traditional discourse analysis manner. ## results quality ## exemplary discourse analysis quantifying the maxims and examining the results in section 5 have allowed us to form a more concrete idea and hypothesis, while it also allows us to filter results that would be of discourse analysis interest. to perform a discourse analysis, we first proceed to select instances according to the results of the previous section. for that reason, we look only at the offensive class and we randomly choose one example that violates each maxim, and one example that does not and proceed to compare the instances. the selected examples can be found in comparing the two examples for the maxim of quality, the one that does not violate the maxim, does not contain any information that could potentially be untrue. the use of hedging with 'seem' and the simile introduced with 'like' mitigate the certainty of the author of the comment, despite the fact that it is an offensive comment. the example that violates the maxim, however, is full of potentially false assumptions, such as "he used all the sexual energy into fighting". this information is misleading and does not contribute to an effective cooperative (online) conversation. looking at the examples for the maxim of quantity, both responses generally adhere to the maxim. response 1, which looks as an additional comment from the author of the title thread, provides enough information to support its point without overwhelming details, justifying the reasoning to their initial question. response 2 offers a concise and direct answer. however, it is possible it could be considered slightly under-informative as it does not precisely reply to the initial question. about the second example, that according to the used algorithm violates the maxim of quantity, response 1 provides an abundance of specific criticisms, making it slightly verbose and less clear due to its structure. that could lead us to the conclusion that it violates the maxim of quantity. the second response expresses an opposite opinion from the one presented in the title. it does not answer directly the question. however, with that response 'i like them', we are to lead to the implicature that the author of the response does not support banning 'gendercritical', contrary to the suggestion in the title. ## conclusions in this paper, we adopt a pragmatics-based approach to hate speech analysis, reinforced by nlp methods. we draw from the linguistic theory of the 4 gricean maxims and the co-operative principle, and we employ nlp methods as tools to assess whether the maxims are flouted or violated in offensive online contexts. our approach provides an essential step before any type of discourse analysis, that allows a better understanding of the data and consequent filtering of instances for qualitative discourse analysis. our experimental results showed some patterns in the flouting/violations of the maxims in offensive language settings, such as the flouting/violation of the maxim of manner due to ambiguity and profanity. with this paper, we advocate for more mixed approaches that will encompass both computational and traditional linguistics, and which will contribute to better data analysis. in future work, we aim to dive deeper into the potential of the metrics, particularly when coupled with advanced llms. this exploration will contribute to further automate the assessment process of the cooperative principle, potentially enhancing its accuracy. additionally, we intend to investigate other discourse domains posing challenges to nlp, such as sentiment analysis, humor, and sarcasm detection, which represent pragmatically charged categories. furthermore, we are interested in examining intersections among the maxims to gain a comprehensive understanding.