Public arguments can be good or bad not only as a matter of logic, but also in the sense that speakers can _do_ good or bad things with arguments. For example, hate speakers use public arguments to contribute to the subordination of their targets. But how can ordinary speakers acquire the authority to perform subordinating speech acts? This is the ‘Authority Problem’. This paper defends a solution inspired by McGowan’s (Australas J Philos 87:389–407, 2009) analysis of oppressive speech, including against (...) concerns raised by McGowan (Just words: On speech and hidden harm, Oxford University Press, Oxford, 2019) herself. A _deflated_ kind of authority can be gained from the hate speaker’s standing in a norm-governed ‘activity of oppression’. We should be wary about engaging with such speakers in public argument. Even if we counter their arguments, we may still elevate their standing within that activity and so enable them to perform more pernicious speech acts than was previously possible. (shrink)

The ‘prohibitionist’ idea that slurs have the same linguistic properties as their neutral counterparts hasn’t received much support in the literature. Here I offer a modified version of prohibitionism, according to which the taboo on using slurs is part of their conventional meaning. I conclude with explanations of the behaviour of slurs in embedded constructions.

The current study represents an integrated model of explicit and implicit offensive language taxonomy. First, it focuses on a definitional revision and enrichment of the explicit offensive language taxonomy by reviewing the collection of available corpora and comparing tagging schemas applied there. The study relies mainly on the categories originally proposed by Zampieri et al. (2019) in terms of offensive language categorization schemata. After the explanation of semantic differences between particular concepts used in the tagging systems and the analysis of (...) theoretical frameworks, a finite set of classes is presented, which cover aspects of offensive language representation along with linguistically sound explanations (Lewandowska-Tomaszczyk et al. 2021). In the analytic procedure, offensive from non-offensive discourse is first distinguished, with the question of offence Target and the following categorization levels and sublevels. Based on the relevant data generated from Sketch Engine (https://www.sketchengine.eu/ententen-english-corpus/), we propose the concept of offensive language as a superordinate category in our system with a number of hierarchically arranged 17 subcategories. The categories are taxonomically structured into 4 levels and verified with the use of neural-based (lexical) embeddings. Together with a taxonomy of implicit offensive language and its subcategorization levels which has received little scholarly attention until now, the categorization is exemplified in samples of offensive discourses in selected English social media materials, i.e., publicly available 25 web-based hate speech datasets (consult Appendix 1 for a complete list). The offensive category levels (types of offence, targets, etc.) and aspects (offensive language property clusters) as well as the categories of explicitness and implicitness are discussed in the study and the computationally verified integrated explicit and implicit offensive language taxonomy proposed in the study. (shrink)