A recurring concern within contemporary philosophy of language has been with the ways in which speakers can be illocutionarily silenced, i.e. hindered in their capacity to do things with words. Moving beyond the traditional conception of silencing as uptake failure, Mary Kate McGowan has recently claimed that silencing may also involve other forms of recognition failure. In this paper I first offer a supportive elaboration of McGowan’s claims by developing a social account of speech act performance, according to which the (...) success of an illocutionary act is not only a function of the intentions of and the conventions deployed by the speaker, but partly depends on how the act is recognized or taken up by the hearer. I then provide a comprehensive definition of illocutionary silencing and spell out what it means for it to occur in a systematic manner. (shrink)

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)

Political metaphors do more than punch up messages; they can systematically bias observers’ attitudes toward the issue at hand. What, then, is an effective strategy for counteracting a metaphor’s influence? One could ignore or criticize the metaphor, emphasizing strong counterarguments directly pertaining to the target issue. Yet if observers rely on it to understand a complicated issue, they may be reluctant to abandon it. In this case, a “metaphor extension” strategy may be effective: Encourage observers to retain the metaphor but (...) reinterpret its meaning by considering other, less obvious implications. The current studies support this claim. Under conditions where participants gained a strong epistemic benefit from a rhetorical metaphor, they were more persuaded by a rebuttal that extended that metaphor. The studies use converging operational definitions of epistemic benefit and offer insight into how political attitudes are made and unmade. (shrink)