Abstract

In this paper, we present a model of pathos, delineate its operationalisation, and demonstrate its utility through an analysis of natural language argumentation. We understand pathos as an interactional persuasive process in which speakers are performing pathos appeals and the audience experiences emotional reactions. We analyse two strategies of such appeals in pre-election debates: pathotic Argument Schemes based on the taxonomy proposed by Walton et al. (Argumentation schemes, Cambridge University Press, Cambridge, 2008), and emotion-eliciting language based on psychological lexicons of emotive words (Wierzba in Behav Res Methods 54:2146–2161, 2021). In order to match the appeals with possible reactions, we collect real-time social media reactions to the debates and apply sentiment analysis (Alswaidan and Menai in Knowl Inf Syst 62:2937–2987, 2020) method to observe emotion expressed in language. The results point to the importance of pathos analysis in modern discourse: speakers in political debates refer to emotions in most of their arguments, and the audience in social media reacts to those appeals using emotion-expressing language. Our results show that pathos is a common strategy in natural language argumentation which can be analysed with the support of computational methods.