Ewelina Gajewska’s research while affiliated with Adam Mickiewicz University in Poznań and other places

The aim of this study was to examine whether the use of affective language by politicians impacts the social evaluation of their image, as measured by the semantic differential method developed by Cwalina et al.. In the study, Polish participants ( N = 958) evaluated the profiles of three well-known Polish politicians from different parties: Bosak (right-wing), Trzaskowski (center), and Biedroń (left-wing). Participants made evaluations before and after reading hateful, kind or neutral tweets about refugees from Ukraine. We controlled for participants’ political views and demographic variables. The results confirmed changes in evaluations of the image of politicians depending on the language they used. The ‘positivity bias’ identified in the study provides encouragement to use kindness speech in public discourse, showing that this type of communication can improve a politician's ratings.

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.