Abstract While Presidential debates present policy positions, they also construct group identities. This article asks how argumentative structures and linguistic choices work together to produce the “We” identity and “Them” identity that is characteristic of polarised political discourse. We analyse the 2024 Trump–Harris debate by integrating only argumentation schemes into the Discourse-Historical Approach (DHA) for its approach to argumentation. Three questions guide our analysis: how argumentation schemes construct in-group and out-group identities; how do lexical choices, analysed through DHA’s nomination strategy, construct in-group/out-group identities; and whether patterned relationships exist between scheme types and discursive strategies. Drawing on 24 reconstructed arguments and six in-depth analyses, we observe that ad hominem attacks and poisoning-the-well arguments tend to co-occur with predication and perspectivisation strategies, while arguments from consequences tend to co-occur with justification. We term these patterned relationships schematic affinities and offer the concept as a hypothesis warranting systematic investigation rather than a confirmed finding. Lexical devices, including terms that categorise actors as criminals, victims, or workers, reinforce the opposition that arguments establish. The framework demonstrates how argumentation theory and Critical Discourse Studies can inform one another, and it opens pathways for research on national identity formation, racism, and discriminatory discourse.

AbstractThis paper introduces moral argument analytics, a technology that provides insights into the use of moral arguments in discourse. We analyse five socio-political corpora of argument annotated data from offline and online discussions, totalling 240k words with 9k arguments, with an average annotation accuracy of 78%. Using a lexicon-based method, we automatically annotate these arguments with moral foundations, achieving an estimated accuracy of 83%. Quantitative analysis allows us to observe statistical patterns and trends in the use of moral arguments, whereas qualitative analysis enables us to understand and explain the communication strategies in the use of moral arguments in different settings. For instance, supporting arguments often rely on Loyalty and Authority, while attacking arguments use Care. We find that online discussions exhibit a greater diversity of moral foundations and a higher negative valence of moral arguments. Online arguers often rely more on Harm rather than Care, Degradation rather than Sanctity. These insights have significant implications for AI applications, particularly in understanding and predicting human and machine moral behaviours. This work contributes to the construction of more convincing messages and the detection of harmful or biased AI-generated synthetic content.

AbstractArgumentation as the public exchange of reasons is widely thought to enhance deliberative interactions that generate and justify reasonable public policies. Adopting an argumentation-theoretic perspective, we survey the norms that should govern public argumentation and address some of the complexities that scholarly treatments have identified. Our focus is on norms associated with the ideals of correctness and participation as sources of a politically legitimate deliberative outcome. In principle, both ideals are mutually coherent. If the information needed for a correct deliberative outcome is distributed among agents, then maximising participation increases information diversity. But both ideals can also be in tension. If participants lack competence or are prone to biases, a correct deliberative outcome requires limiting participation. The central question for public argumentation, therefore, is how to strike a balance between both ideals. Rather than advocating a preferred normative framework, our main purpose is to illustrate the complexity of this theme.