The Language of Affect Heuristic in Online Discourse: A Corpus Study with Human and Llm Coders

Abstract

This paper investigates the affect heuristic in online argumentative discourse through a corpus-based study combining manual and automatic annotation. Drawing on research in argumentation theory, psychology, and decision science, the study approaches affect not as an external addition to reasoning but as a recurring component of evaluative judgment. The analysis focuses on discussions of climate change and artificial intelligence collected from Reddit and X (formerly Twitter), domains characterised by uncertainty, risk perception, and public controversy. The study employs a bottom-up annotation methodology in which four human annotators identify instances of affect heuristic and related cognitive biases in a corpus of more than 30,000 posts and comments. Inter-annotator agreement is assessed using Fleiss’ κ, Cohen’s κ, Gwet’s AC1, and weighted F1 measures. In a second stage, the same annotation scheme is applied to GPT-4o, treated as a constrained fifth annotator operating within predefined categories and probabilistic classification rules. The results show that the affect heuristic is the most frequent heuristic pattern in the corpus, occurring more often than confirmation bias, availability heuristic, or representativeness heuristic. Although traditional κ coefficients remain low because of category imbalance, agreement measures robust to prevalence effects indicate substantial consistency among annotators. The automatic annotation stage reveals partial alignment between human and model judgments, while also exposing systematic discrepancies in the model’s distribution of categories. A lexical and discursive analysis further demonstrates that affect heuristics do not necessarily manifest through explicit emotion vocabulary. Instead, they frequently appear through evaluative framing, practical reasoning under uncertainty, and subtle stance-taking related to collective action and future-oriented judgment. The findings contribute to empirical research on emotional processes in argumentation and demonstrate how affective reasoning can be operationalised and studied through combined qualitative and computational methods.