Affective Foraging: Knowledge Graph-Assisted Analysis of Emotion and Topic Information Patches in Online Discourse on the 2025 US Tariffs

The tariffs introduced by the second Trump administration in early 2025 sparked significant public discourse on social media. This paper presents a computational framework for analyzing the emotional and thematic content of this discourse, focusing on YouTube as a key platform. We introduce a novel methodology for emotion detection and topic modelling using a combination of GenAI and embedding-based models to analyse 5874 transcripts and 866673 comments. The knowledge graph created from this data revealed clusters of related emotions and topics which were subsequently used to surface patterns of affective foraging. These patterns highlight the ways in which users navigate emotional content and seek information in a complex digital landscape. Our results contribute to the field of Information Systems by adding an emotional dimension to Information Foraging Theory, proposing the concept of Affective Foraging that integrates Digital Emotion Regulation with the latter to depict “informavores” as emotionally inclined, irrational decision-makers.