A Multi-Motivational Approach to Understanding Polarized Discourse

Dataset for A Multi-Motivational Approach to Understanding Polarized Discourse This is the dataset for the published paper "A Multi-Motivational Approach to Understanding Polarized Discourse". The dataset was disclosed per requested by the review commitee and in support of open science. This dataset is for the review process only. Reuse of the dataset of any research or commerical purpose requires permissions from the original authors. Additional Information and materials are available on our GitHub repository.https://github.com/research-repo-open/A-Multi-Motivational-Approach-to-Understanding-Polarized-Discourse.gitAbstract. Discourse in politically polarized environments is often shaped by layered, psychologically complex motivations that challenge traditional analytic methods. While Quantitative Ethnography (QE) enables structured coding and visualization of discourse patterns, such tools may implicitly assume that utterances reflect singular, codable constructs. This study combines three techniques to explore the possibility of multiple, codable constructs. They include ENA discourse coding, expert psychological analysis, and Ordered Network Analysis (ONA). Using an AI-generated social media thread designed to simulate politically charged commentary, the analysis models relationships among motivational constructs. Three licensed mental health experts interpreted the same dataset, generating divergent but plausible psychological narratives. These differences illustrate the interpretive ambiguity inherent in discourse and underscore the value of multiple analytical lenses. By integrating ONA, the study offers a novel means of visualizing temporal sequencing and affective escalation. However, ethical caution is warranted when applying motivational modeling to real individuals, particularly public figures. This work contributes to efforts within the QE community to refine tools for analyzing affectively charged discourse.