A Theory-Driven Approach to Fake News/Information Disorder Analysis and Explanation via Target-Based Emotion–Stance Analysis (TESA) and Interpretive Graph Generation (IGG)

Information disorder (IDO) presents a persistent challenge to society, necessitating innovative approaches to understanding its dynamics beyond just merely detecting it. This study introduces a theory-driven framework that integrates advanced natural language processing (NLP) with deep learning, utilizing the target-based emotion–stance analysis (TESA) approach to analyze emotion and stance dynamics within IDO content. Complementing TESA, interactive graph generation (IGG) is applied for scalable and interpretable qualitative analyses. Employing a mixed-methods approach, the study leverages TESA for target-centric emotion and stance analysis, evaluating target-based classifiers on both human-annotated and synthetic datasets. Additionally, the study explores synthetic data generation using generative AI to enrich the analysis, applying IGG to map complex data interactions. The study also found that integrating synthetic data developed from human annotations enhanced model performance, particularly for emotion classification tasks. Results demonstrate that IDO narratives significantly differ from non-IDO narratives, frequently leveraging negative emotions such as anger and disgust to manipulate public perception. TESA proved effective in capturing these nuanced variations, while IGG facilitated the triangulation of such findings via the scalable interpretation of emotional narratives, revealing that IDO content often amplifies polarizing and antagonistic perspectives. By combining TESA and IGG, this research emphasizes the importance of using NLP to extract and examine the emotional and stance nuances toward targets of interest within IDO context. This approach not only deepens theoretical insights into IDO’s persuasive mechanisms but also supports the development of practical tools for analyzing and managing the influence of IDO on public discourse.