Pathfinding of Digital False Information Diffusion by Conformal Field Theory and Feynman's Green's Function: Multiple Analysis Considering Stress Energy Tensor and Symmetry of Virasoro Operators

Abstract

This study delves into the application of Conformal Field Theory (CFT) to understand information diffusion within digital media and its broader social implications. Focusing on the digital native generation, vulnerable to misinformation and data privacy issues, the research highlights the urgency of addressing psychological health in the digital realm. By leveraging CFT's analytical power, particularly in tracing information flows and assessing the impact of misinformation, the study offers novel insights into the dynamics of digital communication. It proposes a model for predicting and analyzing the spread of biased content, emphasizing the need for improved information literacy and personal data protection. This paper explores the concept of infinitesimal conformal transformations using the Virasoro operator, forming a conformal transformation group. In the examination of multiple analytical methods, the approach to quantitatively analyze cases that are likely to expose the risk of spreading filter bubbles such as negative information, rumors, and fake news is to incorporate a two-dimensional conformal field theory approach using the Ward-Takahashi identity and path integral with trace anomalies and metric are introduced. Trace anomalies and metrics were introduced. A trace anomaly refers to the spread of information in an unexpected way, usually indicating anomalous behavior or hidden dynamics in the system. Metric variation hypothesized and analyzed how diffusion characteristics, such as the rate and pattern of information diffusion, vary over time.

Short Discussion Paper, Various hypotheses and inferences with applications to social physics This paper is partially an attempt to utilize "Generative AI" and was written with educational intent. There are currently no plans for it to become a peer-reviewed paper