  This study introduces a new numerical model to simulate how information is
comprehended and processed on social networks, using continuous "Phase Field
Modeling" variables (phiA, phiB, phiC) to represent individual users' opinions.
It captures the immediate and two-way nature of social media interactions,
reproducing the spread and feedback of information. The model incorporates
psychological and social factors like confirmation bias and opinion rigidity to
analyze information processing and opinion development among users. It also
explores the dynamics of opinion segregation and interaction in and out of
filter bubbles, offering a quantitative view of opinion dynamics on platforms
like social networking services (SNS). This approach combines theoretical
models with real-world social network data to study the effects of information
concentration on opinion formation and the phenome Phase Field Modeling of
opinion polarization and echo chamber effects on SNS. 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.
