Visual Representation for Patterned Proliferation of Social Media Addiction: Quantitative Model and Network Analysis

TL;DR

This paper develops a mathematical and network-based model to analyze social media addiction, revealing how nonlinear dynamics and peer influence contribute to addiction spread and suggesting strategies for prevention.

Contribution

It introduces a novel mathematical model incorporating peer-influence relapse and applies network and reaction-diffusion analysis to understand addiction patterns.

Findings

Nonlinearity makes society prone to persistent addiction once a threshold is crossed.

Peer-influence relapse is a major barrier to overcoming social media addiction.

Strategies to prevent addiction are mathematically explored and discussed.

Abstract

With the advancement of information technology, more people, especially young adults, are getting addicted to the use of different social media platforms. Despite immense useful applications in communication and interactions, the habit of spending excessive time on these social media platforms is becoming addictive, causing different consequences, like anxiety, depression, health problems, and many more. Here, we mathematically explored a model of social media addiction, including a peer-influence relapse. We have further done the complex network analysis for a heterogenic synthetic society. Finally, we explore spatiotemporal pattern formation in a diffusive social system using the reaction-diffusion approach. Our model shows how the existent nonlinearity in the system makes it difficult to make society social media addiction free once it crosses a certain threshold. Some possible strategies are explored mathematically to prevent social media addiction, and the importance of peer-influenced relapse has been identified as a major barrier.