Artificial Intelligence and Electrical & Electronics Engineering: AIEEE Open Access
Mitigating Social Media-Induced Dopamine Loops through Machine Learning
Abstract
Ayush Verma and Abhiram Nandiraju
The rise of social media has sparked concerns about its negative impact on mental health, driven by the dopamine- induced feedback loops designed to foster user engagement. This paper investigates how machine learning (ML) and app development can be utilized to mitigate the effects of these dopamine-driven behaviors, specifically focusing on social media addiction. By integrating neuroscience, behavioral science, and digital well-being research, we designed an app that provides personalized interventions aimed at reducing screen time and improving overall well-being. A machine learning model was employed to predict user behavior and deliver tailored recommendations to curb social media usage. The app was tested with a sample group of high school students, and the results demonstrated significant reductions in screen time, increased user engagement, and improvements in well-being scores among the experimental group. Our findings suggest that personalized ML-driven solutions can provide an effective means to combat social media addiction and promote healthier digital habits. Future research could further enhance the app’s functionality and explore the long- term impact of such interventions on user behavior.