Using Predictive Modeling to Assess Social Media's Impact on Parental Well-Being: A Pilot Study
Journal
Communications in Computer and Information Science
HCI International 2026 Posters
Date Issued
2026
Author(s)
Tutillo, Jimena
Martinez, Danilo
Type
Book chapter
Abstract
Social media is part of daily life, facilitating communication, entertainment, and immediate access to information. However, it can negatively affect personal well-being and contribute to the development of addictive behavior. The objective of this paper is to examine how social media users in the economically productive age group of 36 to 56 years are affected by these platforms, and how machine learning (ML) and app development can be used to mitigate the impacts of these dopamine-stimulated behaviors. For this purpose, an empirical study using the Social Media Disorder Test showed that 37.5% of the sample had prolonged usage. To aid in the solution, an app was developed that uses machine learning (ML) to classify addiction levels into 8 categories across 3 social media apps. The app monitors usage in real time and issues alerts for excessive consumption patterns to reduce digital dependence. The results show real patterns of moderate addiction among users. It also seeks to raise awareness of social media use so they can set limits and reduce the impact of their use