Machine Learning for Detecting Social Media Addiction Patterns: Analyzing User Behavior and Mental Health Data

Tahir Ehsan ()
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Tahir Ehsan: Department of Computer Science and Software Engineering, National University of Sciences and Technology, Islamabad

International Journal of Innovations in Science & Technology, 2024, vol. 6, issue 4, 1789-1807

Abstract: In the modern world, communication through social networks has become the norm, and people have started to worry about the possible addictive properties of social networks and their influence on mental states. This research aims to propose a Machine Learning (ML) framework for examining patterns of Social Media (SM) addiction, while also acknowledging the dearth of research on developing appropriate detection tools. We obtained data for the research through surveys, which led to the creation of a larger dataset that included aspects of user behavior, mental health parameters, and social media statistics. We use a Random Forest Classifier to predict different levels of addiction, including low, medium, and high levels, while considering behavioral and psychological characteristics. Further analysis of the research findings shows that more hours spent on social media, especially, are associated with higher levels of distractions, irritation, and other forms of emotional problems among SM users. Additionally, thefeature importance analysis reveals that indicators such as emotional comparisons and the need for self-validation also contribute to addiction. Therefore, these results indicate a high, critical level of awareness and require the development of intervention programs associated with social media addiction while considering the close connection between user behavior and mental health. Lastly, the study adds knowledge on social media addiction and helps to open the next stage in research to identify the prevention of negative impacts on mental health due to addiction to social networks.

Keywords: Social Media Addiction; Machine Learning Framework; Random Forest Classifier; User Behavior Analysis and Mental Health Impact (search for similar items in EconPapers)
Date: 2024
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