Explaining Social Media Addiction Dynamics with SHAP Analysis: Key Influences and Preventive Strategies

Serpil Sevimli Deniz

Article Details

Explaining Social Media Addiction Dynamics with SHAP Analysis: Key Influences and Preventive Strategies

Authors:

Serpil Sevimli Deniz

Publisher:

Fırat University

Main Topics:

Ekonometri (Diğer)

Journal:

Fırat Üniversitesi Sosyal Bilimler Dergisi Vol.35 Issue 2, pp.643-657

Document Type:

Article

Publication Date:

5/2025

Document Language:

Turkish

Document ID (Org No.):

2001347727

pISSN:

1300-9702

Keywords:

Abstract:

 This study employs SHAP Shapley Additive Explanations analysis to determine key factors influencing social media addiction. Data from 500 participants were analyzed, focusing on daily social media usage, screen time, and usage motivations. The findings indicate that entertainment-driven social media use significantly increases addiction risk, whereas usage for work or educational purposes has a lower impact. The study highlights the importance of preventive strategies such as digital detox programs and awareness campaigns to reduce addiction risks.A key innovation of this research is the application of SHAP analysis in the social sciences. Unlike traditional statistical models, SHAP provides a transparent and individualized explanation of how different factors contribute to addiction. By making machine learning models more interpretable, SHAP analysis enhances the understanding of complex psychosocial issues like digital addiction. Additionally, it facilitates the development of personalized intervention strategies, allowing for more effective addiction prevention measures. This study demonstrates that integrating explainable artificial intelligence XAI into social science research can provide deeper insights and innovative solutions for addressing social media addiction.

 This study employs SHAP Shapley Additive Explanations analysis to determine key factors influencing social media addiction. Data from 500 participants were analyzed, focusing on daily social media usage, screen time, and usage motivations. The findings indicate that entertainment-driven social media use significantly increases addiction risk, whereas usage for work or educational purposes has a lower impact. The study highlights the importance of preventive strategies such as digital detox programs and awareness campaigns to reduce addiction risks.A key innovation of this research is the application of SHAP analysis in the social sciences. Unlike traditional statistical models, SHAP provides a transparent and individualized explanation of how different factors contribute to addiction. By making machine learning models more interpretable, SHAP analysis enhances the understanding of complex psychosocial issues like digital addiction. Additionally, it facilitates the development of personalized intervention strategies, allowing for more effective addiction prevention measures. This study demonstrates that integrating explainable artificial intelligence XAI into social science research can provide deeper insights and innovative solutions for addressing social media addiction.

 This study employs SHAP Shapley Additive Explanations analysis to determine key factors influencing social media addiction. Data from 500 participants were analyzed, focusing on daily social media usage, screen time, and usage motivations. The findings indicate that entertainment-driven social media use significantly increases addiction risk, whereas usage for work or educational purposes has a lower impact. The study highlights the importance of preventive strategies such as digital detox programs and awareness campaigns to reduce addiction risks.A key innovation of this research is the application of SHAP analysis in the social sciences. Unlike traditional statistical models, SHAP provides a transparent and individualized explanation of how different factors contribute to addiction. By making machine learning models more interpretable, SHAP analysis enhances the understanding of complex psychosocial issues like digital addiction. Additionally, it facilitates the development of personalized intervention strategies, allowing for more effective addiction prevention measures. This study demonstrates that integrating explainable artificial intelligence XAI into social science research can provide deeper insights and innovative solutions for addressing social media addiction.

 Bu araştırmada, sosyal medya bağımlılığını etkileyen temel faktörleri belirlemek amacıyla SHAP Shapley Additive Explanations analizi kullanılmıştır. Çalışma, 500 katılımcıdan elde edilen veriler doğrultusunda, günlük sosyal medya kullanım süresi, ekran süresi ve kullanım motivasyonları gibi değişkenleri detaylı şekilde incelemiştir. Elde edilen bulgular, özellikle eğlence amaçlı kullanımın sosyal medya bağımlılığı riskini önemli ölçüde artırdığını göstermektedir. Buna karşın, iş veya eğitim odaklı kullanımın bağımlılık üzerindeki etkisi daha sınırlıdır. Çalışma, sosyal medya bağımlılığıyla mücadelede dijital detoks programları, farkındalık kampanyaları ve sağlıklı dijital alışkanlıkların teşvik edilmesi gibi önleyici stratejiler önermektedir.Bu araştırmanın özgün ve yenilikçi yönlerinden biri, SHAP analizinin sosyal bilimlerde uygulanarak bireysel ve toplumsal düzeyde açıklanabilir sonuçlar sunmasıdır. SHAP analizi, makine öğrenmesi modellerinin kara kutu doğasını aşarak, hangi faktörlerin bağımlılık gelişiminde ne ölçüde etkili olduğunu şeffaf bir şekilde ortaya koymaktadır. Bu yöntem, sosyal bilimlerde açıklanabilir yapay zekâ yaklaşımlarına öncülük etmekte ve sosyal medya bağımlılığı gibi karmaşık psikososyal sorunların birey bazında daha derinlemesine anlaşılmasını sağlamaktadır. Ayrıca, kişiye özel müdahale stratejilerinin geliştirilmesine imkân tanıyan SHAP tabanlı modeller, sosyal medya bağımlılığını önleme ve yönetme sürecinde etkili ve yenilikçi çözümler sunmaktadır.

Article Details

Explaining Social Media Addiction Dynamics with SHAP Analysis: Key Influences and Preventive Strategies

Authors:

Serpil Sevimli Deniz

Publisher:

Fırat University

Main Topics:

Ekonometri (Diğer)

Journal:

Fırat Üniversitesi Sosyal Bilimler Dergisi Vol.35 Issue 2, pp.643-657

Document Type:

Article

Publication Date:

5/2025

Document Language:

Turkish

Document ID (Org No.):

2001347727

pISSN:

1300-9702

Social Media Addiction

SHAP Analysis

Interaction Dynamics

Social Media UsageSosyal Medya Bağımlılığı

SHAP Analizi

Etkileşim Dinamikleri

Sosyal Medya Kullanımı

Issues

Article Details

Authors: Serpil Sevimli Deniz

Publisher: Fırat University

Main Topics: Ekonometri (Diğer)

Journal: Fırat Üniversitesi Sosyal Bilimler Dergisi Vol.35 Issue 2, pp.643-657

Document Type: Article

Publication Date: 5/2025

Document Language: Turkish

Document ID (Org No.): 2001347727

pISSN: 1300-9702

Issues

Authors:

Serpil Sevimli Deniz

Publisher:

Fırat University

Main Topics:

Ekonometri (Diğer)

Journal:

Fırat Üniversitesi Sosyal Bilimler Dergisi Vol.35 Issue 2, pp.643-657

Document Type:

Article

Publication Date:

5/2025

Document Language:

Turkish

Document ID (Org No.):

2001347727

pISSN:

1300-9702