Unpacking Divorce

Feature-Based Machine Learning Interpretation of Sociological Patterns

Dados Bibliográficos

ID12171371
AutoresHüseyin Doğan (0000-0002-0375-8437, Department of Social Work, Patnos Social Work College, Agri Ibrahim Cecen University, Agri, Turkey, autor correspondente), Emre Kılınç (0000-0002-5250-9322, Department of Computer Technologies, Patnos Vocational High School, Agri Ibrahim Cecen University, Agri, Turkey)
Ano2025
Data de publicação2025-10-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoSocial Science Computer Review (JOURNAL)
Identificadores do periódicoISSN: 0894-4393 • E-ISSN: 1552-8286
EditoraSAGE Publishing (PUBLISHER • US)
DOI10.1177/08944393251386073
OpenAlexW4414699090
IdiomaEN
Referências citadas39

This study introduces a machine learning-based framework aimed at identifying and interpreting the most influential factors contributing to divorce. Utilizing data from the 2021 Turkey Family Structure Survey, we apply Random Forest and Logistic Regression models to rank predictors based on their relative impact on marital dissolution. The goal is to uncover which socio-legal, temporal, and behavioral variables most significantly contribute to the divorce outcome within a culturally grounded dataset. Both models converge on a set of dominant features—psychological conflict responses, cultural marital rituals, and political disagreements—demonstrating their robust influence across different algorithmic paradigms. Feature importance scores derived from model outputs and explainability tools (e.g., permutation and coefficient-based rankings) reveal consistent patterns and offer interpretable insights aligned with sociological theory. This approach contributes to computational sociology by showcasing how machine learning can be used not only for prediction, but more importantly, for identifying statistical patterns that reflect social structures and behavioral dynamics associated with divorce outcomes

Feature (linguistics · Interpretation (philosophy · Logistic regression · Marital status · Outcome (game theory · Random forest · Rank (graph theory · Set (abstract data type · Unpacking · Crime Patterns and Interventions · Demographic Trends and Gender Preferences · Family Dynamics and Relationships