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Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

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Detecting Patterns of Intimate Partner Violence Using Qualitative Analyses and Machine Learning Algorithms.

Ying Zhang^1,2, Jun Fang^3,4, Ambika Krishnakumar⁵

¹Department of Psychology, Clarkson University, Potsdam, NY, USA. yinzhang@clarkson.edu.

Prevention Science : the Official Journal of the Society for Prevention Research

|May 6, 2026

Summary

Social media data reveals intimate partner violence (IPV) behaviors. Machine learning accurately classifies IPV subtypes and uncovers contextual details, enhancing understanding and intervention strategies for IPV survivors.

Keywords:

Intimate partner violence (IPV)Machine learning (ML)Social media analysis Text mining

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Area of Science:

Computational Social Science
Public Health
Digital Humanities

Background:

Intimate partner violence (IPV) survivors utilize social media for sharing experiences and seeking support.
Social media data offers valuable insights complementing traditional data sources on IPV victimization.

Purpose of the Study:

To identify the range of IPV behaviors using qualitative coding.
To compare the effectiveness of machine learning (ML) text classification against qualitative coding for IPV behaviors.
To determine if unsupervised ML captures additional IPV behaviors or contextual information.

Main Methods:

Analysis of 400 women's posts from IPV-related online forums.
Application of qualitative content analysis.
Utilized supervised text classification (Random Forest, Neural Networks) and unsupervised topic modeling (Latent Dirichlet Allocation).

Main Results:

Supervised ML models achieved high accuracy (F1 scores .62–.85) in classifying IPV subtypes.
Both qualitative and ML approaches identified physical, sexual, psychological/emotional abuse, and coercive control.
ML models revealed relational, child-related contexts, violence indicators, legal, and spatial contexts often missed by qualitative coding alone.

Conclusions:

Machine learning techniques can effectively analyze qualitative social media data on IPV.
Integrating ML with qualitative analysis enhances the identification of IPV behaviors and contexts.
This approach holds potential for developing timely and effective IPV interventions.