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Harnessing Machine Learning in Tackling Domestic Violence-An Integrative Review.

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Summary

Machine learning (ML) shows great promise for detecting domestic violence (DV) in digital text. While challenges exist, ML applications in DV research are expanding, particularly with social media data.

Keywords:
abusebig datadomestic violenceintimate partner violencemachine learning

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Area of Science:

  • Public Health
  • Health Informatics
  • Computational Social Science

Background:

  • Domestic violence (DV) poses significant threats to mental and physical health.
  • The proliferation of digital data offers new avenues for DV research.
  • Machine learning (ML) presents a promising approach for analyzing digital text to detect and predict DV.

Purpose of the Study:

  • To review and synthesize existing research on machine learning applications in domestic violence.
  • To identify common ML methods, data sources, and outcomes in DV research.
  • To highlight the potential and challenges of using ML for DV detection and prediction.

Main Methods:

  • Systematic review of 3588 articles from four databases.
  • Inclusion criteria resulted in 22 relevant articles.
  • Analysis of ML methodologies, data sources, algorithms, and outcomes.

Main Results:

  • Supervised ML (12 articles) and unsupervised ML (7 articles) were predominantly used.
  • Common algorithms included Random Forest, Support Vector Machine, and Naïve Bayes.
  • Social media emerged as a key data source, with Latent Dirichlet Allocation (LDA) used for topic modeling.

Conclusions:

  • ML offers significant potential for DV classification, prediction, and exploration, especially with social media data.
  • Key challenges include adoption barriers, data source limitations, and extensive data preparation.
  • Ongoing development of ML algorithms is crucial for overcoming these obstacles in DV research.