Affiliation:
1. Center for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hong Kong
2. Health and Community Systems, School of Nursing, University of Pittsburgh, Pittsburgh, PA 15260, USA
3. Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA
Abstract
Domestic violence (DV) is a public health crisis that threatens both the mental and physical health of people. With the unprecedented surge in data available on the internet and electronic health record systems, leveraging machine learning (ML) to detect obscure changes and predict the likelihood of DV from digital text data is a promising area health science research. However, there is a paucity of research discussing and reviewing ML applications in DV research. Methods: We extracted 3588 articles from four databases. Twenty-two articles met the inclusion criteria. Results: Twelve articles used the supervised ML method, seven articles used the unsupervised ML method, and three articles applied both. Most studies were published in Australia (n = 6) and the United States (n = 4). Data sources included social media, professional notes, national databases, surveys, and newspapers. Random forest (n = 9), support vector machine (n = 8), and naïve Bayes (n = 7) were the top three algorithms, while the most used automatic algorithm for unsupervised ML in DV research was latent Dirichlet allocation (LDA) for topic modeling (n = 2). Eight types of outcomes were identified, while three purposes of ML and challenges were delineated and are discussed. Conclusions: Leveraging the ML method to tackle DV holds unprecedented potential, especially in classification, prediction, and exploration tasks, and particularly when using social media data. However, adoption challenges, data source issues, and lengthy data preparation times are the main bottlenecks in this context. To overcome those challenges, early ML algorithms have been developed and evaluated on DV clinical data.
Subject
Health, Toxicology and Mutagenesis,Public Health, Environmental and Occupational Health
Reference66 articles.
1. World Health Organization (2023, January 06). Global and Regional Estimates of Violence Against Women: Prevalence and Health Effects of Intimate Partner Violence and Non-Partner Sexual Violence. Available online: http://app.who.int/iris/bitstream/10665/85239/1/9789241564625_eng.pdf.
2. “I never told anyone until the #metoo movement”: What can we learn from sexual abuse and sexual assault disclosures made through social media?;Alaggia;Child Abus. Negl.,2020
3. Why I Stayed/Left: An Analysis of Voices of Intimate Partner Violence on Social Media;Cravens;Contemp. Fam. Ther.,2015
4. The Online Disinhibition Effect;Suler;CyberPsychol. Behav.,2004
5. Home is not always a haven: The domestic violence crisis amid the COVID-19 pandemic;Kofman;Psychol. Trauma Theory Res. Pract. Policy,2020