A Systematic Literature Review of the Use of Computational Text Analysis Methods in Intimate Partner Violence Research

PurposeComputational text mining methods are proposed as a useful methodological innovation in Intimate Partner Violence (IPV) research. Text mining can offer researchers access to existing or new datasets, sourced from social media or from IPV-related organisations, that would be too large to analy...

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Detalles Bibliográficos
Autor principal: Neubauer, Lilly (Autor)
Otros Autores: Straw, Isabel ; Mariconti, Enrico ; Tanczer, Leonie Maria
Tipo de documento: Electrónico Artículo
Lenguaje:Inglés
Publicado: 2023
En: Journal of family violence
Año: 2023, Volumen: 38, Número: 6, Páginas: 1205-1224
Acceso en línea: Presumably Free Access
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Palabras clave:
Systematic Review
Natural Language Processing
Machine Learning
Text Analysis
Text Mining
Domestic Violence
Intimate Partner Violence
Descripción
Sumario:PurposeComputational text mining methods are proposed as a useful methodological innovation in Intimate Partner Violence (IPV) research. Text mining can offer researchers access to existing or new datasets, sourced from social media or from IPV-related organisations, that would be too large to analyse manually. This article aims to give an overview of current work applying text mining methodologies in the study of IPV, as a starting point for researchers wanting to use such methods in their own work.MethodsThis article reports the results of a systematic review of academic research using computational text mining to research IPV. A review protocol was developed according to PRISMA guidelines, and a literature search of 8 databases was conducted, identifying 22 unique studies that were included in the review.ResultsThe included studies cover a wide range of methodologies and outcomes. Supervised and unsupervised approaches are represented, including rule-based classification (n = 3), traditional Machine Learning (n = 8), Deep Learning (n = 6) and topic modelling (n = 4) methods. Datasets are mostly sourced from social media (n = 15), with other data being sourced from police forces (n = 3), health or social care providers (n = 3), or litigation texts (n = 1). Evaluation methods mostly used a held-out, labelled test set, or k-fold Cross Validation, with Accuracy and F1 metrics reported. Only a few studies commented on the ethics of computational IPV research.ConclusionsText mining methodologies offer promising data collection and analysis techniques for IPV research. Future work in this space must consider ethical implications of computational approaches.
ISSN:1573-2851
DOI:10.1007/s10896-023-00517-7