Computational 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.
A systematic review was conducted to PRISMA guidelines, searching 8 databases and identifying 22 unique studies to include in the review.
The 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). Only a few studies commented on the ethics of computational IPV research.
Text mining methodologies offer promising data collection and analysis techniques for IPV research. However, future work in this space must consider the ethical implications of computational approaches.
For further information please see: A Systematic Literature Review of the Use of Computational Text Analysis Methods in Intimate Partner Violence Research | SpringerLink or contact Lilly Neubauer at j.neubauer@cs.ucl.ac.uk or Dr Leonie Tanczer at l.tanczer@ucl.ac.uk
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