A systematic literature review of the use of computational text analysis methods in intimate partner violence research
Purpose: 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 ana...
| VerfasserInnen: | ; ; ; |
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| Medienart: | Elektronisch Aufsatz |
| Sprache: | Englisch |
| Veröffentlicht: |
2023
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| In: |
Journal of family violence
Jahr: 2023, Band: 38, Heft: 6, Seiten: 1205-1224 |
| Online-Zugang: |
Volltext (kostenfrei) Volltext (kostenfrei) |
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| Schlagwörter: |
MARC
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| 100 | 1 | |a Neubauer, Lilly |e VerfasserIn |0 (orcid)0000-0002-8670-5938 |4 aut | |
| 245 | 1 | 2 | |a A systematic literature review of the use of computational text analysis methods in intimate partner violence research |c Lilly Neubauer, Isabel Straw, Enrico Mariconti, Leonie Maria Tanczer |
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| 500 | |a Literaturverzeichnis: Seite 1221-1224 | ||
| 520 | |a Purpose: 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. Methods: This 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. Results: The 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. Conclusions: Text mining methodologies offer promising data collection and analysis techniques for IPV research. Future work in this space must consider ethical implications of computational approaches. | ||
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| 650 | 4 | |a Domestic Violence | |
| 650 | 4 | |a Intimate Partner Violence | |
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| 700 | 1 | |a Tanczer, Leonie Maria |e VerfasserIn |0 (DE-588)1226123252 |0 (DE-627)1746395427 |4 aut | |
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