This paper is in the following e-collection/theme issue:

Mobile Health (mhealth) (3150) Violence including Domestic Violence/Abuse (135) Text-messaging (SMS, WeChat etc)-Based Interventions (697) Methods and New Tools in Mental Health Research (497)

Published on in Vol 22, No 12 (2020): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23725, first published .
Prevalence of Mental Illnesses in Domestic Violence Police Records: Text Mining Study

Prevalence of Mental Illnesses in Domestic Violence Police Records: Text Mining Study

Prevalence of Mental Illnesses in Domestic Violence Police Records: Text Mining Study

Authors of this article:

George Karystianis1 Author Orcid Image ;   Annabeth Simpson2 Author Orcid Image ;   Armita Adily1 Author Orcid Image ;   Peter Schofield3 Author Orcid Image ;   David Greenberg4 Author Orcid Image ;   Handan Wand5 Author Orcid Image ;   Goran Nenadic6 Author Orcid Image ;   Tony Butler1 Author Orcid Image
Article Authors Cited by (14) Tweetations (4) Metrics

Journals

  1. Karystianis G, Cabral R, Adily A, Lukmanjaya W, Schofield P, Buchan I, Nenadic G, Butler T. Mental Illness Concordance Between Hospital Clinical Records and Mentions in Domestic Violence Police Narratives: Data Linkage Study. JMIR Formative Research 2022;6(10):e39373 View
  2. Oatley G. Themes in data mining, big data, and crime analytics. WIREs Data Mining and Knowledge Discovery 2022;12(2) View
  3. Buchner V, Hamm S, Medenica B, Molendijk M. Linguistic Analysis of Online Domestic Violence Testimonies in the Context of COVID-19. Sage Open 2023;13(1) View
  4. Hui V, Constantino R, Lee Y. Harnessing Machine Learning in Tackling Domestic Violence—An Integrative Review. International Journal of Environmental Research and Public Health 2023;20(6):4984 View
  5. Lovell R, Klingenstein J, Du J, Overman L, Sabo D, Ye X, Flannery D. Using machine learning to assess rape reports: “Signaling” words about victims' credibility that predict investigative and prosecutorial outcomes. Journal of Criminal Justice 2023;88:102107 View
  6. Geurts R, Raaijmakers N, Delsing M, Spapens T, Wientjes J, Willems D, Scholte R. Assessing the Risk of Repeat Victimization Using Structured and Unstructured Police Information. Crime & Delinquency 2023;69(9):1736 View
  7. Lovell R, Klingenstein J, Du J, Overman L, Sabo D, Ye X, Flannery D. Using machine learning to assess rape reports: Sentiment analysis detection of officers' “signaling” about victims' credibility. Journal of Criminal Justice 2023;88:102106 View
  8. Reutens S, Karystianis G, Withall A, Butler T. Characteristics of domestic violence perpetrators with dementia from police records using text mining. Frontiers in Psychiatry 2024;15 View
  9. Prof. Vishal Nayakwadi , Ganesh Nehe , Manish Chaudhari , Sonali Powar . Study of Violence Against Women and its Characteristics Using Application of Data Mining Techniques. International Journal of Scientific Research in Science, Engineering and Technology 2024;11(3):194 View
  10. Gnawali S, Atteraya M, Kim E. Association Between Domestic Violence and Mental Health Among Nepalese Women: Results from a Nationally Representative Sample. Journal of Interpersonal Violence 2024 View
  11. Karystianis G, Kita S, Lerigo F, Sheridan L, Butler T. Characteristics of adult male victims in intimate heterosexual relationships from domestic violence police narratives. Crime Science 2024;13(1) View
  12. Hadjimatheou K, Quiroz Flores A, Weir R, Skevington T. Using unsupervised machine learning to find profiles of domestic abuse perpetrators. Policing: A Journal of Policy and Practice 2024;18 View
  13. Hagos A, Karystianis G, Kita S, Montalto A, Lerigo F, Butler T. Domestic violence among adult male victims in non-intimate relationships: a text mining study using NSW police narratives. Crime Science 2025;14(1) View

Books/Policy Documents

  1. Briody L, Walklate S, Weston S. Mental Health, Crime and Justice. View