Published on in Vol 21, No 3 (2019): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13067, first published .
Automated Analysis of Domestic Violence Police Reports to Explore Abuse Types and Victim Injuries: Text Mining Study

Automated Analysis of Domestic Violence Police Reports to Explore Abuse Types and Victim Injuries: Text Mining Study

Automated Analysis of Domestic Violence Police Reports to Explore Abuse Types and Victim Injuries: Text Mining Study

Journals

  1. Hwang Y, Zheng L, Karystianis G, Gibbs V, Sharp K, Butler T. Domestic violence events involving autism: a text mining study of police records in New South Wales, 2005-2016. Research in Autism Spectrum Disorders 2020;78:101634 View
  2. Karystianis G, Cabral R, Han S, Poon J, Butler T. Utilizing Text Mining, Data Linkage and Deep Learning in Police and Health Records to Predict Future Offenses in Family and Domestic Violence. Frontiers in Digital Health 2021;3 View
  3. Withall A, Karystianis G, Duncan D, Hwang Y, Kidane A, Butler T, Bowers B. Domestic Violence in Residential Care Facilities in New South Wales, Australia: A Text Mining Study. The Gerontologist 2022;62(2):223 View
  4. Barth R, Jiranek H. Strengthening Relationships between Couples to Respond to Domestic Violence: a Commentary on Policy Changes Needed to Support this Evolution. Journal of Family Violence 2023;38(4):761 View
  5. BERKİTEN ERGİN A, ÖZDİLEK R, ÖZDEMİR S, BAYDEMİR C. Violence in Pregnancy: Scale Validity and Reliability Study in Turkey. Kocaeli Üniversitesi Sağlık Bilimleri Dergisi 2022;8(1):44 View
  6. Douglas H. The use of fire and threats to burn in the context of domestic and family violence and coercive control. Current Issues in Criminal Justice 2023;35(1):27 View
  7. Philbrick W, Milnor J, Deshmukh M, Mechael P. Information and communications technology use to prevent and respond to sexual and gender‐based violence in low‐ and middle‐income countries: An evidence and gap map. Campbell Systematic Reviews 2022;18(4) View
  8. Wilson M, Spike E, Karystianis G, Butler T. Nonfatal Strangulation During Domestic Violence Events in New South Wales: Prevalence and Characteristics Using Text Mining Study of Police Narratives. Violence Against Women 2022;28(10):2259 View
  9. 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
  10. Chen X, Ding H, Fang S, Chen W. Predicting the Success of Internet Social Welfare Crowdfunding Based on Text Information. Applied Sciences 2022;12(3):1572 View
  11. Chatzisymeonidis S, Kioskli K. An observational study of domestic violence in Greece during COVID-19 through police records: The profile of heinous crimes between nuclear and extended family relationships. Policing: A Journal of Policy and Practice 2023;17(1) View
  12. Neubauer L, Straw I, Mariconti E, Tanczer L. A Systematic Literature Review of the Use of Computational Text Analysis Methods in Intimate Partner Violence Research. Journal of Family Violence 2023;38(6):1205 View
  13. Moodley J, Hove R. Pastoral Care and Mental Health in Post-Pandemic South Africa: A Narrative Review Exploring New Ways to Serve Those in Our Care. Religions 2023;14(4):477 View
  14. 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
  15. Khosa-Nkatini H, Mofokeng J. Siphefumula Ngenxeba in our own homes: Gender-based violence during COVID-19 pandemic. Theologia Viatorum 2023;47(1) View
  16. 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
  17. 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
  18. Sarzaeim P, Mahmoud Q, Azim A, Bauer G, Bowles I. A Systematic Review of Using Machine Learning and Natural Language Processing in Smart Policing. Computers 2023;12(12):255 View
  19. Karystianis G, Chowdhury N, Sheridan L, Reutens S, Wade S, Allnutt S, Kim M, Poynton S, Butler T. Text mining domestic violence police narratives to identify behaviours linked to coercive control. Crime Science 2024;13(1) View
  20. Bey R, Cohen A, Trebossen V, Dura B, Geoffroy P, Jean C, Landman B, Petit-Jean T, Chatellier G, Sallah K, Tannier X, Bourmaud A, Delorme R. Natural language processing of multi-hospital electronic health records for public health surveillance of suicidality. npj Mental Health Research 2024;3(1) View
  21. 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
  22. 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
  23. 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

Books/Policy Documents

  1. Mitiagina E, Borodataya M, Volchenkova E, Ershova N, Luchinina M, Kotelnikov E. Electronic Governance and Open Society: Challenges in Eurasia. View
  2. Soldner F, Tanczer L, Hammocks D, Lopez-Neira I, Johnson S. The Palgrave Handbook of Gendered Violence and Technology. View
  3. Deane T. Gender-Based Violence and Femicide in South Africa. View