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Crime Victims

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Applying Natural Language Processing to Evaluate News Media Coverage of Bullying and Cyberbullying.

Prevention science : the official journal of the Society for Prevention Research
Bullying events have frequently been the focus of coverage by news media, including news stories about teens whose death from suicide was attributed to cyberbullying. Previous work has shown that news media coverage is influential to readers in areas...

Rape narratives analysis through natural language processing: Survivor self-label, narrative time span, faith, and rape terminology.

Psychological trauma : theory, research, practice and policy
OBJECTIVE: Rape-survivor identity is a sign of recovery and positive therapeutic progress among rape victims. This study is one of the few to focus on factors predicting self-labeling as a survivor among self-acknowledged rape victims by evaluating t...

Identification of Child Survivors of Sex Trafficking From Electronic Health Records: An Artificial Intelligence Guided Approach.

Child maltreatment
Survivors of child sex trafficking (SCST) experience high rates of adverse health outcomes. Amidst the duration of their victimization, survivors regularly seek healthcare yet fail to be identified. This study sought to utilize artificial intelligenc...

Predicting Intimate Partner Violence Perpetration Among Young Adults Experiencing Homelessness in Seven U.S. Cities Using Interpretable Machine Learning.

Journal of interpersonal violence
Young adults experiencing homelessness (YAEH) are at higher risk for intimate partner violence (IPV) victimization than their housed peers. This is often due to their increased vulnerability to abuse and victimization before and during homelessness, ...

Predictive analysis of bullying victimization trajectory in a Chinese early adolescent cohort based on machine learning.

Journal of affective disorders
BACKGROUND: The development of bullying victimization among adolescents displays significant individual variability, with general, group-based interventions often proving insufficient for partial victims. This study aimed to conduct a machine learnin...

Predicting bullying victimization among adolescents using the risk and protective factor framework: a large-scale machine learning approach.

BMC public health
BACKGROUND: Bullying, encompassing physical, psychological, social, or educational harm, affects approximately 1 in 20 United States teens aged 12-18. The prevalence and impact of bullying, including online bullying, necessitate a deeper understandin...