Identifying Risk Patterns in Brazilian Police Reports Preceding
  Femicides: A Long Short Term Memory (LSTM) Based Analysis
Vinicius Lima Jaque Almeida de Oliveira
Femicide refers to the killing of a female victim, often perpetrated by an
intimate partner or family member, and is also associated with gender-based
violence. Studies have shown that there is a pattern of escalating violence
leading up to these killings, highlighting the potential for prevention if the
level of danger to the victim can be assessed. Machine learning offers a
promising approach to address this challenge by predicting risk levels based on
textual descriptions of the violence. In this study, we employed the Long Short
Term Memory (LSTM) technique to identify patterns of behavior in Brazilian
police reports preceding femicides. Our first objective was to classify the
content of these reports as indicating either a lower or higher risk of the
victim being murdered, achieving an accuracy of 66%. In the second approach, we
developed a model to predict the next action a victim might experience within a
sequence of patterned events. Both approaches contribute to the understanding
and assessment of the risks associated with domestic violence, providing
authorities with valuable insights to protect women and prevent situations from
escalating.