Identifying Risk Patterns in Brazilian Police Reports Preceding Femicides: A Long Short Term Memory (LSTM) Based Analysis
This work addresses the critical issue of preventing femicides by providing authorities with machine learning tools to assess domestic violence risks, though it is incremental in applying existing methods to this specific domain.
The study tackled the problem of predicting femicide risk from Brazilian police reports using an LSTM model, achieving 66% accuracy in classifying reports as lower or higher risk and predicting next actions in sequences of violence.
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.