识别巴西警方报告中谋杀女性案件前的风险模式：基于长短期记忆（LSTM）的分析
Identifying Risk Patterns in Brazilian Police Reports Preceding Femicides: A Long Short Term Memory (LSTM) Based Analysis
AI总结：
本研究利用LSTM模型分析巴西警方报告，通过分类暴力风险等级（准确率66%）和预测受害者可能经历的下一个行为，为家庭暴力预防和女性保护提供决策支持。
摘要：
谋杀女性是指杀害女性受害者，通常由亲密伴侣或家庭成员实施，并且与基于性别的暴力相关。研究表明，在这些杀人事件发生之前存在暴力逐步升级的模式，这凸显了如果能够评估受害者所处危险程度，则具有预防的潜力。机器学习通过基于暴力的文本描述来预测风险水平，为解决这一挑战提供了一种有前景的方法。在本研究中，我们采用长短期记忆（LSTM）技术来识别巴西警方报告中谋杀女性案件发生前的行为模式。我们的第一个目标是将这些报告的内容分类为表明受害者被谋杀的风险较低或较高，实现了66%的准确率。在第二种方法中，我们开发了一个模型，用于预测受害者在模式化事件序列中可能经历的下一个行为。这两种方法都有助于理解和评估与家庭暴力相关的风险，为当局提供有价值的见解，以保护女性并防止情况升级。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
