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.

该研究使用长短期记忆（LSTM）技术对巴西警方报告中的女性亲杀案前的行为模式进行分析，并通过文本描述预测风险水平。第一目标是将报告内容分类为较低或较高的受害者被谋杀风险，实现了 66% 的准确率。第二个方法是根据事件序列预测受害者可能经历的下一步行动，为了理解和评估家庭暴力的风险，为当局提供有价值的洞察力以保护妇女并防止情况升级。

基于长短期记忆网络（LSTM）的巴西警方报告中前置妇女谋杀风险模式识别

Identifying Risk Patterns in Brazilian Police Reports Preceding  Femicides: A Long Short Term Memory (LSTM) Based Analysis

Different ways of linguistically expressing the same real-world event can
lead to different perceptions of what happened. Previous work has shown that
different descriptions of gender-based violence (GBV) influence the reader's
perception of who is to blame for the violence, possibly reinforcing
stereotypes which see the victim as partly responsible, too. As a contribution
to raise awareness on perspective-based writing, and to facilitate access to
alternative perspectives, we introduce the novel task of automatically
rewriting GBV descriptions as a means to alter the perceived level of
responsibility on the perpetrator. We present a quasi-parallel dataset of
sentences with low and high perceived responsibility levels for the
perpetrator, and experiment with unsupervised (mBART-based), zero-shot and
few-shot (GPT3-based) methods for rewriting sentences. We evaluate our models
using a questionnaire study and a suite of automatic metrics.

通过使用无监督，零样本和少量样本的方法来改写低责任感和高责任感的语句，以改变对施暴者的责任感知水平，该研究提出了一项新的任务，即自动改写基于性别的暴力描述，以促进替代视角的访问和提高透视写作的意识。

意大利女性被害新闻的责任视角转移

Responsibility Perspective Transfer for Italian Femicide News

Different linguistic expressions can conceptualize the same event from
different viewpoints by emphasizing certain participants over others. Here, we
investigate a case where this has social consequences: how do linguistic
expressions of gender-based violence (GBV) influence who we perceive as
responsible? We build on previous psycholinguistic research in this area and
conduct a large-scale perception survey of GBV descriptions automatically
extracted from a corpus of Italian newspapers. We then train regression models
that predict the salience of GBV participants with respect to different
dimensions of perceived responsibility. Our best model (fine-tuned BERT) shows
solid overall performance, with large differences between dimensions and
participants: salient _focus_ is more predictable than salient _blame_, and
perpetrators' salience is more predictable than victims' salience. Experiments
with ridge regression models using different representations show that features
based on linguistic theory similarly to word-based features. Overall, we show
that different linguistic choices do trigger different perceptions of
responsibility, and that such perceptions can be modelled automatically. This
work can be a core instrument to raise awareness of the consequences of
different perspectivizations in the general public and in news producers alike.

本研究通过对意大利报纸语料库中自动提取的 GBV 描述的大规模感知调查和回归模型的训练，展示了不同语言选择会引发不同的责任感知，并且这样的感知可以被自动建模。