Purpose

The second of two articles from a larger study whose aim was to teach a computer to detect innuendo (or signaling) about a victim's credibility in incident reports of rape. This study explored if the words expressed or not expressed, intentionally or not, influenced case progression and outcomes.

Methods

We employed machine learning, specifically text classification, to identify predictive phrases. Sample consisted of 5638 incident reports of rape with a sexual assault kit from a U.S., urban Midwestern jurisdiction.

Results

As hypothesized, predictive phrases were different in cases that stalled earlier. Cases not recommended for prosecution lacked detail and more heavily mentioned: (in)actions of victims, actions that stall cases, and procedural words. Reports where victims were not believed or unfounded were similarly vague, procedural, and terse. Cases recommended for prosecution predictively mentioned suspects and the rape statute.

Conclusions

We taught a computer to detect signaling via words that were predictive of case progression and outcomes. Negative signals about a victim's credibility often presented as unqualified statements of “fact” or observations or procedural words, indicating a focus on the process vs. victim or suspect. Implications and recommendations are provided, including how unqualified doubts about victims' credibility have substantial public safety consequences.

中文翻译：

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使用机器学习评估强奸报告：有关受害者可信度的“信号”词语可预测调查和起诉结果

目的

一项更大型研究的两篇文章中的第二篇，其目的是教会计算机检测强奸事件报告中受害者可信度的影射（或信号）。这项研究探讨了表达或未表达的言语、有意或无意的表达是否会影响病例的进展和结果。

方法

我们采用机器学习，特别是文本分类，来识别预测短语。样本包含 5638 份来自美国中西部城市管辖区的性侵犯工具包强奸事件报告。

结果

正如假设的那样，预测短语在较早停滞的情况下是不同的。不建议起诉的案件缺乏细节，更多地提到了：受害者的行为、拖延案件的行为以及程序性措辞。那些不相信受害者或毫无根据的报道也同样含糊、程序性和简洁。建议起诉的案件预见性地提到了嫌疑人和强奸法规。

结论

我们教计算机通过预测病例进展和结果的单词来检测信号。关于受害者可信度的负面信号通常表现为对“事实”或观察或程序性词语的无保留陈述，表明对过程的关注与对受害者或嫌疑人的关注。提供了启示和建议，包括对受害者可信度的无条件怀疑如何造成重大公共安全后果。