ABSTRACT

Unfounded child sexual abuse (CSA) allegations take investigative resources from real cases and have detrimental consequences for the people involved. The Finnish Investigative Instrument of Child Sexual Abuse (FICSA) supports investigators by estimating the probability of a CSA allegation being true based on the child’s background information. In the current study, we aimed at making FICSA resistant to deception. Two gender-specific questionnaires with FICSA questions and additional “trap” questions were constructed. The trap questions were designed to seem statistically related to CSA although they were not. Combining the answers of 278 real victims and 275 16–year-old students, instructed to simulate being CSA victims, we built a Naïve Bayes classifier that successfully separated the two groups (AUC = 0.91 for boys and 0.92 for girls). By identifying false allegations early in the investigation, authorities’ resources can be directed towards allegations that are more likely true, effectively helping actual CSA victims.

摘要

毫无根据的儿童性虐待 (CSA) 指控从真实案件中获取调查资源，并对相关人员造成不利后果。芬兰儿童性虐待调查工具 (FICSA) 通过根据儿童的背景信息估计 CSA 指控属实的可能性来支持调查人员。在当前的研究中，我们旨在使 FICSA 能够抵抗欺骗。构建了两个带有 FICSA 问题和附加“陷阱”问题的性别特定问卷。陷阱问题的设计似乎与 CSA 在统计上相关，但实际上并非如此。结合 278 名真实受害者和 275 名 16 岁学生的答案，他们被指示模拟成为 CSA 受害者，我们构建了一个朴素贝叶斯分类器，成功地将两组（AUC = 男孩为 0.91，女孩为 0.92）。通过在调查早期识别虚假指控，当局可以将资源用于更有可能真实的指控，从而有效地帮助实际的 CSA 受害者。