Identification and Detection of Human Trafficking Using Language Models
Identification and Detection of Human Trafficking Using Language Models
复制标题
使用语言模型识别和检测人口贩运
DOI：
10.1109/eisic49498.2019.9108860
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Cara Jones
中科院分区：
文献类型：
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作者：
Jessica Zhu;Lin Li;Cara Jones
在本文中，我们提出了一种新的基于语言模型的方法来检测人口贩运广告和贩运指标。该系统利用语言模型来学习成人服务广告中的语言结构，自动选择关键字特征列表，并训练机器学习模型来检测人口贩运广告。该方法可以解释，并可适用于贩运者所使用的不断变化的关键词。我们将这种方法应用于Trafficking-10 k数据集，并表明它比以前利用广告文本和图像进行检测的模型取得了更好的结果。此外，我们证明，我们的系统可以成功地应用于检测可疑的人口贩运组织，并根据其风险评分对这些组织进行排名。这种方法为执法部门提供了一种强大的新能力，可以快速识别涉嫌贩运人口的广告和组织，并允许使用数据进行更积极的警务。
In this paper, we present a novel language model-based method for detecting both human trafficking ads and trafficking indicators. The proposed system leverages language models to learn language structures in adult service ads, automatically select a list of keyword features, and train a machine learning model to detect human trafficking ads. The method is interpretable and adaptable to changing keywords used by traffickers. We apply this method to the Trafficking-10k dataset and show that it achieves better results than the previous models that leverage both ad text and images for detection. Furthermore, we demonstrate that our system can be successfully applied to detect suspected human trafficking organizations and rank these organizations based on their risk scores. This method provides a powerful new capability for law enforcement to rapidly identify ads and organizations that are suspected of human trafficking and allow more proactive policing using data.