A Human-Centered Systematic Literature Review of the Computational Approaches for Online Sexual Risk Detection
A Human-Centered Systematic Literature Review of the Computational Approaches for Online Sexual Risk Detection
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DOI：
10.1145/3479609
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发表时间：
2021-10
影响因子：
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通讯作者：
Afsaneh Razi;Seunghyun Kim;Ashwaq Alsoubai;G. Stringhini;T. Solorio;M. Choudhury;P. Wisniewski
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文献类型：
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作者：
Afsaneh Razi;Seunghyun Kim;Ashwaq Alsoubai;G. Stringhini;T. Solorio;M. Choudhury;P. Wisniewski
在大数据和人工智能的时代，在线风险检测已成为流行的研究主题。从检测在线骚扰到对青年的性掠夺，计算风险检测的最新发现有可能保护特别脆弱的人群免受在线伤害。但是，这是一项高风险，高回报的努力，需要一种系统的，以人为中心的方式来综合不同应用领域的不同研究机构，以便我们可以确定最佳实践，潜在的差距，并设定战略研究议程以更好的社会方式利用这些方法。因此，我们进行了全面的文献综述，分析了使用文本或元数据/多媒体进行在线性风险检测的73个同行评审的文章。我们确定了性修饰（75％），性贩运（12％）以及性骚扰和/或虐待（12％）是现存文献中存在的三种性风险检测。此外，我们发现，这项工作的大多数（93％）集中在事后确定性掠食者，而不是采取更细微的方法来识别潜在的受害者和有问题的模式，这些模式可用于在发生之前防止受害。许多研究依靠公共数据集（82％）和第三方注释者（33％）来建立地面真理并训练其算法。最后，这项工作的大多数（78％）主要集中在其模型的算法性能评估上，很少（4％）与真实用户评估这些系统。因此，我们敦促计算风险检测研究人员整合以人为本的方法来开发和评估性风险检测算法，以确保这项重要工作对社会的广泛影响。
In the era of big data and artificial intelligence, online risk detection has become a popular research topic. From detecting online harassment to the sexual predation of youth, the state-of-the-art in computational risk detection has the potential to protect particularly vulnerable populations from online victimization. Yet, this is a high-risk, high-reward endeavor that requires a systematic and human-centered approach to synthesize disparate bodies of research across different application domains, so that we can identify best practices, potential gaps, and set a strategic research agenda for leveraging these approaches in a way that betters society. Therefore, we conducted a comprehensive literature review to analyze 73 peer-reviewed articles on computational approaches utilizing text or meta-data/multimedia for online sexual risk detection. We identified sexual grooming (75%), sex trafficking (12%), and sexual harassment and/or abuse (12%) as the three types of sexual risk detection present in the extant literature. Furthermore, we found that the majority (93%) of this work has focused on identifying sexual predators after-the-fact, rather than taking more nuanced approaches to identify potential victims and problematic patterns that could be used to prevent victimization before it occurs. Many studies rely on public datasets (82%) and third-party annotators (33%) to establish ground truth and train their algorithms. Finally, the majority of this work (78%) mostly focused on algorithmic performance evaluation of their model and rarely (4%) evaluate these systems with real users. Thus, we urge computational risk detection researchers to integrate more human-centered approaches to both developing and evaluating sexual risk detection algorithms to ensure the broader societal impacts of this important work.