A human-centered systematic literature review of the computational approaches for online sexual risk detection
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Accepted manuscript
Date
2021-10-13
DOI
Authors
Razi, Afsaneh
Kim, Seunghyun
Alsoubai, Ashwaq
Stringhini, Gianluca
Solorio, Thamar
De Choudhury, Munmun
Wisniewski, Pamela J.
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Accepted manuscript
OA Version
Citation
A. Razi, S. Kim, A. Alsoubai, G. Stringhini, T. Solorio, M. De Choudhury, P.J. Wisniewski. 2021. "A Human-Centered Systematic Literature Review of the Computational Approaches for Online Sexual Risk Detection" Proceedings of the ACM on Human-Computer Interaction, Volume 5, Issue CSCW2, pp.1-38. https://doi.org/10.1145/3479609
Abstract
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.
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© 2021 Association for Computing Machinery.