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Grant Foundation","doi-asserted-by":"crossref","award":["187941"],"award-info":[{"award-number":["187941"]}],"id":[{"id":"10.13039\/100001143","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["1827700"],"award-info":[{"award-number":["1827700"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Hum.-Comput. Interact."],"published-print":{"date-parts":[[2021,10,13]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.1145\/3479609","type":"journal-article","created":{"date-parts":[[2021,10,19]],"date-time":"2021-10-19T02:39:17Z","timestamp":1634611157000},"page":"1-38","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":65,"title":["A Human-Centered Systematic Literature Review of the Computational Approaches for Online Sexual Risk Detection"],"prefix":"10.1145","volume":"5","author":[{"given":"Afsaneh","family":"Razi","sequence":"first","affiliation":[{"name":"University of Central Florida, Orlando, FL, USA"}]},{"given":"Seunghyun","family":"Kim","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, GA, USA"}]},{"given":"Ashwaq","family":"Alsoubai","sequence":"additional","affiliation":[{"name":"University of Central Florida, Orlando, FL, USA"}]},{"given":"Gianluca","family":"Stringhini","sequence":"additional","affiliation":[{"name":"Boston University, Boston, MA, USA"}]},{"given":"Thamar","family":"Solorio","sequence":"additional","affiliation":[{"name":"University of Houston, Cypress, TX, USA"}]},{"given":"Munmun","family":"De Choudhury","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, GA, USA"}]},{"given":"Pamela J.","family":"Wisniewski","sequence":"additional","affiliation":[{"name":"University of Central Florida, Orlando, FL, USA"}]}],"member":"320","published-online":{"date-parts":[[2021,10,18]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Victims of trafficking and violence protection act of","author":"Protection Act Trafficking Victims","year":"2000","unstructured":"Trafficking Victims Protection Act. 2000. 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