A multi-site data sample for analyzing the online commercial sex ecosystem.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-02-11 DOI:10.1038/s41597-025-04442-w

Nickolas K Freeman, Gregory J Bott, Burcu B Keskin, Tiffany L Marcantonio

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引用次数: 0

Abstract

Online sex advertisements (sex ads) have been linked to many U.S. sex trafficking cases. However, since the closure of the dominant website, Backpage.com (Backpage), many competing sites have emerged that are hosted in countries where U.S. law enforcement organizations have no jurisdiction. Although the online ecosystem has changed significantly, very little research uses data from sites other than Backpage, and even less uses data from multiple sites. This paper presents an anonymized dataset derived from the text and image artifacts of more than 10 million sex ads. By making this dataset publicly available, we aim to reduce barriers to entry for researchers interested in conducting data-driven counter-trafficking research. The dataset can be used to test hypotheses related to sex ads and intersite connectivity, understand the posting processes employed by prominent sites in the current online sex ad ecosystem, and develop multidisciplinary approaches for estimating ad legitimacy. Progress in any of these areas can result in potentially lifesaving interventions for ST victims.

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.