Reconstructing and analyzing the transnational human trafficking network

Goist, Mitchell; Chen, Ted Hsuan Yun; Boylan, C.

doi:10.1145/3341161.3342879

Cited by 5 publications

(7 citation statements)

References 33 publications

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“…Social network analysis could be applied to examine the structure of transnational kidney trade networks, as applied in a human trafficking setting by Goist et al . 42 In addition to social network analysis, various types of logistic regressions could also be applied to understand the determinants of a country becoming a seller, buyer or surgery country.…”

Section: Discussionmentioning

confidence: 99%

“…Although the data presented here are limited in South Asia, analytical tools developed here can be applied to examine the structure and attributes of transnational kidney trade networks worldwide. Social network analysis could be applied to examine the structure of transnational kidney trade networks, as applied in a human trafficking setting by Goist et al 42. In addition to social network analysis, various types of logistic regressions could also be applied to understand the determinants of a country becoming a seller, buyer or surgery country.…”

Section: Discussionmentioning

confidence: 99%

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Identifying kidney trade networks using web scraping data

Siddique

Wilson

et al. 2022

BMJ Glob Health

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Kidney trade has been on the rise despite the domestic and international law enforcement aiming to protect the vulnerable population from potential exploitation. Regional hubs are emerging in several parts of the world including South Asia, Central America, the Middle East and East Asia. Kidney trade networks reported in these hot spots are often complex systems involving several players such as buyers, sellers and surgery countries operating across international borders so that they can bypass domestic laws in sellers and buyers’ countries. The exact patterns of the country networks are, however, largely unknown due to the lack of a systematic approach to collect the data. Most of the kidney trade information is currently available in the form of case studies, court materials and news articles or reports, and no comprehensive database exists at this time. The present study thus explored online newspaper scraping to systematically collect 10 419 news articles from 24 major English newspapers in South Asia (January 2016 to May 2019) and build transnational kidney trade networks at the country level. Additionally, this study applied text mining techniques to extract words from each news article and developed machine learning algorithms to identify kidney trade and non-kidney trade news articles. Our findings suggest that online newspaper scraping coupled with the machine learning method is a promising approach to compile such data, especially in the dire shortage of empirical data.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Identifying kidney trade networks using web scraping data

Siddique

Wilson

et al. 2022

BMJ Glob Health

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“…Studies on human trafficking, regardless of whether they focus on climate impacts, have sought to understand the determinants to trafficking inflow and outflow using country-level analysis (Cho 2015;Bell, Flynn and Martinez Machain 2018), dyadic gravity-type models (Hernandez and Rudolph 2015), and network analysis (Goist, Chen and Boylan 2019). However, trafficking is a complex problem (International Labour Organization 2012, 2014, and as such involves not only origin and destination countries -where trafficked individuals come from and the endpoint of where they are taken -but also "intermediary" countries through which individuals are trafficked en route to their destinations (Kangaspunta 2003).…”

Section: Introductionmentioning

confidence: 99%

“…Yet, the downstream complexities of the diffusion of the international anti-trafficking regime have yet to be explicitly examined in quantitative analysis of trafficking patterns. Gravity-type models and network analysis start to get at important dyadic factors of trafficking flows, and network analysis in particular is able to capture system level dependencies in trafficking patterns, but existing studies generally remain focused on trafficking origins, destinations, and flows between them (Goist, Chen and Boylan 2019), through which it is difficult to analyze trafficking spread and route divergence.…”

Section: Introductionmentioning

confidence: 99%

“…To answer our questions, we conduct a comprehensive evaluation of trafficking and climate change using new combined tools in machine learning and network analysis. Using data on human trafficking networks mined using machine learning tools from Trafficking in Persons (TIP) reports from the U.S. State Department by Goist, Chen and Boylan (2019), we tested the impact of anti-trafficking laws and natural shocks on real trafficking flows and network structures using the exponential random graph model, which has been previously used to study human trafficking (Goist, Chen and Boylan 2019) and other illicit trade networks (Aziani, Berlusconi and Giommoni 2021;Norbutas 2018). First, we tested whether and how anti-trafficking laws impact trafficking patterns, focusing on the potential for route divergence through intermediary states.…”

Section: Introductionmentioning

confidence: 99%

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Legal Institutions and Human Trafficking in a Shifting Climate

Chen,

Wang,

Koren

2024

Preprint

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While both international law and climate change researchers have shown how legal institutions and climate change impact human trafficking flows in origin and destination countries, little attention is given to the ``intermediary'' countries through which individuals are trafficked en route to their destination. This study develops a two-part theory to explain how the effects of anti-trafficking legalization and climate shocks impact these intermediary countries. First, we argue that the diffusion of anti-trafficking laws will induce pressures on traffickers to diversify their trafficking routes. We then theorize that because climate change-induced shocks impose costs on both states and traffickers, these costs confound the effect of legal institutions by incentivizing traffickers to shift their operations into less costly areas. Using data on human trafficking networks mined from the U.S. State Department's Trafficking in Persons Report, we show with inferential network analysis (1) how legal institutions impact trafficking through these intermediary countries and (2) how natural shocks impact the importance of different states as preferred trafficking routes.

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