Sociometric network analysis in illicit drugs research: A scoping review

PLOS ONE RESEARCH ARTICLE Sociometric network analysis in illicit drugs research: A scoping review Naomi Zakimi ID1*, Alissa Greer1, Martin Bouchard1, Arshpreet Dhillon1, Alison Ritter2 1 School of Criminology, Simon Fraser University, Burnaby, British Columbia, Canada, 2 Drug Policy Modelling Program, Social Policy Research Centre, University of New South Wales, Sydney, New South Wales, Australia * a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 Abstract Background OPEN ACCESS Citation: Zakimi N, Greer A, Bouchard M, Dhillon A, Ritter A (2023) Sociometric network analysis in illicit drugs research: A scoping review. PLoS ONE 18(2): e0282340. https://doi.org/10.1371/journal. pone.0282340 Editor: Carl A. Latkin, Johns Hopkins University Bloomberg School of Public Health, UNITED STATES Sociometric or whole network analysis, a method used to analyze relational patterns among social actors, emphasizes the role of social structure in shaping behaviour. Such method has been applied to many aspects of illicit drug research, including in the areas of public health, epidemiology, and criminology. Previous reviews about social networks and drugs have lacked a focus on the use of sociometric network analysis for illicit drugs research across disciplines. The current scoping review aimed to provide an overview of the sociometric network analysis methods used in illicit drugs research and to assess how such methods could be used for future research. Received: June 29, 2022 Methods Accepted: February 12, 2023 A systematic search of six databases (Web of Science, ProQuest Sociology Collection, Political Science Complete, PubMed, Criminal Justice Abstracts, and PsycINFO) returned 72 relevant studies that met the inclusion criteria. To be included, studies had to mention illicit drugs and use whole social network analysis as one of their methods. Studies were summarized quantitatively and qualitatively using a data-charting form and a description of the studies’ main topics. Published: February 27, 2023 Copyright: © 2023 Zakimi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper and its Supporting information files. Funding: This work was supported by The Social Sciences and Humanities Research Council (SSHRC) under Grant #435-2021-0749 (AG). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Results Sociometric network analysis in illicit drugs research has grown in popularity in the last decade, using mostly descriptive network metrics, such as degree centrality (72.2%) and density (44.4%). Studies were found to belong to three study domains. The first, drug crimes investigated network resilience and collaboration patterns in drug trafficking networks. The second domain, public health, focused on the social networks and social support of people who use drugs. Finally, the third domain focused on the collaboration networks of policy, law enforcement, and service providers. Competing interests: The authors have declared that no competing interests exist. PLOS ONE | https://doi.org/10.1371/journal.pone.0282340 February 27, 2023 1 / 24 PLOS ONE Scoping review of social networks and illicit drugs Conclusion Future illicit drugs research using whole network SNA should include more diverse data sources and samples, incorporate mixed and qualitative methods, and apply social network analysis to study drug policy. Introduction Social network analysis (SNA) is both a theoretical perspective and a methodological approach to examining the social connections and structures among social beings. Its foundations can be traced back to early 1900s business, anthropology, and sociology research [1–3]. Business and organizational research conducted at Harvard’s School of Business Administration played an important role in developing and popularizing early SNA methods [2, 4, 5]. Jacob Moreno and Helen Jennings, working within the psychiatric and psychological fields, are most often credited with the birth of SNA as we know it today [6–8]. Since then, SNA has been used in a wide variety of disciplines in the social sciences, such as anthropology, criminology, and education, and has turned into a “vibrant multidisciplinary field” [9]. SNA is a particularly useful approach because it treats people as interconnected social beings, emphasizing the role of structure in shaping behaviour. As a method, SNA provides a variety of tools stemming from graph theory to study all kinds of interactions, from human and animal relationships to institutional and government processes [2, 9]. Broadly speaking, social network studies can focus on a network as a whole (e.g., the network of all friendships in a classroom) or on egocentric networks (e.g., the individual network of each student) [10]. Most commonly, SNA studies will administer questionnaires and/or interviews to participants to elicit names of contacts that represent specific relationships or ties (i.e., social support, friendship) [11]. Another important type of network data comes from archival sources, which contain data that were not collected with the purpose of conducting SNA, such as police files or historical documents. This data collection strategy is useful when studying hard-to-reach populations, such as criminal organizations, politicians, or historical figures [12–14]. Although rare, observations can be a rich data source for researchers who are able to conduct fieldwork [15]. This data collection method can help uncover relationships participants may not have shared in a questionnaire or interview [16, 17]. Collectively, this variety of sources—observational, archival, and questionnaires or interviews—facilitates the multidisciplinary use of SNA. Once data are collected, different analytical tools are available to study social networks. SNA techniques can be divided into three categories: descriptive network graphs, whole network or individual quantitative measures/metrics, and advanced network modelling [18]. First, descriptive network graphs can be used to visualize social ties. Network graphs can help illustrate and describe network data, as well as uncover relationship patterns that may be difficult to grasp using other methods. Second, network measures can be calculated for both the network as a whole or for individuals within the network. Whole network (or sociometric) measures—the interest for the current review—can provide information about density (i.e., the portion of total possible connections that actually exist in the network) and centralization (i.e., how focused a network is on a single node or person) [19, 20]. Individual-level measures can detect the most central member in (...truncated)