The Network Structure of Opioid Distribution on a Darknet Cryptomarket

Duxbury, Scott W.; Haynie, Denise L.

doi:10.1007/s10940-017-9359-4

Cited by 61 publications

(45 citation statements)

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“…On the other hand these markets may represent an as-yet underutilised opportunity for informing policy more widely. For example, utilising digital trace information could help inform ways of disrupting their trade from a law enforcement perspective (Broséus et al, 2016; Mireault, Ouellette, Décary-Hétu, Crispino, & Broséus, 2016), or using the data for an analysis of the structure of drug networks more generally (Duxbury & Haynie, 2017). Whilst looking at the specifics of the Australian online drug trade, Cunliffe, Martin, Décary-Hétu, and Aldridge, (2017) compared online to offline prices and found that the large differential in the methamphetamine market is indicative of the higher levels of risk through violent exchanges present in that specific market, driven in no small part by the involvement of motorbike gangs, and posed the question as to whether this differential can be used to uncover violent drug markets prospectively.…”

Section: Introductionmentioning

confidence: 99%

Nonmedical prescription psychiatric drug use and the darknet: A cryptomarket analysis

Cunliffe

Décary‐Hétu

Pollak

2019

International Journal of Drug Policy

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Background Nonmedical prescription psychiatric drug use (NMPDU) is an increasing global health problem, with recent concern focusing on darknet cryptomarkets as sources of procurement. There is a shortage of evidence regarding comparative worldwide NMPDU trends, due in part to data collection difficulties. This problem is particularly marked for non-opioid drugs, particularly those psychiatric drugs which act on the central nervous system (CNS) and have high misuse potential and are associated with high levels of dependency and fatal overdose. This paper therefore has two goals: 1) to report on the kinds of psychiatric prescription drugs available on cryptomarkets, and 2) to use this data to uncover temporal and geographical trends in sales of these products, potentially informing policy regarding NMPDU more generally. Method Digital trace data collected from 31 cryptomarkets in operation between September 2013 and July 2016 was analysed by country of origin descriptively and for trends in the sales for 7 psychiatric drug groupings, based on their main indication or intended use in psychiatric practice. Results Sedatives (such as diazepam and alprazolam) and CNS stimulants (mainly Adderall, modafinil and methylphenidate) had the greatest share of sales, but usage and trends varied by location. The UK has high and rising levels of sedative sales, whilst the USA has the greatest stimulant sales and increasing sedative rates. Sales of drugs used in the treatment of opioid dependency are also substantial in the USA. The picture is less clear in mainland Europe with high sales levels reported in unexpected Central and Northern European countries. There is evidence of a move towards the more potent sedative alprazolam – already implicated as a source of problematic NMPDU in the USA – in Australia and the UK. Sales of drugs such as antidepressants, antipsychotics, mood stabilisers and antidementia drugs – all drugs with limited abuse potential – were negligible, indicating minimal levels of online cryptomarket procurement for self-medicating mental health problems. Conclusion Predominantly, psychiatric drugs with potent sedative, stimulant or euphoriant effects are sold on cryptomarkets and this varies by country. With some caveats regarding the limitations of cryptomarket digital trace data taken into account, the study of trends of these products sold online over time may offer a novel and increasingly important window onto wider drug purchasing habits.

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Section: Introductionmentioning

confidence: 99%

Nonmedical prescription psychiatric drug use and the darknet: A cryptomarket analysis

Cunliffe

Décary‐Hétu

Pollak

2019

International Journal of Drug Policy

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“…Like many other criminal networks, the offender-victim knife crime network is markedly diffused, with a network density of 0 (note: the network density was rounded to 0). For the sake of comparison, the density of a dark web opioid distribution market studied by Duxbury and Haynie [ 66 ] was 0.002, while Jihadi networks examined by Krebs [ 67 ] and Morselli, Giguere, and Petit [ 68 ] ranged from 0.2 to 0.4. Furthermore, this network possessed a modularity of 0.997, with 2,923 identifiable communities.…”

Section: Resultsmentioning

confidence: 99%

Victims, offenders and victim-offender overlaps of knife crime: A social network analysis approach using police records

2020

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Knife crime is a source of concern for the police in England and Wales, however little published research exists on this crime type. Who are the offenders who use knives to commit crime, when and why? Who are their victims, and is there a victim-offender overlap? What is the social network formation for people who are exposed to knife crime? Using a multidimensional approach, our aim is to answer these questions about one of England and Wales’ largest jurisdictions: Thames Valley. We first provide a state-of-the-art narrative review of the knife crime literature, followed by an analysis of population-level data on central tendency and dispersion of knife crimes reported to the police (2015–2019), on offences, offenders, victims, victim-offender overlaps and gang-related assaults. Social network analysis was used to explore the formations of offender-victim networks. Our findings show that knife crime represents a small proportion of crime (1.86%) and is associated largely with violence offenses. 16–34 year-old white males are at greatest risk of being the victims, offenders or victim-offenders of knife crime, with similar relative risks between these three categories. Both knife offenders and victims are likely to have a criminal record. Knife crimes are usually not gang-related (less than 20%), and experienced mostly between strangers, with the altercation often a non-retaliatory ‘one-off event’. Even gang-related knife crimes do not follow ‘tit-for-tat’ relationships—except when the individuals involved have extensive offending histories and then are likely to retaliate instantaneously. We conclude that while rare, an incident of knife crime remains predicable, as a substantial ratio of offenders and victims of future knife crime can be found in police records. Prevention strategies should not be focused on gang-related criminals, but on either prolific violent offenders or repeat victims who are known to the police—and therefore more susceptible to knife crime exposure.

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“…Different research efforts have been made to monitor Tor domains, and insights gained have been used to develop techniques that allow for the supervision of suspicious activities. These techniques range from solutions based on Natural Language Processing [ 15 , 16 ], to Computer Vision [ 17 , 18 ] or Graph Theory [ 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Rectification and Super-Resolution Enhancements for Forensic Text Recognition

Blanco‐Medina

Fidalgo

Alegre

et al. 2020

Sensors

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Retrieving text embedded within images is a challenging task in real-world settings. Multiple problems such as low-resolution and the orientation of the text can hinder the extraction of information. These problems are common in environments such as Tor Darknet and Child Sexual Abuse images, where text extraction is crucial in the prevention of illegal activities. In this work, we evaluate eight text recognizers and, to increase the performance of text transcription, we combine these recognizers with rectification networks and super-resolution algorithms. We test our approach on four state-of-the-art and two custom datasets (TOICO-1K and Child Sexual Abuse (CSA)-text, based on text retrieved from Tor Darknet and Child Sexual Exploitation Material, respectively). We obtained a 0.3170 score of correctly recognized words in the TOICO-1K dataset when we combined Deep Convolutional Neural Networks (CNN) and rectification-based recognizers. For the CSA-text dataset, applying resolution enhancements achieved a final score of 0.6960. The highest performance increase was achieved on the ICDAR 2015 dataset, with an improvement of 4.83% when combining the MORAN recognizer and the Residual Dense resolution approach. We conclude that rectification outperforms super-resolution when applied separately, while their combination achieves the best average improvements in the chosen datasets.

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The Network Structure of Opioid Distribution on a Darknet Cryptomarket

Cited by 61 publications

References 50 publications

Nonmedical prescription psychiatric drug use and the darknet: A cryptomarket analysis

Nonmedical prescription psychiatric drug use and the darknet: A cryptomarket analysis

Victims, offenders and victim-offender overlaps of knife crime: A social network analysis approach using police records

Rectification and Super-Resolution Enhancements for Forensic Text Recognition

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