Mixing politics and crime – The prevalence and decline of political discourse on the cryptomarket

Munksgaard, Rasmus; Demant, Jakob

doi:10.1016/j.drugpo.2016.04.021

Cited by 68 publications

(65 citation statements)

References 26 publications

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“…Previous studies have shown that many vendors sell drugs not only for revenue but also to support the libertarian ideology (e.g. Kruithof et al 2016: 80;Munksgaard & Demant 2016;Ormsby 2016: 66). Showing this support offers an image that these vendors are on the same side with their customers, against legislators and law enforcement.…”

Section: Pirates and Goodfellas: Criminal Imagesmentioning

confidence: 99%

User names of illegal drug vendors on a darknet cryptomarket

Hämäläinen¹

2015

Onoma

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The illegal drug trade has recently found a new route: darknet cryptomarkets. On these Ebay-like marketplaces, located in an anonymous network, drug sellers and buyers from all over the world make deals without meeting each other in person. Drug vendors are known by their user names, and those names could have a huge financial significance for their owners. This study examines the characteristics of those vendor user names and contrasts them to both other types of user names as well as to real-life commercial names. The data for the study were collected from AlphaBay, which was the largest cryptomarket in early 2017. Many vendors share information of themselves through their user names, referring to their products, home country and whether they work alone or in a group. Names are also used to create various images of vendor's business. Some vendors try to create an image of a normal, legal business by using company and brand name vocabulary or by plagiarising famous real-life brand names. Other vendors, however, appear to even highlight their criminality, referring to illegal activities or well-known 46 LASSE HÄMÄLÄINEN real-life or fictional drug criminals. Vendor names also include references to the desired effects of drugs and the high quality of products and service. One-third of names are not connected to drugs in any visible way. Overall, vendor names more closely resemble other user names, but they clearly have also been influenced by commercial names.

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Section: Pirates and Goodfellas: Criminal Imagesmentioning

confidence: 99%

User names of illegal drug vendors on a darknet cryptomarket

Hämäläinen¹

2015

Onoma

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“…Activity to date has explored marketplace dynamics and activity on Silk Road 1 (Barratt, Ferris, & Winstock, 2014;Christin, 2012;Martin, 2014a;Phelps & Watt;Soska & Christin, 2015;Van Hout & Bingham, 2013a:b;, Agora (Tzanetakis et al, 2016;Van Buskirk et al, 2016b), Silk Road 2 (Broséus et al, 2016;Dolliver, 2015;Martin, 2014b;Soska & Christin, 2015) and Evolution (Rhumorbarbe et al 2016). Particular efforts also have focused on monitoring listings (Aldridge & Décary-Hétu, 2014Burns, Roxburgh, Bruno, & van Buskirk, 2014;Christin, 2013;Dolliver, 2015), analysis of vendor reputation (Hardy & Norgaard, 2015), user and vendor perceptions of quality, trust, impacts of drug cryptomarkets on drug use trajectories, and forum harm reduction (Bancroft & Scott Reid, 2016;Barratt et al, 2016;Tzanetakis et al, 2016), changing political content on cryptomarkets (Munksgaard and Demant 2016) and the combination of digital, chemical and physical information to reconstruct vendor activity (Rhumorbarbe et al 2016).…”

Section: Introductionmentioning

confidence: 99%

New psychoactive substances (NPS) on cryptomarket fora: An exploratory study of characteristics of forum activity between NPS buyers and vendors

Hout

Hearne

2017

International Journal of Drug Policy

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“…Simply put, we maintain that by using a high number of topics, by focusing on one welldelineated meta-subject (such as trade policy or the national economy), and by using a corpus that features only a single genre of texts (speeches, newspaper articles), topic modelling becomes a useful tool for discourse analysts. This hypothesis is demonstrated by the case studies at the end of this paper, and it has already implicitly applied in the literature (Munksgaard & Demant, 2016;Törnberg & Törnberg, 2016a, pp. 6-7, 2016b), but our most important arguments to back up this claim, are theoretical.…”

Section: Meta-theoretical Fitmentioning

confidence: 53%

“…Most discourse analyses that study large text corpora employ fairly simple tools that count words, collocations, and concordances. More complex models and algorithms such as topic modelling have only entered into the consideration of discourse analysts very recently, and to a limited degree (Levy & Franklin, 2014;Jaworska & Nanda, 2016;Munksgaard & Demant, 2016;Tornberg & Tornberg, 2016a, 2016b are some of the few examples of the explicit use of topic modelling for discourse analysis). This is noteworthy, since topic modelling has been around since 2003.…”

Section: Introductionmentioning

confidence: 99%

Topic models meet discourse analysis: a quantitative tool for a qualitative approach

Jacobs

Tschötschel

2019

International Journal of Social Research Methodology

113

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Quantitative text analysis tools have become increasingly popular methods for the operationalization of various types of discourse analysis. However, their application usually remains fairly simple and superficial, and fails to exploit the resources which the digital era holds for discourse analysis to their full extent. This paper discusses the discourse-analytic potential of a more complex and advanced text analysis tool, which is already frequently employed in other approaches to textual analysis, notably topic modelling. We argue that topic modelling promises advances in areas where discourse analysis has traditionally struggled, such as scaling, repetition, and systematization, which go beyond the contributions of simpler frequency and collocation counts. At the same time, it does not violate the epistemological premises and methodological ethos of even the more radical theories of discourse, we will demonstrate. Finally, we present two small case studies to show how topic modellingwhen used with appropriate parameterscan straightforwardly enhance our ability to systematically investigate and interpret discourses in large collections of text.

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Mixing politics and crime – The prevalence and decline of political discourse on the cryptomarket

Cited by 68 publications

References 26 publications

User names of illegal drug vendors on a darknet cryptomarket

User names of illegal drug vendors on a darknet cryptomarket

New psychoactive substances (NPS) on cryptomarket fora: An exploratory study of characteristics of forum activity between NPS buyers and vendors

Topic models meet discourse analysis: a quantitative tool for a qualitative approach

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