"Mixing politics and crime - the prevalence and decline of political discourse on the cryptomarket"

"Mixing politics and crime - the prevalence and decline of political discourse on the cryptomarket", Munksgaard & Demant 2016:

Background: Dread Pirate Roberts, founder of the first cryptomarket for illicit drugs named Silk Road, articulated libertarian political motives for his ventures. Previous research argues that there is a significant political component present or involved in cryptomarket drug dealing which is specifically libertarian. The aim of the paper is to investigate the prevalence of political discourses within discussions of cryptomarket drug dealing, and further to research the potential changes of these over the timespan of the study. Methods: We develop a novel operationalization of discourse analytic concepts which we combine with topic modelling enabling us to study how politics are articulated on cryptomarket forums. We apply the Structural Topic Model on a corpus extracted from crawls of cryptomarket forums encompassing posts dating from 2011 to 2015. Results: The topics discussed on cryptomarket forums are primarily centered around the distribution of drugs including discussions of shipping and receiving, product advertisements, and reviews as well as aspects of drug consumption such as testing and consumption. However, on forums whose primary function is aiding operations on a black market, we still observe political matter. We identified one topic which expresses a libertarian discourse that emphasizes the individual's right to non-interference. Over time we observe an increasing prevalence of the libertarian discourse from 2011 to the end of 2013. In the end of 2013 - when Silk Road was seized - we observe an abrupt change in the prevalence of the libertarian discourse. Conclusions: The libertarian political discourse has historically been prevalent on cryptomarket forums. The closure of Silk Road has affected the prevalence of libertarian discourse suggesting that while the closure did not succeed in curtailing the cryptomarket economy, it dampened political sentiments.

...We apply the statistical technique topic modelling as a method to identify political discourses. Topic models excel in the analysis of large amounts of textual data by providing an automated procedure for coding the content of a corpus of texts (including very large corpora) into a set of substantively meaningful coding categories called 'topic' (Mohr and Bogdanov, 2013, p. 546). Described as a statistical model of language (DiMaggio, Nag, and Blei, 2013, p. 577), topic models have been used for a variety of purposes in the context of social sciences (Grimmer and Stewart, 2013, for an extensive discussion of applications), cultural sociology (DiMaggio et al., 2013) and literature history (Jockers, 2013; Jockers and Mimno, 2013). Within the context of the dark web, Spitters, Verbruggen, and Van Staalduinen (2014) used topic modelling to study the thematic structure of the dark web using latent Dirichlet allocation and interlinking between sites. DiMaggio et al. (2013) argue that topic models renders central concepts within cultural sociology operational. Conversely, we agree with these scholars and suggest that topic modelling further makes concepts from Fairclough's (2008) discourse analysis operational on large sets of textual data. The key result produced by the topic model is a number of topics, or themes (Blei, 2012). Within topic modelling a topic is formally defined as a distribution over a vocabulary (DiMaggio et al., 2013, p. 578; Roberts et al. 2014, p. 1067). That is, in one topic, 'cannabis' for example, it is likely that there is a higher probability of the terms 'weed' and 'smoke' occurring than the terms 'capital' and 'investment'. 'Weed', for example, may appear in the topic 'gardening' as well as in the topic 'cannabis', having entirely different contextual meanings. As the topic is a distribution, 'weed' appears with other high-probability terms such as 'garden', 'nature' and 'flower', in the 'gardening' topic, and with other terms in the 'cannabis' topic such as 'smoke', 'bong' and 'high'. The distribution of terms can be considered as a preferential topical vocabulary. Conceptually, we suggest that the topics can be understood as if one was to talk about a topic, and when doing so, one is more likely to use some words than others when the topic is 'gardening' as opposed to 'cannabis'. Thus, the preferential vocabularies of individuals can be interpreted as a measure of their meaning-making. Put more simply, if the probability of 'fun' appearing in the same sentence as 'cannabis' is higher than that of 'abuse', this is the ascribing of meaning to cannabis expressed as probabilities.

...The analysis is based on crawls of cryptomarket forums conducted between October 2013 and March 2015 which contains posts dating back to 2011. 2.6 million forum posts were extracted from five cryptomarket forums: Silk Road, Silk Road 2.0, Evolution Marketplace, Agora Marketplace, and Black Market Reloaded. The omni-forum, a forum where users can discuss multiple marketplaces, The Hub was further included. The five market forums were chosen as they represent some of the largest platforms for discussions of cryptomarkets and have operated for extended periods of time. We included The Hub as it is the only omni- forum located on the dark web. These crawls contain a large proportion of what has been written on cryptomarket forums from 2011 to 2015 as the economy generally has centered on these markets (see Soska and Christin, 2015) and should therefore be considered as representative of the cryptomarket discursive order. March 17th was chosen as a cut-off point being the last date where more than 2 of the studied sites were operational as Evolution Marketplace absconded with user funds. ...The crawls we use were conducted by independent researcher Gwern Branwen, and are part of a larger collection with many contributors, which is publicly available (Branwen et al. 2015). Forum posts and associated metadata (date, user, subforum, market) were extracted from Branwen's crawls using XPath- expressions. When extracting forum posts, we further discard quotations of other posts in these, so as to isolate what the individual poster is writing. Posts in subforums dedicated to non-English languages were discarded.

...Unsupervised topic models require that the researcher specify the number of topics. To find the appropriate number of topics (k) we estimated three models with 100, 150 and 200 topics. As suggested by the literature (Grimmer and Stewart, 2013, p. 270; Roberts, Stewart, and Airoldi, 2015, p. 19), these were then subsequently evaluated qualitatively by their ability to produce coherent topics and capture topics discussed in the literature on cryptomarkets (e.g. harm-reduction, libertarian politics) and obvious topics such as smuggling, dealing, and discussion of substances. We settled on the model with 100 topics but found that the models reflected the same thematic structure differing only in granularity or level of detail. The Structural Topic Model, hereafter STM, developed by Roberts et al. (2015) was applied to the corpus using LDA initialization. After processing, our dataset consisted of a vocabulary of 16,133 terms represented in 417,491 documents, each containing at least 10 unique terms from the vocabulary. The key innovation of the STM is its ability to incorporate metadata and we therefore allow topical prevalence to vary by forum (e.g. Agora Marketplace, Silk Road) and date of posting. The date on which a post is submitted is allowed to have a non-linear relationship in the topic estimation by using a B-spline with 10 degrees of freedom.

...Because the interest is in broader shifts in the prevalence of discourse we estimate a regression wherein we fit libertarian discourse to the date on which a post was written using a B-spline, as recommended by Roberts et al. (2015), with 10 degrees of freedom to the estimated document-topic proportions provided by the STM calculating uncertainty using the "Global" option (see Roberts et al., 2015, for further details). The results are shown in Figure 4 wherein we observe a trend towards a higher prevalence of libertarian discourse from 2011 to the end of 2013. In the end of 2013, when Silk Road was seized, we observe an abrupt downward change in the prevalence of libertarian discourse.

...We observe that the prevalence of the libertarian discourse was increasing until the seizure of Silk Road after which is decreased. Maddox et al. (2016) found that respondents after the fall of Silk Road experienced a sense of loss for the political vision of a different future (Maddox et al., 2016, p. 122). Empirically, we see this loss expressed in a declining prevalence of libertarian discourse indicating that after the fall of Silk Road, participants stopped discussing libertarian politics. Though some expressed a desire to recreate what was lost with Silk Road (Maddox et al., 2016, p. 123), our findings suggest that the libertarian discourse on cryptomarket has never been as prevalent as before the fall of Silk Road. After the fall of Silk Road, new markets emerged to take its place (Van Buskirk, Roxburgh, Farrell, and Burns, 2014) along with Silk Road 2.0 and a new Dread Pirate Roberts. However, in spite of this, the libertarian discourse did not reemerge. The territorial and structural qualities of the cryptomarket, whether demimonde or informal governing node, did not change as new cryptomarkets operated similarly. Polletta (1999) argues that long-standing community institutions, which Silk Road can be considered as being, and networks are crucial to the generation of cultural challenges within free spaces. With the fall of Silk Road, the change consisted in the longest-standing community institution being seized, and the unmasking of a charismatic leader, one who had articulated drug dealing as a means to liberation. While interventions against cryptomarkets have not stopped the growth of the economy (Soska and Christin 2015; Van Buskirk et al. 2014), the closure of Silk Road seems to have put an end to the expressions of libertarian politics by cryptomarket participants.


Comments


[9 Points] sapiophile:

Yes, uh, hi... I uh, I came for the state-smashing? There was some talk of smashing the state? Yes, thank you, if you could direct me appropriately I'd be very grateful.


[6 Points] bobbiggs69:

Great article Gwern. I've noticed myself that since SR, the markets have become less of a movement and more of a way to make money or buy drugs. Even here on Reddit, you could look at all the old postings from /r/silkroad and compare them with /r/dnm and as time goes on you'll see less and less libertarian discussion here compared with SR. In the beginning, we talked about the movement and it was more personable. Back on the SR forums, we felt like sort of a family. They tried to continue on SR2 but it never gained traction. Now, on here if you post something that's not a review or not specifically related to the market, people freak out.

Great research.


[3 Points] Pelican_Vendor:

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[1 Points] bobbiggs69:

The Maddox references, is that the guy from http://maddox.xmission.com ? ;)