We caught up with Martin Dittus, a data scientist working with Mark Graham and Joss Wright on the OII’s darknet mapping project, to find out some basics about darknet markets, and why they’re interesting to study.

Firstly: what actually is the darknet?

Martin: The darknet is simply a part of the Internet you access using anonymising technology, so you can visit websites without being easily observed. This allows you to provide (or access) services online that can’t be tracked easily by your ISP or law enforcement. There are actually many ways in which you can visit the darknet, and it’s not technically hard. The most popular anonymising technology is probably Tor. The Tor browser functions just like Chrome, Internet Explorer or Firefox: it’s a piece of software you install on your machine to then open websites. It might be a bit of a challenge to know which websites you can then visit (you won’t find them on Google), but there are darknet search engines, and community platforms that talk about it.

The term ‘darknet’ is perhaps a little bit misleading, in that a lot of these activities are not as hidden as you might think: it’s inconvenient to access, and it’s anonymising, but it’s not completely hidden from the public eye. Once you’re using Tor, you can see any information displayed on darknet websites, just like you would on the regular internet. It is also important to state that this anonymisation technology is entirely legal. I would personally even argue that such tools are important for democratic societies: in a time where technology allows pervasive surveillance by your government, ISP, or employer, it is important to have digital spaces where people can communicate freely.

And is this also true for the marketplaces you study on the darknet?

Martin: Definitely not! Darknet marketplaces are typically set up to engage in the trading of illicit products and services, and as a result are considered criminal in most jurisdictions. These market platforms use darknet technology to provide a layer of anonymity for the participating vendors and buyers, on websites ranging from smaller single-vendor sites to large trading platforms. In our research, we are interested in the larger marketplaces, these are comparable to Amazon or eBay — platforms which allow many individuals to offer and access a variety of products and services.

The first darknet market platform to acquire some prominence and public reporting was the Silk Road — between 2011 and 2013, it attracted hundreds of millions of dollars worth of bitcoin-based transactions, before being shut down by the FBI. Since then, many new markets have been launched, shut down, and replaced by others… Despite the size of such markets, relatively little is known about the economic geographies of the illegal economic activities they host. This is what we are investigating at the Oxford Internet Institute.

And what do you mean by “economic geography”?

Martin: Economic geography tries to understand why certain economic activity happens in some places, but not others. In our case, we might ask where heroin dealers on darknet markets are geographically located, or where in the world illicit weapon dealers tend to offer their goods. We think this is an interesting question to ask for two reasons. First, because it connects to a wide range of societal concerns, including drug policy and public health. Observing these markets allows us to establish an evidence base to better understand a range of societal concerns, for example by tracing the global distribution of certain emergent practices. Second, it falls within our larger research interest of internet geography, where we try to understand the ways in which the internet is a localised medium, and not just a global one as is commonly assumed.

So how do you go about studying something that’s hidden?

Martin: While the strong anonymity on darknet markets makes it difficult to collect data about the geography of actual consumption, there is a large amount of data available about the offered goods and services themselves. These marketplaces are highly structured — just like Amazon there’s a catalogue of products, every product has a title, a price, and a vendor who you can contact if you have questions. Additionally, public customer reviews allow us to infer trading volumes for each product. All these things are made visible, because these markets seek to attract customers. This allows us to observe large-scale trading activity involving hundreds of thousands of products and services.

Almost paradoxically, these “hidden” dark markets allow us to make visible something that happens at a societal level that otherwise could be very hard to research. By comparison, studying the distribution of illicit street drugs would involve the painstaking investigative work of speaking to individuals and slowly trying to acquire the knowledge of what is on offer and what kind of trading activity takes place; on the darknet it’s all right there. There are of course caveats: for example, many markets allow hidden listings, which means we don’t know if we’re looking at all the activity. Also, some markets are more secretive than others. Our research is limited to platforms that are relatively open to the public.

Finally: will you be sharing some of the data you’re collecting?

Martin: This is definitely our intention! We have been scraping the largest marketplaces, and are now building a reusable dataset with geographic information at the country level. Initially, this will be used to support some of our own studies. We are currently mapping, visualizing, and analysing the data, building a fairly comprehensive picture of darknet market trades. It is also important for us to state that we’re not collecting detailed consumption profiles of participating individuals (not that we could). We are independent academic researchers, and work neither with law enforcement, nor with platform providers.

Primarily, we are interested in the activity as a large-scale global phenomenon, and for this purpose, it is sufficient to look at trading data in the aggregate. We’re interested in scenarios that might allow us to observe and think about particular societal concerns, and then measure the practices around those concerns in ways that are quite unusual, that otherwise would be very challenging. Ultimately, we would like to find ways of opening up the data to other researchers, and to the wider public. There are a number of practical questions attached to this, and the specific details are yet to be decided — so stay tuned!

Martin Dittus is a researcher and data scientist at the Oxford Internet Institute, where he studies the economic geography of darknet marketplaces. More: @dekstop

Follow the project here: https://www.oii.ox.ac.uk/research/projects/economic-geog-darknet/

Twitter: @OiiDarknet

Further reading (academic):

• Dolliver et al. (2016), “A Geographic Analysis of Drug Trafficking patterns on the Tor Network”. Geographical Review DOI: 10.1111/gere.12241
• Buskirk et al. (2016), “Who Sells What? Country Specific Differences in Substance Availability on the Agora Cryptomarket”. International Journal of Drug Policy 35: 16-23.
• Broséus et al. (2017), “A Geographical Analysis of Trafficking on a Popular Darknet Market”. Forensic Science International 277:88-102.

Further reading (popular):

• Wired: Dark net drug markets kept alive by great customer service
• The Guardian: Dark web marketplaces AlphaBay and Hansa shut down

Martin Dittus was talking to OII Managing Editor David Sutcliffe.

Homejoy was slated to become the Uber of domestic cleaning services. It was a platform that allowed customers to summon a cleaner as easily as they could hail a ride. Regular cleanups were just as easy to schedule. Ratings from previous clients attested to the skill and trustworthiness of each cleaner. There was no need to go through a cleaning services agency, or scour local classifieds to find a cleaner directly: the platform made it easy for both customers and people working as cleaners to find each other. Homejoy made its money by taking a cut out of each transaction. Given how incredibly successful Uber and Airbnb had been in applying the same model to their industries, Homejoy was widely expected to become the next big success story. It was to be the next step in the inexorable uberization of every industry in the economy.

On 17 July 2015, Homejoy announced that it was shutting down. Usage had grown slower than expected, revenues remained poor, technical glitches hurt operations, and the company was being hit with lawsuits on contractor misclassification. Investors’ money and patience had finally ran out. Journalists wrote interesting analyses of Homejoy’s demise (Forbes, TechCrunch, Backchannel). The root causes of any major business failure (or indeed success) are complex and hard to pinpoint. However, one of the possible explanations identified in these stories stands out, because it corresponds strongly with what theory on platforms and markets could have predicted. Homejoy wasn’t growing and making money because clients and cleaners were taking their relationships off-platform: after making the initial contact through Homejoy, they would simply exchange contact details and arrange further cleanups directly, taking the platform and its revenue share out of the loop. According to Forbes, only 15-20 percent of customers came back to Homejoy within a month to arrange another cleanup.

According to the theory of platforms in economics and management studies literature, platforms solve coordination problems. Digital service platforms like Uber and Airbnb solve, in particular, the problem of finding another party to transact with. Through marketing and bootstrapping efforts they ensure that both buyers and sellers sign up to the platform, and then provide match-making mechanisms to bring them together. They also provide solutions towards the problem of opportunism, that is, how to avoid being cheated by the other party. Rating systems are their main tool in this.

Platforms must compete against the existing institutional arrangements in their chosen industry. Uber has been very successful in taking away business from government-licensed taxicabs. Airbnb has captured market share from hotels and hotel booking sites. Both have also generated lots of new business: transactions that previously didn’t happen at all. It’s not that people didn’t already occassionally pay a highschool friend to give them a ride home from a party, or rent a room for the weekend from a friend of a friend who lives in New York. It’s that platforms make similar things possible even when the highschool friend is not available, or if you simply don’t know anyone with a flat in New York. Platforms coordinate people to turn what is otherwise a thin market into a thick one. Not only do platforms help you to find a stranger to transact with, but they also help you to trust that stranger.

Now consider the market for home cleaning services. Home cleaning differs from on-demand transport and short-term accommodation in one crucial way: the service is typically repeated. Through repeated interactions, the buyer and the seller develop trust in each other. They also develop knowledge capital specific to that particular relationship. The buyer might invest time into communicating their preferences and little details about their home to the seller, while the seller will gradually become more efficient at cleaning that particular home. They have little need for the platform to discipline each individual cleanup; relationships are thus soon taken off-platform. Instead of an all-encompassing Uber-style platform, all that may be needed is a classifieds site or a conventional agency that provides the initial introduction and references. Contrast this with on-demand transport and short-term accommodation, where each transaction is unique and thus each time the counterparty is a stranger — and as such a potential cheat or deadbeat. Here the platform continues to provide security after the parties have been introduced.

The case of Homejoy and the economic theory on platforms thus suggest that there are fundamental limits to the uberization of the economy. Digital service platforms can be very successful at mediating one-off transactions, but they are much less useful in industries where the exact same service is repeated many times, and where buyers and sellers develop assets specific to the relationship. Such industries are more likely to continue to be shaped by hierarchies and networks of personal relationships.

There are probably other dimensions that are also pivotal in predicting whether an industry is susceptible to uberization. Geographical span is one: there are efficiencies to be had from particular cleaners specializing in particular neighbourhoods. Yet, at the same time, online labour platforms like Upwork cater to buyers and sellers of software development (and other digitally mediated contract work) across national boundaries. I will discuss this dimension in detail in a future blog post.

Vili Lehdonvirta is a Research Fellow at the OII. He is an economic sociologist who studies the design and socioeconomic implications of digital marketplaces and platforms, using conventional social research methods as well as novel data science approaches. Read Vili’s other Policy & Internet Blog posts on Uber and Airbnb:

Uber and Airbnb make the rules now – but to whose benefit?

Why are citizens migrating to Uber and Airbnb, and what should governments do about it?

Should we love Uber and Airbnb or protest against them?