This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.
Opioid use disorder presents a public health issue afflicting millions across the globe. There is a pressing need to understand the opioid supply chain to gain new insights into the mitigation of opioid use and effectively combat the opioid crisis. The role of anonymous online marketplaces and forums that resemble eBay or Amazon, where anyone can post, browse, and purchase opioid commodities, has become increasingly important in opioid trading. Therefore, a greater understanding of anonymous markets and forums may enable public health officials and other stakeholders to comprehend the scope of the crisis. However, to the best of our knowledge, no large-scale study, which may cross multiple anonymous marketplaces and is cross-sectional, has been conducted to profile the opioid supply chain and unveil characteristics of opioid suppliers, commodities, and transactions.
We aimed to profile the opioid supply chain in anonymous markets and forums via a large-scale, longitudinal measurement study on anonymous market listings and posts. Toward this, we propose a series of techniques to collect data; identify opioid jargon terms used in the anonymous marketplaces and forums; and profile the opioid commodities, suppliers, and transactions.
We first conducted a whole-site crawl of anonymous online marketplaces and forums to solicit data. We then developed a suite of opioid domain–specific text mining techniques (eg, opioid jargon detection and opioid trading information retrieval) to recognize information relevant to opioid trading activities (eg, commodities, price, shipping information, and suppliers). Subsequently, we conducted a comprehensive, large-scale, longitudinal study to demystify opioid trading activities in anonymous markets and forums.
A total of 248,359 listings from 10 anonymous online marketplaces and 1,138,961 traces (ie, threads of posts) from 6 underground forums were collected. Among them, we identified 28,106 opioid product listings and 13,508 opioid-related promotional and review forum traces from 5147 unique opioid suppliers’ IDs and 2778 unique opioid buyers’ IDs. Our study characterized opioid suppliers (eg, activeness and cross-market activities), commodities (eg, popular items and their evolution), and transactions (eg, origins and shipping destination) in anonymous marketplaces and forums, which enabled a greater understanding of the underground trading activities involved in international opioid supply and demand.
The results provide insight into opioid trading in the anonymous markets and forums and may prove an effective mitigation data point for illuminating the opioid supply chain.
Overdoses from opioids, a class of drugs that includes both prescription pain relievers and illegal narcotics, account for more deaths in the United States than traffic deaths or suicides. Overdose deaths involving heroin began increasing in 2000 with a dramatic change in pace, and as of 2014, 61% of drug overdoses involved some type of opioid, inclusive of heroin [
The past 10 years have witnessed a spree of anonymous online marketplaces and forums, mostly catering to drugs in anonymous ways and resembling eBay or Amazon. For instance, SilkRoad, the first modern darknet market and best known as a platform for selling illegal drugs, was launched in February 2011 and subsequently shut down in October 2013 [
Anonymous online marketplaces are usually platforms for sellers and buyers to conduct transactions in a virtual environment. They usually come with anonymous forums for sellers and buyers to share information, promote their products, leave feedback, and share experiences about purchases. To understand how it works, we describe an opioid transaction’s operational steps on the anonymous online marketplaces and forums. We present a view about how such services operate and how different entities interact with each other (
Overview of the opioid trading in the anonymous marketplaces and forums.
First, an opioid trader, who intends to list the selling information and find potential customers, will first access the anonymous online marketplaces and forums, using an anonymous browsing tool such as a Tor client or a web-to-Tor proxy (step 1 in
Suppose that an opioid buyer wants to purchase opioids. The opioid buyer (client) will also access the anonymous online market and create an account in each anonymous marketplace before they can find the listings of opioids (step 4). After perusing the items available on the anonymous online market (step 5), the buyer will add opioids to their shopping cart (step 6). When the buyer wants to check out and make a purchase using cryptocurrency (eg, Bitcoin), if the trader accepts payment through an anonymous online marketplace as an escrow, the buyer will place the listed amount of cryptocurrency in escrow (step 7). Then, the trader receives the order and escrow confirmation (step 8). Otherwise, the buyer will pay the trader directly using cryptocurrency or any other payment method accepted by the trader (step 9) [
Example of opioid listings in The Versus Project.
Example of opioid listings in Alphabay.
Recent years have witnessed the trend of studying opioid use disorders using anonymous marketplaces and forums data [
This paper seeks to complement current studies widening the understanding of opioid supply chains in underground marketplaces using comprehensive, large-scale, longitudinal anonymous marketplace and forum data. To this end, we propose a series of techniques to collect data; identify opioid jargon terms used in the anonymous marketplaces and forums; and profile the opioid commodities, suppliers, and transactions. Specifically, we first conducted a whole-site crawl of anonymous online marketplaces and forums to solicit data. We then developed a suite of opioid domain–specific text mining techniques (eg, opioid jargon detection and opioid trading information retrieval) to recognize information relevant to opioid trading activities (eg, commodities, price, shipping information, and suppliers). Subsequently, we conducted a comprehensive, large-scale, longitudinal study to demystify opioid trading activities in anonymous markets and forums.
The contributions of this study are elaborated below. First, we designed and implemented an anonymous marketplace data collection and analysis pipeline to gather and identify opioids data in 16 anonymous marketplaces and forums over a period of almost 9 years between 2011 and 2020. Second, we fine-tuned the semantic comparison model proposed by Yuan et al [
This section elaborates on the methodology used to identify opioid trading information in the anonymous market and forums. We illustrate the methodology pipeline (
Overview of the methodology workflow. SCM: semantic comparison model, POS: part-of-speech.
Our research collected product listings and forum posts from 10 anonymous online market places and 6 forums. Our study determined the underground marketplace and forum list based on darknet site search engines and previous research works [
To collect the listing information of 5 anonymous online marketplaces (ie, Apollon, Avaris, Darkbay, Empire, and The Versus Project), we conducted a whole-site crawl. The crawler was implemented in Python and used the Selenium module to launch browsers and to send crawling requests [
In total, we collected 248,359 listings of 10 anonymous online marketplaces between December 2013 and March 2020. For forum corpora, we gathered 1,138,961 traces (spanning from June 2011 to July 2015) from the underground forums The Hub, Silk Road, Black Market, Evolution, Hydra, and Pandora.
Data set summary of marketplaces and forums that were collected for this study.
Name | Type | Lifetime | Measurement dates | Number of traces/listings | Number of opioid traces/listings |
Agora | Marketplace | December 2013 to August 2015 | January 2014 to July 2015 | 140,266 | 12,051 |
Alphabay | Marketplace | December 2014 to July 2017 | December 2014 to July 2015 | 21,679 | 1344 |
Hydra | Marketplace | March 2014 to November 2014 | August 2014 to October 2014 | 3048 | 218 |
Pandora | Marketplace | October 2013 to November 2014 | December 2013 to November 2014 | 20,013 | 1749 |
Evolution | Marketplace | January 2014 to March 2015 | April 2014 to March 2015 | 54,196 | 4954 |
Apollon | Marketplace | May 2018 to March 2020 | September 2018 to February 2020 | 2921 | 2552 |
Empire | Marketplace | February 2018 to August 2020 | April 2018 to March 2020 | 2995 | 2548 |
The Versus Project | Marketplace | November 2019 to now | November 2019 to March 2020 | 233 | 202 |
Avaris | Marketplace | October 2019 to August 2020 | October 2019 to February 2020 | 291 | 286 |
Darkbay | Marketplace | July 2019 to September 2020 | July 2019 to February 2020 | 2717 | 2112 |
Black Market | Forum | December 2013 to February 2014 | December 2013 to February 2014 | 52,127 | 669 |
Pandora | Forum | October 2013 to September 2014 | January 2014 to September 2014 | 18,640 | 798 |
Hydra | Forum | March 2014 to November 2014 | April 2014 to September 2014 | 887 | 41 |
The Hub | Forum | January 2014 to now | January 2014 to July 2015 | 53,973 | 1082 |
Evolution | Forum | January 2014 to March 2015 | January 2014 to November 2014 | 166,641 | 2682 |
Silk Road | Forum | January 2011 to November 2014 | June 2011 to November 2013 | 846,693 | 34,519 |
Our study used opioid keywords and jargons to recognize listings and forum traces related to underground opioid trading activities. Our opioid jargon identification procedure implemented a modified semantic comparison model [
Our modification of the semantic comparison model will generate comparable word embeddings for opioid jargon words in legitimate documents (ie, benign corpora embedding) and in underground corpora (ie, underground corpora embedding). Specifically, our modification used a series of opioid keywords collected to generate their benign corpora embeddings and then searched for words whose underground corpora embeddings were close to the opioid keywords’ benign corpora embeddings. We output the top 100 proper nouns in the underground corpora in our implementation, whose embeddings showed the closest cosine distance to the known opioid keywords.
We trained the semantic comparison model using the traces of Reddit as the benign corpora and the traces of the anonymous marketplaces/forums as the underground corpora. The parameters of the model were set as default [
Opioid jargons used in the anonymous online marketplaces and forums.
Category | Jargons |
Heroin | gunpowder, pearl tar (black pearl tar), speedball, heroin #4, diacetylmorphin, and h3 brown sugar |
Fentanyl | chyna (china white), acetylfentanyl (acetyl fentanyl), phenaridine, and duragesic |
Buprenorphine | subutex and suboxone |
Oxycodone | roxy, roxi, roxies, roxys, oxynorm, A215, K8/K9, M15/30, blueberries, A15, OC30/80, OP80, oxyneo, M523/IP204/C230, bananas, V4812, and CDN 80 |
Dihydrocodeine | DHCa |
Oxymorphone | panda and o bomb |
Morphine | zomorph, mscontin (ms contin), skenan, oramorph, and kadian |
Methadone | amidone, methadose, and chocolate chip cookies |
Hydromorphone | hydromorph |
Hydrocodone | lortab, norcos, zohydro, IP109/110, and M367 |
Tramadol | UDTb 200 |
Codeine | thiocodin and lean |
Others | tapentadol, tapalee, and nucynta |
aDihydrocodeine bitartrate.
bUltram hydrochloride tramadol.
Our goal here was to identify anonymous forum posts with the topics of opioid commodity promotion (eg, listing promotion) and review (eg, report fake opioid vendors). We then analyzed these forum posts to profile underground opioid trading behaviors.
To identify forum posts related to opioid commodity promotion and review, our methodology was designed to filter forum posts with opioid keywords and then use a classifier to the posts with the topics of interest. The classifier was built upon transfer learning and a crafted objective function that heavily weighs the penalty of misclassifying a positive instance.
The model training process for opioid promotion and review posts’ detection consists of 3 stages: model initialization, transfer learning, and model refining. First, 2 neural network models with 3 hidden layers are trained on the data sets (
where
Data sets used in the forum post modeling.
Topic | Positive samples (n) | Negative samples (n) | |||
|
Model initialization data set | Annotated anonymous market/forum data set | Model initialization data set | Annotated anonymous market/forum data set | |
Promotion | Listing descriptions in the marketplace Agora and Alphabay (60,000) | Listing descriptions and product promotions in the anonymous marketplace (1000) | Amazon review data set [ |
Nonpromotion (ie, review and question answering) posts in anonymous markets and forums (1000) | |
Review | Amazon review data set [ |
Review posts in an anonymous marketplace (1000) | Yahoo! Answers data sets [ |
Nonreview posts in anonymous markets and forums (1000) |
Finally, in the model refining stage, the model is trained for other 2 iterations using the same objective function. We manually investigated the results by randomly sampling 10% of data records during each iteration and adding false positive samples into the unlabeled set. Our model was evaluated via 10-fold cross-validation. The review detection model yielded a mean precision of 81.5% and an average recall of 80.1%, whereas for the promotion detection model, it yielded a mean precision of 88.1% and an average recall of 85.1% (
The results and 95% CIs of forum posts’ topic modeling.
Topic modeling methods | Promotion topic | Review topic | ||||
|
Precision | Recall | Precision | Recall | ||
|
||||||
|
NaiveBayes | 81 (2) | 80 (3) | 64 (2) | 87 (3) | |
|
C45 | 66 (7) | 68 (11) | 56 (6) | 75 (9) | |
|
Decision tree | 83 (3) | 51 (3) | 71 (2) | 61 (4) | |
|
||||||
|
Unsupervised topic modeling | 814 (48) | 814 (81.40) | 939 (53.69) | 939 (93.90) | |
Our model, mean (SD) | 88 (1) | 85 (2) | 82 (1) | 80 (1) | ||
Baseline, mean (SD) | 84 (1) | 84 (3) | 76 (3) | 74 (2) |
aMALLET: Machine Learning for Language Toolkit.
We compared our method with the state-of-the-art topic modeling method Machine Learning for Language Toolkit (MALLET) [
In this way, we collected 7100 promotion posts and 6408 review posts from forum posts in total.
For each marketplace listing and forum posts related to opioid promotion, we extracted 8 properties: vendor name, product, price, number of products sold, advertised origins, acceptable shipping destinations, and whether escrow or not. For the forum posts on the topic of the opioid commodity review, we recognized the sentiment of the review. Below, we elaborate on the methodology used to identify each of the properties:
Vendor name: To identify the vendor name, we designed a parser to identify the authors of the listings and promotional posts by applying platform-specific heuristics, which we manually derived from each marketplace and forum’s HTML templates.
Product: We recognized the type of opioid in each listing’s description content using the opioid keyword data set generated in the previous step.
Price: We used a price extraction model [
Number of products sold: Listings of 5 marketplaces (Alphabay, The Versus Project, Apollon, Empire, and Darkbay) consist of the number of items that have been sold (as shown in
Advertised origins and acceptable shipping destinations: We parsed the advertised origins and acceptable shipping destinations from the HTML template of marketplace listings and used the country name dictionary to find the country names from a forum post. We considered the contextual information based on the keywords
Whether escrow: In the marketplaces Alphabay, Apollon, and Empire, the product listing usually has a field to indicate whether the escrow is supported. Hence, we designed a parser to obtain this information. In forum posts, we used the keyword
Review sentiment: To investigate the sentiment of the opioid product reviews, we applied the chi-square score–based sentiment analysis model to classify the product review into positive and negative [
To evaluate the aforementioned methods for extracting properties, we randomly chose 1000 listings for each property and manually annotated the properties as ground truth. We evaluated our method on our annotated data set, which yields an accuracy of over 90% for each property extraction, as shown in
The results of calculating accuracy of opioid information retrieval.
Property | Number of ground truth, n | Accuracy, n (%) |
Vendor name | 1000 | 1000 (100) |
Product | 1000 | 954 (95.40) |
Price | 1000 | 1000 (100) |
Number of products sold | 1000 | 1000 (100) |
Advertised origins | 1000 | 1000 (100) |
Acceptable shipping destinations | 1000 | 1000 (100) |
Whether escrow | 1000 | 1000 (100) |
Review sentiment | 1000 | 926 (92.60) |
In total, we collected 248,359 listings from 10 anonymous online marketplaces and 1,138,961 traces (ie, threads of posts) from 6 underground forums. Among them, we identified 28,106 opioid product listings and 13,508 opioid-related promotional and review forum traces from 5147 unique opioid suppliers’ IDs and 2778 unique opioid buyers’ IDs. As observed in our data set, the top 3 marketplaces with the most opioid listings are Agora, Evolution, and Apollon.
In our study, we found that 23.78% (9896/41,614) listings and traces were identified with the help of 58 opioid jargons (
Note that we should not overestimate the number of suppliers and buyers given the number of IDs found in this research, but we regarded it as the upper-bounded number of the opioid suppliers and buyers. This is because the same user could have different IDs, and the same ID in different marketplaces can point to different users. Owing to the anonymity of the underground marketplaces and forums, there exists no ground truth to link users with their IDs.
We list the top 5 opioids with most listings and their average prices in 2014, 2015, 2019, and 2020 (
Popular opioids according to different years. Note that data for 2020 only included data from January to March; price per gram is in US $.
2014 | 2015 | 2019 | 2020 | |||||||||
Name | Number of listings | Price (per gram), mean (SD) | Name | Number of listings | Price (per gram), mean (SD) | Name | Number of listings | Price (per gram), mean (SD) | Name | Number of listings | Price (per gram), mean (SD) | |
Heroin | 4251 | 129.5 (99.9) | Heroin | 2408 | 185.6 (138.4) | Heroin | 1697 | 73.0 (49.8) | Heroin | 611 | 67.0 (37.8) | |
Oxycodone | 3086 | 660.3 (445.3) | Oxycodone | 2079 | 1239.1 (843.0) | Oxycodone | 1078 | 520.8 (444.9) | Oxycodone | 356 | 590.6 (450.4) | |
Fentanyl | 1397 | 1116.4 (647.5) | Fentanyl | 1450 | 1546 (909.4) | Codeine | 418 | 80.3 (61.1) | Fentanyl | 149 | 154.2 (123.2) | |
Buprenorphine | 934 | 2764.9 (2007.8) | Buprenorphine | 571 | 4243.7 (3006.1) | Tramadol | 331 | 16.1 (11.7) | Buprenorphine | 90 | 2083.7 (1471.7) | |
Tramadol | 839 | 21.5 (14.4) | Tramadol | 555 | 29.8 (29.1) | Fentanyl | 282 | 247.8 (172.1) | Hydrocodone | 89 | 1183.1 (374.0) |
When evaluating the activeness of the underground opioid listing, we measured the monthly
Number of monthly newly-appeared and disappeared listings in the marketplaces Agora and Evolution.
We observed the same listings posted in different marketplaces and illustrate the dependency of the same opioid listings among different marketplaces. Note that we determined if 2 listings are identical by matching the same elements (ie, listing’s title and description information and the vendor’s name) in 2 listings. We observed that the marketplaces of Agora and Evolution shared 530 opioid listings from 290 unique supplier IDs. The opioid commodity with most listings across different marketplaces was
To understand the scale of opioid suppliers on the anonymous online market, we scanned the listings of 10 marketplaces and the promotional posts of 6 forums to extract the account information from 5147 unique opioid suppliers. By the time Agora was shut down in August 2015, 916 opioid suppliers were found, with an average number of listings of 13 per supplier. We observed that the opioid suppliers with most listings is
To better understand the potential bundling relationship of opioid suppliers across different marketplaces, we calculated the Jaccard similarity coefficient between the suppliers in different marketplaces (
Co-occurrence of the same opioid suppliers across different anonymous marketplaces.
Inspired by the work [
In addition, 204 suppliers were reported as scammers in the anonymous forums of the Evolution, SilkRoad, Pandora, and The Hub. It is not surprising to find that the top 3 marketplaces and forums that found the most scam reports were Evolution, Silk Road, and Pandora, as the scam reports mostly come from the associated forums of the marketplaces [
When inspecting the advertised origins and the acceptable shipping destinations on the opioid listings from 7 marketplaces (Apollon, Avaris, Alphabay, Hydra, Pandora, Empire, and Versus), we observed that most of the opioid commodities were shipped from the United States, followed by the United Kingdom, Germany, Netherlands, and Canada (
The advertised origin countries.
Name of country | Percentage of origin countries in opioid listings, n (%) |
United States | 3520 (35.3) |
United Kingdom | 1648 (17.1) |
Germany | 1459 (14.7) |
Netherlands | 897 (9) |
Canada | 622 (6.2) |
France | 479 (4.8) |
Australia | 398 (4) |
India | 200 (2) |
Spain | 160 (1.6) |
Sweden | 80 (0.8) |
Japan | 73 (0.7) |
Italy | 60 (0.6) |
Singapore | 55 (0.6) |
Belgium | 51 (0.5) |
Switzerland | 50 (0.5) |
Portugal | 22 (0.2) |
Afghanistan | 18 (0.2) |
Denmark | 17 (0.2) |
Czech Republic | 14 (0.1) |
China | 11 (0.1) |
Top 5 advertised origin countries according to different years. Note that data for 2020 only included data from January to March.
2014 | 2015 | 2019 | 2020 | ||||
Country | Number of appearance in listings | Country | Number of appearance in listings | Country | Number of appearance in listings | Country | Number of appearance in listings |
United States | 778 | United States | 603 | United States | 1394 | United States | 680 |
Germany | 646 | France | 188 | United Kingdom | 1269 | Netherlands | 250 |
Netherlands | 145 | Canada | 151 | Germany | 537 | United Kingdom | 185 |
United Kingdom | 142 | Australia | 112 | Netherlands | 451 | Germany | 176 |
Canada | 131 | United Kingdom | 96 | Canada | 287 | Australia | 75 |
Considering the shipping destination, we observed that the majority of opioid commodities were shipped worldwide 36.37% (5654/15,546), followed by shipping to the United States only 19.35% (3008/15,546), Europe only 10.52% (1635/15,546), and the United Kingdom only 5.46% (849/15,546).
To understand customer satisfaction, we conducted a sentiment analysis on 624 review posts related to 190 opioid suppliers from 4 marketplaces: Agora, Alphabay, Pandora, and Evolution. We observed that 145 opioid suppliers had 378 positive reviews, whereas 102 opioid suppliers had at least one negative review. For instance, the opioid supplier from the SilkRoad with the user ID
As observed in our data set, the opioid suppliers in the marketplaces Evolution, Pandora, and Silk Road accepted escrow as a method of payment. However, most of the suppliers only used escrow for small orders. This shows the weak platform trust of opioid suppliers. In fact, the shutdown of the marketplace Evolution was discovered to be an exit scam, with the site’s operators shutting down abruptly to steal the approximately US $12 million in bitcoins that it was holding as an escrow [
Our study identified 41,000 opioid trade–related marketplace listings and forum posts by analyzing more than 1 million listings and posts in multiple anonymous marketplaces and forums, which is the largest underground opioid trading data set ever reported. We found evidence through extensive analyses of the anonymous online market of pervasive supply, which fuels the international opioid epidemic. Nontraditional methods, as presented here by studying the online supply chain, present a novel approach for governmental and other large-scale solutions. When interpreted by professionals, our initial results demonstrate useful findings and may be used downstream by law enforcement and public policy makers for impactful structural interventions to the opioid crisis. Although a large body of current research is focused on pathways for treatment of opioid use disorder and analyzing deaths per treatment capacity of substance use providers, these research areas are limited to the demand side of the opioid epidemic [
We acknowledge some limitations of our study. For example, there might be varying types of heroin or fentanyl, but we could not subcategorize them due to the lack of precise ontology. Addressing this challenge requires deep domain knowledge and expertise, which is constantly evolving. Another limitation is pointed out in the paper that multiple online suppliers might belong to the same vendor. This problem might be addressed by studying the product overlapping patterns over time to merge suppliers, which might reveal more interesting hierarchical clustering patterns. Another important source of information is the trading cash flow, which is recorded in the block chain and might contribute to a comprehensive view of the supply-demand relationship. We did not include such analyses due to the time and scope constraints, and it is a topic that we plan to investigate further.
In our study, a total of 248,359 listings from 10 anonymous online marketplaces and 1,138,961 traces (ie, threads of posts) from 6 underground forums were collected. Among them, we identified 28,106 opioid product listings and 13,508 opioid-related promotional and review forum traces from 5147 unique opioid suppliers’ IDs and 2778 unique opioid buyers’ IDs. Our study characterized opioid suppliers (eg, activeness and cross-market activities), commodities (eg, popular items and their evolution), and transactions (eg, origins and shipping destination) in anonymous marketplaces and forums, which enabled a greater understanding of the underground trading activities involved in international opioid supply and demand.
To the best of our knowledge, a comprehensive overview of the opioid supply chain in the anonymous online marketplaces and forums, as well as a measurement study of trading activities, is still an open research challenge. This is the first study to measure and characterize opioid trading in anonymous online marketplaces and forums. From our measurement, we concluded that anonymous online marketplaces and forums provided easy-access platforms for global opioid supply. These findings characterizing mass opioid suppliers, commodities, and transactions on anonymous marketplaces and forums can enable law enforcement, policy makers, and invested health care stakeholders to better understand the scope of opioid trading activities and provide insight into this new type of opioid supply chain.
Application Programming Interface
Machine Learning for Language Toolkit
None declared.