Luca Giommoni

Drug cryptomarkets are a significant development in the recent history of illicit drug markets. Dealers and buyers can now finalize transactions with people they have never met, who could be located anywhere across the globe. What factors shape the geography of international drug trafficking via these cryptomarkets? In our current study, we test the determinants of drug trafficking through cryptomarkets by using a mix of social network analysis and a new dataset composed of self-reported transactions. Our findings contribute to existing research by demonstrating that a country’s level of technological advancement increases the probability of forming trafficking connections on cryptomarkets. Additionally, we found that a country’s capacity to police cryptomarkets reduces the number of trafficking connections with other countries. We also observed that trafficking on cryptomarkets is more likely to occur between countries that are geographically close. In summary, our study highlights the need to consider both online and offline factors in research on cryptomarkets.

Hateful individuals and groups have increasingly been using the Internet to express their ideas, spread their beliefs and recruit new members. Understanding the network characteristics of these hateful groups could help understand individuals’ exposure to hate and derive intervention strategies to mitigate the dangers of such networks by disrupting communications. This article analyses two hateful followers’ networks and three hateful retweet networks of Twitter users who post content subsequently classified by human annotators as containing hateful content. Our analysis shows similar connectivity characteristics between the hateful followers networks and likewise between the hateful retweet networks. The study shows that the hateful networks exhibit higher connectivity characteristics when compared to other “risky” networks, which can be seen as a risk in terms of the likelihood of exposure to, and propagation of, online hate. Three network performance metrics are used to quantify the hateful content exposure and contagion: giant component (GC) size, density and average shortest path. In order to efficiently identify nodes whose removal reduced the flow of hate in a network, we propose a range of structured node-removal strategies and test their effectiveness. Results show that removing users with a high degree is most effective in reducing the hateful followers network connectivity (GC, size and density), and therefore reducing the risk of exposure to cyberhate and stemming its propagation.

This study identifies the presence of human trafficking indicators in a UK-based sample of sex workers who advertise their services online. To this end, we developed a crawling and scraping software that enabled the collection of information from 17, 362 advertisements for female sex workers posted on the largest dedicated platform for sex work services in the UK. We then established a set of 10 indicators of human trafficking and a transparent and replicable methodology through which to detect their presence in our sample. Most of the advertisements (58.3%) contained only one indicator, while 3,694 of the advertisements (21.3%) presented 2 indicators of human trafficking. Only 1.7% of the advertisements reported three or more indicators, while there were no advertisements that featured more than four. 3, 255 advertisements (19.0%) did not contain any indicators of human trafficking. Based on this analysis, we propose that this approach constitutes an effective screening process for quickly identifying suspicious cases, which can then be examined by more comprehensive and accurate tools to identify if human trafficking is occurring. We conclude by calling for more empirical research into human trafficking indicators.

There is a relative dearth of literature on both the effects of cross-border interdictions and the impact of different types of interventions on international drug trafficking. This study identifies the main trafficking routes for cocaine and heroin, along with comparing the disruptive effects induced by targeted and non-coordinated interventions. It adopts a social network approach to identify the routes along which cocaine and heroin are trafficked, and then simulates the impact of different interdiction strategies on these two trafficking networks. The findings indicate that targeting countries based on their respective positions in the networks, as opposed to on the basis of the quantity of drugs exchanged, is more likely to disrupt drug flows. More specifically, concentrating law enforcement resources on countries with several incoming or outgoing trafficking connections, or those countries that mediate between producer, transit and consumer countries, would appear to be particularly effective in this regard. Interventions focused on specific trafficking routes are also likely to be effective if these routes have high edge betweenness centrality scores. This study contributes to extant understanding on the vulnerability of cocaine and heroin international trafficking networks, and, moreover, demonstrates that empirically-driven strategies are potentially more effective at interdicting international trafficking than non-strategic and non-coordinated interventions.

This study analyses large-scale online data to examine the characteristics of a national commercial sex network of off-street female sex workers and their male clients to identify implications for public health policy and practice. We collected sexual contact information from the largest online community dedicated to reviewing sex workers’ services in the UK. We built the sexual network using reviews reported between January 2014 and December 2017. We then quantified network parameters using social network analysis measures. The network is composed of 6477 vertices with 59% of them concentred in a giant component clustered around London and Milton Keynes. We found minimal disassortative mixing by degree between sex workers and their clients, and that a few clients and sex workers are highly connected whilst the majority only have one or few sexual contacts. Finally, our simulation models suggested that prevention strategies targeting both sex workers and clients with high centrality scores are the most effective in reducing network connectedness and average closeness centrality scores, thus limiting the transmission of STIs.