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Scientists at Leiden University have developed a method to predict which sellers in illegal online markets will succeed. The results of the study, published in the journal Scientific Reports, open up new opportunities for combating crime on the darknet.
On the dark side of the Internet, there is a thriving trade in drugs, weapons, stolen personal data, and child abuse materials. These sites, known as cryptomarkets, are inaccessible through regular browsers and search engines. Users remain anonymous, and transactions are conducted using cryptocurrencies like bitcoin.
Law enforcement agencies have to analyze huge amounts of data to identify criminals.
PhD student Hanyo Burkhout, together with Professors Frank Teix and Arjan Blokland, analyzed data from Evolution — one of the most popular darknet markets in 2014. We studied not only the section where direct trading takes place, but also the forums where users and sellers communicate. By analyzing their communication patterns, the researchers were able to identify the most influential participants.
It turns out that the success of a salesperson can be predicted by two factors: engagement in discussions and the role of an intermediary. Engagement is measured by the number of responses to topics created by the user on the forum. The "degree of mediation" indicates how often the user acts as a link between other network participants.
"Engagement in discussions was a particularly strong predictor. Users whose forum topics generated a lot of feedback often became successful sellers," explains Burkhout. The degree of mediation helped identify important players who were less active on the forum. "Some sellers may not write messages as often, but when they do, they connect different parts of the criminal community," the scientist adds.
This method will help law enforcement agencies prioritize their investigations. "These markets are so big that the police have to choose what to focus on," Blokland says. "Our method can help identify promising salespeople before they become truly influential figures."
Burkhout emphasizes that their research was conducted using historical data and did not violate the privacy of current users of darknet markets.
• Source: https://www.nature.com/articles/s41598-024-67115-5
On the dark side of the Internet, there is a thriving trade in drugs, weapons, stolen personal data, and child abuse materials. These sites, known as cryptomarkets, are inaccessible through regular browsers and search engines. Users remain anonymous, and transactions are conducted using cryptocurrencies like bitcoin.
Law enforcement agencies have to analyze huge amounts of data to identify criminals.
PhD student Hanyo Burkhout, together with Professors Frank Teix and Arjan Blokland, analyzed data from Evolution — one of the most popular darknet markets in 2014. We studied not only the section where direct trading takes place, but also the forums where users and sellers communicate. By analyzing their communication patterns, the researchers were able to identify the most influential participants.
It turns out that the success of a salesperson can be predicted by two factors: engagement in discussions and the role of an intermediary. Engagement is measured by the number of responses to topics created by the user on the forum. The "degree of mediation" indicates how often the user acts as a link between other network participants.
"Engagement in discussions was a particularly strong predictor. Users whose forum topics generated a lot of feedback often became successful sellers," explains Burkhout. The degree of mediation helped identify important players who were less active on the forum. "Some sellers may not write messages as often, but when they do, they connect different parts of the criminal community," the scientist adds.
This method will help law enforcement agencies prioritize their investigations. "These markets are so big that the police have to choose what to focus on," Blokland says. "Our method can help identify promising salespeople before they become truly influential figures."
Burkhout emphasizes that their research was conducted using historical data and did not violate the privacy of current users of darknet markets.
• Source: https://www.nature.com/articles/s41598-024-67115-5
