AI system reveals hidden drug dark jargon unknown even to police officers

Drug trafficking and other criminal activities on social media have become a growing social concern. To evade detection by law enforcement and automated monitoring systems, offenders use dark jargons, often combining multiple common words to form “dark jargon”—covert dark jargons that are difficult to identify. Once authorities recognize these dark jargons, offenders quickly switch to new ones, making it a constant race to keep up with the evolving words.

A team of researchers from the University of Electro-Communications (UEC), including Takuro Hada, Yuichi Sei, Yasuyuki Tahara, and Akihiko Ohsuga, has developed a groundbreaking method to tackle this challenge. Their new system leverages the power of artificial intelligence to detect these elusive, compound-type dark jargons with greater accuracy than ever before. This innovative approach focuses on the relationships between words within posts to identify terms that are often used together as part of a dark jargon.

The work is published in the Journal of Information Processing.

Traditional methods struggle to detect dark jargons when it consists of multiple words because most text analysis tools automatically break down sentences into smaller word units. This process often separates words that should be treated as a single phrase, like “Green-Crack” or “Pineapple-Chunk,” both known as dark jargons for illegal substances.

The new AI system overcomes this by identifying pairs of words that frequently appear together in similar contexts. By analyzing large datasets of social media posts, the AI can recognize when two or more words form dark jargons, even if it’s a newly emerging term.

The impact of this development is significant. In experiments, the system identified more dark jargons than previous methods, with a 7% improvement in accuracy. Notably, during interviews with police officers experienced in organized crime investigations, 93% of the newly detected dark jargons were confirmed as previously unknown. This highlights the potential of the AI to reveal emerging dark jargons that evade current detection efforts.

With crime increasingly moving to online spaces, this technology provides law enforcement with a vital tool to stay ahead of offenders. By automating the detection of new dark jargons, police forces and monitoring systems can respond more swiftly, reducing the risk of illegal activity on social media platforms. This innovation not only strengthens public safety but also supports social media companies in keeping their platforms safer for all users.

This research marks a major step forward in the fight against online crime, offering an adaptable, AI-driven solution that evolves as quickly as the dark jargon it aims to detect. With its high accuracy and ability to detect previously unknown terms, this system has the potential to become a key element of future cybercrime prevention strategies.

In Japan, where “yami baito”—a term referring to illegal part-time jobs often linked to criminal activities—has become a growing social issue, this AI technology is expected to play a critical role. Detecting the dark jargon used in online recruitment for these illicit activities, supports law enforcement and enhances public safety.

Provided by
The University of Electro-Communications