Abstract

Digital platforms have revolutionized the way illegal drug trafficking is taking place. Modern drug dealers use social network platforms, such as Instagram and TikTok, as direct-to-consumer marketing tools. But apart from the marketing side, drug dealers also use fintech payment apps to engage in financial transactions with their clients. In this work, we leverage a large dataset from Venmo to investigate the digital money trail of drug dealers and the social networks they create. Using text and social network analytics, we identify two types of illicit users: mixed-activity participants and heavy drug traffickers and build a random forest classifier that accurately predicts both types of illicit nodes. We then investigate the social network structure of drug dealers on Venmo and find that heavy drug traffickers share similar network characteristics with previous literature findings on drug trafficking networks. However, mixed-activity participants exhibit different patterns of network structure characteristics, including a higher clustering coefficient, suggesting that they may be accessing multiple networks and bridging those networks through their illicit activities. Our findings highlight the importance of distinguishing between these two types of illicit users and provide law enforcement agencies with valuable insights that can aid in combating illegal drug transactions in digital payment apps.

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