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Drug dealers and dodgy pharmacies illegally touting opioids online – think heroin, fentanyl, codeine, morphine, and so on – may have their collars felt by an AI cop soon. Ish. Maybe.
The US Department of Health and Human Services (HHS) has awarded a contract worth $224,864 (£172,000) to S-3 Research, a startup spun out of the University of California, San Diego, to build machine-learning software capable of sniffing out opioid peddlers on social media.
Timothy - Mackey - CEO - S-3 - Research
Timothy Mackey, CEO of S-3 Research and an assistant professor at the university, told The Register on Tuesday the upstart's software roams platforms such as Instagram, Twitter, Reddit, Tumblr, and YouTube looking for opioid ads – such as specific keywords, or hashtags of mispelled drugs.
Once these posts are detected, the text is analysed by AI algorithms to determine whether the poster intends to sell illicit substances. “We use natural language processing models to look for words like 'buy,' 'sell,' 'discount,' or if there is a phone number or hyperlink included," said Mackey. "The order of the syntax of those words hints that the content has been posted to try and sell opioids.”
Off - Mackey - Wealth - Data - Classifiers
To pull this off, Mackey has amassed a wealth of data to train various classifiers and neural network models through his research over the past few years. For S-3 Research’s software to be successful, it has to recognize common patterns in illicit drug ads on the web. The software also has to cope with changing tactics and slang used by dealers.
In an effort to evade detection by human investigators, dodgy opioid adverts are typically left as comments lower down on a webpage, rather than directly and blatantly in YouTube videos or Instagram snaps, which makes them easier to process with code. If deals are discussed in private messages on Facebook, though, for example, the machine-learning model, which...
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