Can machine learning help address the scourge of opioid addiction?
What’s new: A public health researcher developed a neural network that spots sellers of opioids on social media, Recode reported.
How it works: The model built by University of California professor Tim K. Mackey sifts through Instagram profiles to find those that offer drugs.
- In past research, Mackey identified Instagram as a popular platform for dealers. His team collected posts mentioning opioids and manually confirmed 12,857 that offered opioids for sale.
- The researchers used half the data to train a model to identify language that correlated with drug advertisements. They used the other half to validate the trained model.
- The neural net found 1,228 ads posted by 267 unique users. It achieved F1 scores of 95 percent for precision and accuracy, outperforming models based on random forests, decision trees, and support vector machines.
- The U.S. Department of Health and Human Services has contracted with Mackey to expand his method to cover Reddit, Tumblr, and YouTube. He’s also building a commercial platform for law-enforcement agencies to monitor social media streams in real time.
Yes, but: Online opioid sales represent only a small fraction of the total, RAND Corporation drug policy expert Bryce Pardo told Recode. Mackey’s tool spots small-scale dealers, but it can’t do much to bring down the cartels responsible for much of the supply, he said.
Why it matters: Shutting down suppliers could save lives. Two million Americans are addicted to opioids, and 130 people die from overdoses every day, according to the National Institute on Drug Abuse.
We’re thinking: Will dealers resort to adversarial examples to thwart such automatic detection algorithms? Unfortunately, there’s plenty of financial incentive to advertise illegal opioids on social networks.