The study pulled in tweets posted between January 2012 and October 2015 in the state of Pennsylvania that referred to opioids. The team used over 200 keywords that represented opioid use, which included everything from randomly generated misspellings of those keywords to mirror errors common in social media use. They manually reviewed 16,000 posts to understand the way keywords were usually used in posts, and grouped 550 posts into four different categories: self-reported abuse or misuse, information sharing, unrelated, and non-English. They then trained machine learning algorithms, one of which was neural network, on those annotated posts. The neural network performed the best, and identified tweets noting opioid abuse at the same level of accuracy as a person would.