Yet a surprising number of us indulge in this peculiar form of indiscretion. And this practice has given Nabil Hossain and pals at the University of Rochester an interesting idea.
Today, these guys show how they’ve trained a machine to spot alcohol-related tweets. And they also show how to use this data to monitor alcohol-related activity and the way it is distributed throughout society. They say the method could have a significant impact on the way we understand and respond to the public health issues that alcohol and other activities raise.
Hossain and co’s work is based on two breakthroughs. The first is a way to train a machine-learning algorithm to spot tweets that relate to alcohol and those sent by people drinking alcohol at the time. The second is a way to find a Twitter user’s home location with much greater accuracy than has ever been possible and therefore to determine whether they are drinking at home or not.
The team began by collecting geotagged tweets sent ...