  Alcohol abuse may lead to unsociable behavior such as crime, drunk driving,
or privacy leaks. We introduce automatic drunk-texting prediction as the task
of identifying whether a text was written when under the influence of alcohol.
We experiment with tweets labeled using hashtags as distant supervision. Our
classifiers use a set of N-gram and stylistic features to detect drunk tweets.
Our observations present the first quantitative evidence that text contains
signals that can be exploited to detect drunk-texting.
