A Computational Approach to Automatic Prediction of Drunk Texting
This addresses privacy and safety concerns for social media users and platforms by detecting potentially harmful drunk messages.
The researchers tackled the problem of automatically identifying text messages written under alcohol influence by developing classifiers using N-gram and stylistic features on distantly supervised tweet data, providing the first quantitative evidence that text contains detectable drunk-texting signals.
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.