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.
@article{arxiv.1610.00879,
title = {A Computational Approach to Automatic Prediction of Drunk Texting},
author = {Aditya Joshi and Abhijit Mishra and Balamurali AR and Pushpak Bhattacharyya and Mark Carman},
journal= {arXiv preprint arXiv:1610.00879},
year = {2016}
}