# A Computational Approach to Automatic Prediction of Drunk Texting

arXiv: https://arxiv.org/abs/1610.00879 · HTML: https://mlsanity.com/paper/1610.00879/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2016.

## Built on by (most cited first, 1 of 1)

- [In Vino Veritas and Vulnerabilities: Examining LLM Safety via Drunk Language Inducement](https://mlsanity.com/paper/2601.22169/) (2026)

## Abstract (arXiv)

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.