@inProceedings{
 title = {A computational approach to automatic prediction of drunk-texting},
 type = {inProceedings},
 year = {2015},
 identifiers = {[object Object]},
 volume = {2},
 id = {6ab5beb0-3445-3149-8cce-b4288325d386},
 created = {2017-01-19T22:52:57.000Z},
 file_attached = {false},
 profile_id = {da451fa3-3eab-3814-8b13-756bc24c281d},
 last_modified = {2017-03-17T13:17:53.002Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {? 2015 Association for Computational Linguistics.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.},
 bibtype = {inProceedings},
 author = {Joshi, A. and Mishra, A. and Balamurali, A.R. and Bhattacharyya, P. and Carman, M.J.},
 booktitle = {ACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference}
}

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