A computational approach to automatic prediction of drunk-texting

Aditya Joshi, Abhijit Mishra, A. R. Balamurali, Pushpak Bhattacharyya, Mark James Carman

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

    4 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Title of host publication53rd Annual Meeting of the Association for Computational Linguistics and 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing (ACL-IJCNLP 2015)
    Subtitle of host publicationBeijing, China, 26 - 31 July 2015, Short Papers [Proceedings]
    EditorsChengqing Zong, Michael Strube
    Place of PublicationStroudsburg, Pennsylvania
    PublisherAssociation for Computational Linguistics (ACL)
    Pages604-608
    Number of pages5
    ISBN (Print)9781510808461
    Publication statusPublished - 2015
    EventAnnual Meeting of the Association of Computational Linguistics and International Joint Conference on Natural Language Processing 2015 - Beijing, China
    Duration: 26 Jul 201531 Jul 2015
    Conference number: 53rd
    https://www.aclweb.org/anthology/events/acl-2015/ (Proceedings)

    Conference

    ConferenceAnnual Meeting of the Association of Computational Linguistics and International Joint Conference on Natural Language Processing 2015
    Abbreviated titleACL-IJCNLP 2015
    CountryChina
    CityBeijing
    Period26/07/1531/07/15
    OtherACL has held jointly with International Joint Conference on Natural Language Processing, Proceedings of System Demonstrations
    Internet address

    Cite this