Deep learning model for classifying drug abuse risk behavior in tweets

Han Hu, Pranavi Moturu, Kannan Neten Dharan, James Geller, Sophie Di Iorio, Hai Phan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Social media such as Twitter can provide urgently needed drug abuse intelligence to support the campaign of fighting against the national drug abuse crisis. We employed a targeted tweet collection approach and a two-staged annotation strategy that combines conventional annotation with crowdsourced annotation to produce annotated training dataset. In this demo, we share deep learning models trained in a boosting manner using the data from the two-staged annotation method and unlabeled data collection to detect drug abuse risk behavior in tweets.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages386-387
Number of pages2
ISBN (Electronic)9781538653777
DOIs
StatePublished - Jul 24 2018
Event6th IEEE International Conference on Healthcare Informatics, ICHI 2018 - New York, United States
Duration: Jun 4 2018Jun 7 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018

Other

Other6th IEEE International Conference on Healthcare Informatics, ICHI 2018
CountryUnited States
CityNew York
Period6/4/186/7/18

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Health Informatics

Keywords

  • Deep Learning
  • Drug Abuse Detection
  • Social Media
  • Twitter

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