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

10 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
Country/TerritoryUnited 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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