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Deep Self-Taught Learning for Detecting Drug Abuse Risk Behavior in Tweets

  • Han Hu
  • NhatHai PhanEmail author
  • James Geller
  • Huy Vo
  • Bhole Manasi
  • Xueqi Huang
  • Sophie Di Lorio
  • Thang Dinh
  • Soon Ae Chun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11280)

Abstract

Drug abuse continues to accelerate toward becoming the most severe public health problem in the United States. The ability to detect drug abuse risk behavior at a population scale, such as among the population of Twitter users, can help us to monitor the trend of drug-abuse incidents. Unfortunately, traditional methods do not effectively detect drug abuse risk behavior, given tweets. This is because: (1) Tweets usually are noisy and sparse; and (2) The availability of labeled data is limited. To address these challenging problems, we proposed a deep self-taught learning system to detect and monitor drug abuse risk behaviors in the Twitter sphere, by leveraging a large amount of unlabeled data. Our models automatically augment annotated data: (i) To improve the classification performance, and (ii) To capture the evolving picture of drug abuse on online social media. Our extensive experiment has been conducted on 3 million drug abuse-related tweets with geo-location information. Results show that our approach is highly effective in detecting drug abuse risk behaviors.

Keywords

Deep learning Self-taught learning Drug abuse Tweets 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Han Hu
    • 1
  • NhatHai Phan
    • 1
    Email author
  • James Geller
    • 1
  • Huy Vo
    • 2
  • Bhole Manasi
    • 1
  • Xueqi Huang
    • 2
  • Sophie Di Lorio
    • 1
  • Thang Dinh
    • 3
  • Soon Ae Chun
    • 4
  1. 1.New Jersey Institute of TechnologyNewarkUSA
  2. 2.The City College of New YorkNew YorkUSA
  3. 3.Virginia Commonwealth UniversityRichmondUSA
  4. 4.City University of New YorkStaten IslandUSA

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