Deep self-taught learning for detecting drug abuse risk behavior in tweets

Han Hu, Nhat Hai Phan, James Geller, Huy Vo, Bhole Manasi, Xueqi Huang, Sophie Di Lorio, Thang Dinh, Soon Ae Chun

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

7 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationComputational Data and Social Networks - 7th International Conference, CSoNet 2018, Proceedings
EditorsMy T. Thai, Xuemin Chen, Wei Wayne Li, Arunabha Sen
PublisherSpringer Verlag
Pages330-342
Number of pages13
ISBN (Print)9783030046477
DOIs
StatePublished - Jan 1 2018
Event7th International Conference on Computational Data and Social Networks, CSoNet 2018 - Shanghai, China
Duration: Dec 18 2018Dec 20 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11280 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other7th International Conference on Computational Data and Social Networks, CSoNet 2018
Country/TerritoryChina
CityShanghai
Period12/18/1812/20/18

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • Deep learning
  • Drug abuse
  • Self-taught learning
  • Tweets

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