Enabling real-Time drug abuse detection in tweets

Nhathai Phan, Manasi Bhole, Soon Ae Chun, James Geller

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

32 Scopus citations


Prescription drug abuse is one of the fastest growing public health problems in the USA. To address this epidemic, a near real-Time monitoring strategy, instead of one resorting to a retrospective health records, may improve detecting the prevalence and patterns of abuse of both illegal drugs and prescription medications. In this paper, our primary goals are to demonstrate the possibility of utilizing social media, e.g., Twitter, for automatic monitoring of illegal drug and prescription medication abuse. We use machine learning methods for an automatic classification that can identify tweets that are indicative of drug abuse. We collected tweets associated with well-known illegal and prescription drugs. We manually annotated 300 tweets that are likely to be related to drug abuse. Our experiment compares a set of classification algorithms, and a decision tree classifier J48, and the SVM outperform others for determining whether tweets contain signals of drug abuse. This automatic supervised classification study results illustrate the utility of Twitter in examining patterns of abuse, and show the feasibility of building the drug abuse detection system that can process large volume data from social media sources in a near real-Time.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781509065431
StatePublished - May 16 2017
Event33rd IEEE International Conference on Data Engineering, ICDE 2017 - San Diego, United States
Duration: Apr 19 2017Apr 22 2017

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627


Other33rd IEEE International Conference on Data Engineering, ICDE 2017
Country/TerritoryUnited States
CitySan Diego

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Information Systems


  • Drug abuse
  • Illegal drug
  • Online social network
  • Prescription drug
  • Social media


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