Affordable Access

deepdyve-link
Publisher Website

Towards Automating Location-Specific Opioid Toxicosurveillance from Twitter via Data Science Methods.

Authors
  • Sarker, Abeed1
  • Gonzalez-Hernandez, Graciela1
  • Perrone, Jeanmarie2
  • 1 Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, U.S.A.
  • 2 Department of Emergency Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, U.S.A.
Type
Published Article
Journal
Studies in health technology and informatics
Publication Date
Aug 21, 2019
Volume
264
Pages
333–337
Identifiers
DOI: 10.3233/SHTI190238
PMID: 31437940
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Social media may serve as an important platform for the monitoring of population-level opioid abuse in near real-time. Our objectives for this study were to (i) manually characterize a sample of opioid-mentioning Twitter posts, (ii) compare the rates of abuse/misuse related posts between prescription and illicit opiods, and (iii) to implement and evaluate the performances ofsupervised machine learning algorithms for the characterization of opioid-related chatter, which can potentially automate social media based monitoring in the future.. We annotated a total of 9006 tweets into four categories, trained several machine learning algorithms and compared their performances. Deep convolutional neural networks marginally outperformed support vector machines and random forests, with an accuracy of 70.4%. Lack of context in tweets and data imbalance resulted in misclassification of many tweets to the majority class. The automatic classification experiments produced promising results, although there is room for improvement.

Report this publication

Statistics

Seen <100 times