Leveraging Twitter Data to Explore the Feasibility of Detecting Negative Health Outcomes Related to Vaping

Erin Kasson, Lijuan Cao, Ming Huang, Dezhi Wu, Patricia A. Cavazos-Rehg

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

Abstract

Adverse health outcomes (e.g., respiratory infections, lung injury, death) related to vaping were reported at significantly higher rates in healthcare systems starting in the fall of 2019. This study seeks to leverage artificial intelligence (AI) techniques, such as latent dirichlet allocation (LDA) methods, to determine whether a signal of these negative health outcomes could have been detected by the frequency of Twitter content posted about vaping and these health outcomes prior to this increase. We utilized a random sample of 3,523 tweets from 2019 and performed LDA methods on this sample to cluster the tweets and identify latent topics. We then utilized keywords from within the health-related cluster (topic) to manually verify the frequency of these tweets across previous years to approximate topic trends. LDA methods resulted in 4 distinct topics of tweets, including a health-related topic. Keywords from this topic were found to increase slightly in 2017 and 2018, with a dramatic increase in 2019. Further, the highest performing keyword combination was found to increase most significantly beginning in August 2019. The results of this study support the feasibility of leveraging artificial intelligence techniques for surveillance of public health concerns such as vaping and adverse health outcomes reported in Twitter. Further research is needed into the development of such models, which could promote earlier detection of public health issues and timely outreach to those groups most at risk.

Original languageEnglish (US)
Title of host publicationHCI International 2020 – Late Breaking Posters - 22nd International Conference, HCII 2020, Proceedings
EditorsConstantine Stephanidis, Margherita Antona, Stavroula Ntoa
PublisherSpringer Science and Business Media Deutschland GmbH
Pages464-468
Number of pages5
ISBN (Print)9783030607029
DOIs
StatePublished - 2020
Event22nd International Conference on Human-Computer Interaction, HCI International 2020 - Copenhagen, Denmark
Duration: Jul 19 2020Jul 24 2020

Publication series

NameCommunications in Computer and Information Science
Volume1294
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference22nd International Conference on Human-Computer Interaction, HCI International 2020
Country/TerritoryDenmark
CityCopenhagen
Period7/19/207/24/20

Keywords

  • Adverse health outcomes
  • GENSIM
  • Latent dirichlet allocation
  • LDA
  • Social media
  • Surveillance
  • Twitter
  • Vaping

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

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