TY - GEN
T1 - Leveraging Twitter Data to Explore the Feasibility of Detecting Negative Health Outcomes Related to Vaping
AU - Kasson, Erin
AU - Cao, Lijuan
AU - Huang, Ming
AU - Wu, Dezhi
AU - Cavazos-Rehg, Patricia A.
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Adverse health outcomes
KW - GENSIM
KW - LDA
KW - Latent dirichlet allocation
KW - Social media
KW - Surveillance
KW - Twitter
KW - Vaping
UR - http://www.scopus.com/inward/record.url?scp=85097212862&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-60703-6_60
DO - 10.1007/978-3-030-60703-6_60
M3 - Conference contribution
AN - SCOPUS:85097212862
SN - 9783030607029
T3 - Communications in Computer and Information Science
SP - 464
EP - 468
BT - HCI International 2020 – Late Breaking Posters - 22nd International Conference, HCII 2020, Proceedings
A2 - Stephanidis, Constantine
A2 - Antona, Margherita
A2 - Ntoa, Stavroula
PB - Springer Science and Business Media Deutschland GmbH
T2 - 22nd International Conference on Human-Computer Interaction, HCI International 2020
Y2 - 19 July 2020 through 24 July 2020
ER -