Content and trend analysis of user-generated nicotine sickness tweets: A retrospective infoveillance study.

Autor: Purushothaman V; Global Health Policy and Data Institute, San Diego, United States.; Division of Infectious Diseases and Global Public Health, School of Medicine, University of California, San Diego, San Diego, United States., McMann TJ; Global Health Policy and Data Institute, San Diego, United States.; Global Health Program, Department of Anthropology, University of California, San Diego, San Diego, United States., Li Z; Global Health Policy and Data Institute, San Diego, United States.; Global Health Program, Department of Anthropology, University of California, San Diego, San Diego, United States.; S-3 Research, San Diego, United States., Cuomo RE; Global Health Policy and Data Institute, San Diego, United States.; Division of Infectious Diseases and Global Public Health, School of Medicine, University of California, San Diego, San Diego, United States., Mackey TK; Global Health Policy and Data Institute, San Diego, United States.; Global Health Program, Department of Anthropology, University of California, San Diego, San Diego, United States.; S-3 Research, San Diego, United States.
Jazyk: angličtina
Zdroj: Tobacco induced diseases [Tob Induc Dis] 2022 Mar 15; Vol. 20, pp. 30. Date of Electronic Publication: 2022 Mar 15 (Print Publication: 2022).
DOI: 10.18332/tid/145941
Abstrakt: Introduction: Exposure to pro-tobacco and electronic nicotine delivery system (ENDS) social media content can lead to overconsumption, increasing the likelihood of nicotine poisoning. This study aims to examine trends and characteristics of nicotine sickness content on Twitter between 2018-2020.
Methods: Tweets were collected retrospectively from the Twitter Academic Research Application Programming Interface (API) stream filtered for keywords: 'nic sick', 'nicsick', 'vape sick', 'vapesick' between 2018-2020. Collected tweets were manually annotated to identify suspected user-generated reports of nicotine sickness and related themes using an inductive coding approach. The Augmented Dickey-Fuller (ADF) test was used to assess stationarity in the monthly variation of the volume of tweets between 2018-2020.
Results: A total of 5651 tweets contained nicotine sickness-related keywords and 18.29% (n=1034) tweets reported one or more suspected nicotine sickness symptoms of varied severity. These tweets were also grouped into five related categories including firsthand and secondhand reports of symptoms, intentional overconsumption of nicotine products, users expressing intention to quit after 'nic sick' symptoms, mention of nicotine product type/brand name that they consumed while 'nic sick', and users discussing symptoms associated with nicotine withdrawal following cessation attempts. The volume of tweets reporting suspected nicotine sickness appeared to increase throughout the study period, except between February and April 2020. Stationarity in the volume of 'nicsick' tweets between 2018-2020 was not statistically significant (ADF= -0.32, p=0.98) indicating a change in the volume of tweets.
Conclusions: Results point to the need for alternative forms of adverse event surveillance and reporting, to appropriately capture the growing health burden of vaping. Infoveillance approaches on social media platforms can help to assess the volume and characteristics of user-generated content discussing suspected nicotine poisoning, which may not be reported to poison control centers. Increasing volume of user-reported nicotine sickness and intentional overconsumption of nicotine in twitter posts represent a concerning trend associated with ENDS-related adverse events and poisoning.
Competing Interests: The authors have each completed and submitted an ICMJE form for disclosure of potential conflicts of interest. The authors declare that they have no competing interests, financial or otherwise, related to the current work. All the authors report that since the initial planning of the work awards were received from Tobacco-Related Disease Research Program (#T29IP0465 and #T29IP0384). T.J. McMann, Z. Li, T.K. Mackey also report that in the past 36 months they were employees of a startup company funded by National Institutes of Health (S-3 Research).
(© 2022 Purushothaman V. et al.)
Databáze: MEDLINE
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