Year: 2021
Type: article
Abstract: Vaping-related posts ( n = 2,516,664 posts in 2018, n = 2,731,399 posts in 2019, total n = 5,248,063) were obtained from January 1, 2018, to December 31, 2019, using Twitter’s Streaming Application Program Interface (API). Data cleaning procedures included removal of retweets so that each observation could be treated as an independent unit of observation, and removal of posts from bot accounts. Posts containing health-related references to e-cigarettes were identified using two dictionaries: (a) the Unified Medical Language System® (UMLS) Consumer Health Vocabulary (CHV), comprising n =13,479 informal, common medical terms used by consumers and health care professionals; and (b) a list of n =177 additional informal terms corresponding to the CHV terms, wherever relevant (e.g., informal term of “inebriation” is “drunk”). Keywords from the two dictionaries encompassed health diseases and symptoms (referred to as “health effects” in this study). A total sample of n = 667,140 ( n = 257,620 posts in 2018; n = 409,520 posts in 2019) e-cigarette-related posts with mentions of health effects were identified. Each post in the analytic sample was then classified to one or more of the 14 categories of health effects (see Table 1 ). Percentage distribution of categories of health effects by 2018 and 2019 is reported in Figure 1 . In 2018, Neurological effects ( n = 44,345; 17.2%) were the most common, followed by Mental Health ( n = 31,227; 12.1%) and Death ( n = 27,040; 10.5%). In 2019, Death was most commonly referenced ( n = 77,719; 19.0%), followed by references to Neurological ( n = 67,504; 16.5%) and Respiratory ( n = 48,875; 11.9%) health effects in 2019. Compared to 2018, Death , Mental health , and Respiratory health effects were more prevalent in 2019. Respiratory health effects emerged as part of the top five categories in 2019. Percentage distribution of categories of health effects during 2018 and 2019.
Institutions: University of Southern California, American Cancer Society
Language: English
Subfield: Physiology (medical)
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,480
OpenAlex ID: W3142978418