#Coronavirus on TikTok: user engagement with misinformation as a potential threat to public health behavior
UMB Dataset

UID: 225

Author(s): Jonathan D. Baghdadi*, K.C. Coffey, Rachael Belcher, James Frisbie, Naeemul Hassan, Danielle Sim, Rena D. Malik * Corresponding Author

Description: A sample of TikTok videos associated with the hashtag #coronavirus were downloaded on September 20, 2020. Misinformation was evaluated on a scale (low, medium, high) using a codebook developed by experts in infectious diseases. Multivariable modeling was used to evaluate factors associated with number of views and presence of user comments indicating intention to change behavior. Videos and related metadata were downloaded using a third-party TikTok Scraper using the search term #coronavirus. Videos were reviewed for content and data were entered on a spreadsheet.
Timeframe: 2017 -

Subject Domain

Keywords

Access via Dryad
TikTok video content analysis
Access Restrictions: Unrestricted access
Access Instructions: The data that support the findings are available from GitHub.
Associated Publications: Baghdadi JD, Coffey KC, Belcher R, Frisbie J, Hassan N, Sim D, Malik RD. #Coronavirus on TikTok: user engagement with misinformation as a potential threat to public health behavior. JAMIA Open. 2023 Feb 23;6(1)
Data Type: Quantitative
Software Used: SAS 9.4
advanced analytics, multivariate analyses, business intelligence, data management, and predictive analytics
Study Type: Observational
Dataset Format(s): XLSX
Dataset Size: 789.7 KB