Date of Award
May 2021
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Parks, Recreation and Tourism Management
Committee Member
Ryan J Gagnon
Committee Member
Iryna Sharaievska
Committee Member
Barry Garst
Committee Member
Gwynn Powell
Abstract
COVID-19 is currently at the forefront of both out-of-school time program providers’ and parents’ minds, with additional policies and procedures added existing operating standards to protect the health of participants, staff, and parents (Environmental Health & Engineering, 2020). A failure to adequately prepare and react to different parenting styles may have both operational and financial implications for out-of-school time programs. These implications are only further exacerbated in the additional context of a global pandemic. While the COVID-19 vaccine is a hope to many that the end of the pandemic is near, parental vaccine hesitancy or refusal may pose a significant hurdle to the safe operation of out-of-school time programs. By exploring the topics of vaccine hesitancy, children, and parents in an online environment, this study offers a closer look into a digital leisure space.
In order to better explore the conversations and commentaries occurring on social media about parents, children, vaccines, and COVID-19, web-scraping technologies were employed to aid in a more robust data collection. Due to the nature of web-scraped data as large in size and unruly, a machine learning method was used to analyze the data: Latent Dirichlet Allocation (i.e., LDA), a specific form of topic modelling. After establishing model parameters for the LDA, 25 latent topics were identified from the cleaned dataset (N = 31,925). These 25 topics were subsequently sorted into seven categories: Government, Feelings, School, Public Health, Christmas, Risk & Safety, and Parents & Families. Interpretation of the 25 latent topics was aided by a visualization of the top words most relevant to individual topics, in context to the overall dataset. Representative tweets from each category further identified the range of conversations and commentaries occurring on social media about parents, children, vaccines, and COVID-19. Challenges with research at the cusp of innovation for leisure sciences, as well as implications of practice for out-of-school-time professionals, are also discussed.
Recommended Citation
Thurson, Kathleen, "Parenting, Vaccines, and COVID-19: A Machine-Learning Approach" (2021). All Theses. 3528.
https://open.clemson.edu/all_theses/3528