Use of Health Belief Model-based Deep Learning to Understand Public Health Beliefs in Breast Cancer Screening from Social Media before and during the COVID-19 Pandemic

Michelle Bak, Chieh-Li Chin, Jessie Chin

Research output: Contribution to journalConference articlepeer-review

Abstract

Breast cancer is the second leading cause of cancer death for women in the United States. While breast cancer screening participation is the most effective method for early detection, screening rate has remained low. Given that understanding health perception is critical to understand health decisions, our study utilized the Health Belief Model-based deep learning method to predict and examine public health beliefs in breast cancer and its screening behavior. The results showed that the trends in public health perception are sensitive to political (i.e., changes in health policy), sociological (i.e., representation of disease and its preventive care by public figure or organization), psychological (i.e., social support), and environmental factors (i.e., COVID-19 pandemic). Our study explores the roles social media can play in public health surveillance and in public health promotion of preventive care.

Original languageEnglish (US)
Pages (from-to)280-288
Number of pages9
JournalAMIA Annual Symposium Proceedings
Volume2023
StatePublished - 2023

Keywords

  • Humans
  • Female
  • United States/epidemiology
  • COVID-19/epidemiology
  • Breast Neoplasms/diagnosis
  • Public Health
  • Social Media
  • Pandemics/prevention & control
  • Deep Learning
  • Early Detection of Cancer/psychology
  • Health Belief Model

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