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Sharing of Verified Information about COVID-19 on Social Network Sites: A Social Exchange Theory Perspective.

Jiabei XiaTailai WuLiqin Zhou
Published in: International journal of environmental research and public health (2021)
Background: Verified and authentic information about coronavirus disease (COVID-19) on social networking sites (SNS) could help people make appropriate decisions to protect themselves. However, little is known about what factors influence people's sharing of verified information about COVID-19. Thus, the purpose of this study was to explore the factors that influence people's sharing of verified information about COVID-19 on social networking sites. Methods: Based on social exchange theory, we explore the factors that influence sharing of verified information about COVID-19 from two perspectives: benefits and costs. We employed the survey method to validate our hypothesized relationships. By using our developed measurement instruments, we collected 347 valid responses from SNS users and utilized the partial least squares method to analyze the data. Results: Among the benefits of sharing verified information about COVID-19, enjoyment in helping (β = 0.357, p = 0.000), altruism (β = 0.133, p = 0.029) and reputation (β = 0.202, p = 0.000) were significantly associated with verified information sharing about COVID-19. Regarding the costs of sharing verified information about COVID-19, both verification cost (β = -0.078, p = 0.046) and executional cost (β = -0.126, p = 0.011) also significantly affect verified information sharing about COVID-19. All the proposed hypotheses were supported. Conclusions: By exploring factors from both benefits and costs perspectives, we could understand users' intention to share verified information about COVID-19 comprehensively. This study not only contributes to the literature on information sharing, but also has implications concerning users' behaviors on SNS.
Keyphrases
  • coronavirus disease
  • health information
  • sars cov
  • social media
  • healthcare
  • respiratory syndrome coronavirus
  • mental health
  • cross sectional
  • deep learning