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The Effects of Receiving and Expressing Health Information on Social Media during the COVID-19 Infodemic: An Online Survey among Malaysians
Social media is used by the masses not only to seek health information but also to express feelings on an array of issues, including those related to health. However, there has been little investigation on the influence of expressing and receiving information in terms of health-related engagement on...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9265326/ https://www.ncbi.nlm.nih.gov/pubmed/35805646 http://dx.doi.org/10.3390/ijerph19137991 |
Sumario: | Social media is used by the masses not only to seek health information but also to express feelings on an array of issues, including those related to health. However, there has been little investigation on the influence of expressing and receiving information in terms of health-related engagement on social media. Moreover, little is known of the cognitive mechanisms underlying the expression and reception of health information on information overload (IO) during an infectious disease outbreak. Guided by the Cognitive Mediation Model (CMM), this study proposes a conceptual model to understand the effects of receiving and expressing COVID-19 information on social media on IO. Using an online survey conducted in Malaysia, our results indicated that risk perception positively predicted the reception and expression of information which, in turn, was positively associated with perceived stress and IO. Additionally, perceived stress triggered IO, indicating that the greater the perceived stress from health information engagement, the higher the likelihood of one experiencing IO during the pandemic. We conclude that the CMM can be extended to study IO as an outcome variable. More studies in diverse health contexts need to be conducted to enhance the conceptualization and operationalization of IO in health information processing. |
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