Algorithmic Health Content Exposure, Cognitive Load, And Advertising Skepticism: A Conceptual Model Proposal On Digital Health Information Fatigue
Öz
This study examines the psychosocial effects of intense exposure to algorithmically curated health content on digital platforms, conceptualized as "digital health information fatigue." It posits that algorithmic exposure, cognitive burden, and heightened skepticism generate a self-reinforcing cycle, resulting in persistent fatigue within health communication. Through a critical synthesis of interdisciplinary literature, the analysis underscores that algorithmic personalization of health information, intertwined with commercial content, strains individuals' cognitive capacities and fosters general distrust toward persuasive actors. This process leads to three principal psychosocial consequences: chronic health anxiety, erosion of trust in resources and institutions, and functional passivity in health behaviors. Digital health information fatigue is treated not as an individual shortcoming, but as a contextual phenomenon structured by platform logics and algorithmic incentives, distinguishing it from related concepts such as health information avoidance, information overload, and news fatigue. Furthermore, by extending the Persuasion Knowledge Model to algorithmically mediated digital health content, the article proposes an integrated conceptual model for contemporary persuasion dynamics in health communication.
Anahtar Kelimeler
digital health communication, cognitive load, advertising skepticism, health information fatigue
Destekleyen Kurum
There is no supporting organization.
Etik Beyan
This study does not require an ethics committee statement.
Kaynakça
- Bauman, Z. (2000). Liquid modernity. Polity Press.
- Bawden, D., & Robinson, L. (2009). The dark side of information: Overload, anxiety, and other paradoxes and pathologies. Journal of Information Science, 35(2), 180–191. https://doi.org/10.1177/0165551508095781
- Boerman, S.C., Willemsen, LM, & Van Der Aa, E.P. (2017). "This post is sponsored": Effects of sponsorship disclosure on persuasion knowledge and electronic word of mouth in the context of Facebook. Journal of Interactive Marketing, 38, 82–92. https://doi.org/10.1016/j.intmar.2016.12.002
- Bucher, T. (2018). If... then: Algorithmic power and politics. Oxford University Press.
- Eppler, M. J., & Mengis, J. (2004). The concept of information overload: A review of literature from organization science, accounting, marketing, MIS, and related disciplines. The Information Society, 20(5), 325–344. https://doi.org/10.1080/01972240490507974
- Eysenbach, G. (2008). Infodemiology and infoveillance: Framework for an emerging set of public health informatics methods to analyze search, communication, and publication behavior on the internet. Journal of Medical Internet Research, 10(1), e11. https://doi.org/10.2196/jmir.1157
- Friestad, M., & Wright, P. (1994). The persuasion knowledge model: How people cope with persuasion attempts. Journal of Consumer Research, 21(1), 1–31. https://doi.org/10.1086/209380
- Han, B.-C. (2015). The burnout society. Stanford University Press.
- Jabareen, Y. (2009). Building a conceptual framework: Philosophy, definitions, and procedure. International Journal of Qualitative Methods, 8(4), 49–62. https://doi.org/10.1177/160940690900800406
- Moorhead, S.A., Hazlett, DE, Harrison, L., Carroll, J.K., Irwin, A., & Hoving, C. (2013). A new dimension of health care: Systematic review of the uses, benefits, and limitations of social media for health communication. Journal of Medical Internet Research, 15(4), e85. https://doi.org/10.2196/jmir.1933