Toward Data Sense-Making in Digital Health Communication Research: Why Theory Matters in the Age of Big Data

Frontiers in Communication

Andrew Z. H. Yee²  Edmund W. J. Lee⁴ 

[1] Department of Social and Behavioral Sciences, Harvard T. H. Chan School of Public Health, Boston, MA, United States;Humanities, Arts, and Social Sciences, Singapore University of Technology and Design, Singapore, Singapore;Medical Oncology, Dana–Farber Cancer Institute, Boston, MA, United States;Wee Kim Wee School of Communication and Information, College of Humanities, Arts, and Social Sciences, Nanyang Technological University, Singapore, Singapore;

关键词: big data;    artificial intelligence;    machine learning;    digital health;    social media;    wearables;   

DOI  :  10.3389/fcomm.2020.00011

来源: DOAJ

【 摘 要 】

The rapidly increasing volume of health data generated from digital technologies have ushered in an unprecedented opportunity for health research. Despite their promises, big data approaches in understanding human behavior often do not consider conceptual premises that provide meaning to social and behavioral data. In this paper, we update the definition of big data, and review different types and sources of health data that researchers need to grapple with. We highlight three problems in big data approaches—data deluge, data hubris, and data opacity—that are associated with the blind use of computational analysis. Finally, we lay out the importance of cultivating health data sense-making—the ability to integrate theory-led and data-driven approaches to process different types of health data and translating findings into tangible health outcomes—and illustrate how theorizing can matter in the age of big data.

【 授权许可】

Unknown