Big Data, Computational Social Science, and Health Communication: A Review and Agenda for Advancing Theory

被引:22
|
作者
Rains, Stephen A. [1 ]
机构
[1] Univ Arizona, Dept Commun, Tucson, AZ 85721 USA
关键词
PERCEIVED NORMS; PUBLIC-HEALTH; NETWORK; MEDIA; TWITTER; COMMUNITY; TWEETS; INFORMATION; BEHAVIOR; BREAST;
D O I
10.1080/10410236.2018.1536955
中图分类号
G2 [信息与知识传播];
学科分类号
05 ; 0503 ;
摘要
Contemporary research on health communication has been marked by the presence of big data and computational social science (CSS) techniques. The relative novelty of these approaches makes it worthwhile to consider their status and potential for advancing health communication scholarship. This essay offers an introduction focusing on how big data and CSS techniques are being employed to study health communication and their utility for theory development. Key trends in this body of research are summarized, including the use of big data and CSS for examining public perceptions of health conditions or events, investigating network-related dimensions of health phenomena, and illness monitoring. The implications of big data and CSS for health communication theory are also evaluated. Opportunities presented by big data and CSS to help extend existing theories and build new communication theories are discussed.
引用
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页码:26 / 34
页数:9
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