Methods for Analyzing the Contents of Social Media for Health Care: Scoping Review (Preprint)

Abstract

BACKGROUND Given the rapid development of social media, how to effectively extract and analysis contents of social media for health care has attracted widespread attention from healthcare providers. As far as we know, most of the reviews focus on the application of social media, and there is no review that integrates the methods for analyzing social information for health care. OBJECTIVE This scoping review aims to solve the two questions on (1) What types of research have been used to investigate social media for health care? (2) What methods have been used to analyze the existing health information on social media? METHODS A scoping review following PRISMA guidance was conducted. We searched PubMed, Web of Science, EMBASE, CINAHL, and Cochrane Library from inception to February 2022 for primary studies focused on social media and health care. Two independent reviewers screened eligible studies against inclusion criteria. A narrative synthesis of the included studies was conducted. RESULTS Of 10073 identified citations, 113 studies were included in the review. These included 58 qualitative designs, 33 quantitative designs, and 22 mixed-methods designs. The applied research methods are divided into manual analysis methods (content analysis methodology, grounded theory, ethnography, classification analysis, thematic analysis, and scoring table) and computer-aided analysis methods (latent Dirichlet allocation, support vector machine, probabilistic clustering, image analysis technology, natural language processing, topic modeling, and sentiment analysis). CONCLUSIONS Traditional content analysis is still the mainstream of social media information analysis, and future research may be combined with big data research. With the progress of computers, mobile phones, smart watches, and other smart devices, social media information sources will become more diversified. Future research can combine new sources such as pictures, videos, and physiological signals with online social networking to adapt to the development trend of the internet. More medical information talents need to be trained in the future to better solve the problem of network information analysis.

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