Public Perception on Healthcare Services: Evidence from Social Media Platforms in China

Guangyu Hu, Xueyan Han, Huixuan Zhou and Yuanli Liu
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Guangyu Hu: School of Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
Xueyan Han: School of Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
Huixuan Zhou: School of Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
Yuanli Liu: School of Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China

IJERPH, 2019, vol. 16, issue 7, 1-10

Abstract: Social media has been used as data resource in a growing number of health-related research. The objectives of this study were to identify content volume and sentiment polarity of social media records relevant to healthcare services in China. A list of the key words of healthcare services were used to extract data from WeChat and Qzone, between June 2017 and September 2017. The data were put into a corpus, where content analyses were performed using Tencent natural language processing (NLP). The final corpus contained approximately 29 million records. Records on patient safety were the most frequently mentioned topic (approximately 8.73 million, 30.1% of the corpus), with the contents on humanistic care having received the least social media references (0.43 Million, 1.5%). Sentiment analyses showed 36.1%, 16.4%, and 47.4% of positive, neutral, and negative emotions, respectively. The doctor-patient relationship category had the highest proportion of negative contents (74.9%), followed by service efficiency (59.5%), and nursing service (53.0%). Neutral disposition was found to be the highest (30.4%) in the contents on appointment-booking services. This study added evidence to the magnitude and direction of public perceptions on healthcare services in China’s hospital and pointed to the possibility of monitoring healthcare service improvement, using readily available data in social media.

Keywords: healthcare; social media; China; WeChat; Qzone; natural language processing (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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