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

被引:18
作者
Hu, Guangyu [1 ,2 ]
Han, Xueyan [1 ,2 ]
Zhou, Huixuan [1 ,2 ]
Liu, Yuanli [1 ,2 ]
机构
[1] Chinese Acad Med Sci, Sch Publ Hlth, Beijing 100730, Peoples R China
[2] Peking Union Med Coll, Beijing 100730, Peoples R China
基金
中国国家自然科学基金;
关键词
healthcare; social media; China; WeChat; Qzone; natural language processing; DOCTOR;
D O I
10.3390/ijerph16071273
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页数:10
相关论文
共 37 条
  • [21] Twitter as a Tool for Health Research: A Systematic Review
    Sinnenberg, Lauren
    Buttenheim, Alison M.
    Padrez, Kevin
    Mancheno, Christina
    Ungar, Lyle
    Merchant, Raina M.
    [J]. AMERICAN JOURNAL OF PUBLIC HEALTH, 2017, 107 (01) : E1 - E8
  • [22] Social Big Data Analysis of Information Spread and Perceived Infection Risk During the 2015 Middle East Respiratory Syndrome Outbreak in South Korea
    Song, Juyoung
    Song, Tae Min
    Seo, Dong-Chul
    Jin, Dal-Lae
    Kim, Jung Sun
    [J]. CYBERPSYCHOLOGY BEHAVIOR AND SOCIAL NETWORKING, 2017, 20 (01) : 22 - 29
  • [23] Consumer satisfaction with tertiary healthcare in China: findings from the 2015 China National Patient Survey
    Sun, Jing
    Hu, Guangyu
    Ma, Jing
    Chen, Yin
    Wu, Laiyang
    Liu, Qiannan
    Hu, Jia
    Livoti, Christine
    Jiang, Yu
    Liu, Yuanli
    [J]. INTERNATIONAL JOURNAL FOR QUALITY IN HEALTH CARE, 2017, 29 (02) : 213 - 221
  • [24] Sun S, 2011, LANCET, V377, P1747, DOI 10.1016/S0140-6736(11)60733-2
  • [25] Characterizing Depression Issues on Sina Weibo
    Tian, Xianyun
    Batterham, Philip
    Song, Shuang
    Yao, Xiaoxu
    Yu, Guang
    [J]. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2018, 15 (04)
  • [26] Utility of social media and crowd-sourced data for pharmacovigilance: a scoping review protocol
    Tricco, Andrea C.
    Zarin, Wasifa
    Lillie, Erin
    Pham, Ba
    Straus, Sharon E.
    [J]. BMJ OPEN, 2017, 7 (01):
  • [27] What do patients care most about in China's public hospitals? Interviews with patients in Jiangsu Province
    Wang, Xuanxuan
    Jiang, Rongqin
    Li, Jingxian
    Chen, Jiaying
    Burstrom, Bo
    Burstrom, Kristina
    [J]. BMC HEALTH SERVICES RESEARCH, 2018, 18
  • [28] How to decrease violence against doctors in China?
    Wang, Yujun
    Fang, Ming
    Wang, Yuedong
    [J]. INTERNATIONAL JOURNAL OF CARDIOLOGY, 2016, 211 : 66 - 66
  • [29] Weeg Christopher, 2015, JMIR Public Health Surveill, V1, pe6, DOI 10.2196/publichealth.3953
  • [30] Wong K.O., 2016, P 8 INT JOINT C KNOW