Griffis Heather, Asch David A, Schwartz H Andrew, Ungar Lyle, Buttenheim Alison M, Barg Frances K, Mitra Nandita, Merchant Raina M
Children's Hospital of Philadelphia, Philadelphia, PA, United States.
University of Pennsylvania, Philadelphia, PA, United States.
JMIR Diabetes. 2020 Jan 26;5(1):e14431. doi: 10.2196/14431.
Social media posts about diabetes could reveal patients' knowledge, attitudes, and beliefs as well as approaches for better targeting of public health messages and care management.
This study aimed to characterize the language of Twitter users' posts regarding diabetes and describe the correlation of themes with the county-level prevalence of diabetes.
A retrospective study of diabetes-related tweets identified from a random sample of approximately 37 billion tweets from the United States from 2009 to 2015 was conducted. We extracted diabetes-specific tweets and used machine learning to identify statistically significant topics of related terms. Topics were combined into themes and compared with the prevalence of diabetes by US counties and further compared with geography (US Census Divisions). Pearson correlation coefficients are reported for each topic and relationship with prevalence.
A total of 239,989 tweets from 121,494 unique users included the term diabetes. The themes emerging from the topics included unhealthy food and drink, treatment, symptoms/diagnoses, risk factors, research, recipes, news, health care, management, fundraising, diet, communication, and supplements/remedies. The theme of unhealthy foods most positively correlated with geographic areas with high prevalence of diabetes (r=0.088), whereas tweets related to research most negatively correlated (r=-0.162) with disease prevalence. Themes and topics about diabetes differed in overall frequency across the US geographical divisions, with the East South Central and South Atlantic states having a higher frequency of topics referencing unhealthy food (r range=0.073-0.146; P<.001).
Diabetes-related tweets originating from counties with high prevalence of diabetes have different themes than tweets originating from counties with low prevalence of diabetes. Interventions could be informed from this variation to promote healthy behaviors.
关于糖尿病的社交媒体帖子可以揭示患者的知识、态度和信念，以及更好地定位公共卫生信息和护理管理的方法。
本研究旨在描述推特用户关于糖尿病的帖子语言特征，并描述这些主题与县级糖尿病患病率的相关性。
对2009年至2015年从美国约370亿条推文的随机样本中识别出的与糖尿病相关的推文进行回顾性研究。我们提取了特定于糖尿病的推文，并使用机器学习来识别相关术语的统计学显著主题。将主题合并为主题类别，并与美国各县的糖尿病患病率进行比较，进而与地理区域（美国人口普查分区）进行比较。报告每个主题与患病率的皮尔逊相关系数及关系。
来自121494个唯一用户的总共239989条推文包含“糖尿病”一词。从这些主题中浮现出的主题类别包括不健康的食物和饮料、治疗、症状/诊断、危险因素、研究、食谱、新闻、医疗保健、管理、筹款、饮食、交流以及补充剂/疗法。不健康食物主题与糖尿病高患病率地理区域的相关性最为显著（r = 0.088），而与研究相关的推文与疾病患病率的负相关性最强（r = -0.162）。关于糖尿病的主题类别和主题在美国各地理分区的总体频率有所不同，东中南部和南大西洋各州提及不健康食物的主题频率较高（r范围 = 0.073 - 0.146；P <.001）。
来自糖尿病高患病率县的与糖尿病相关的推文主题与来自低患病率县的推文主题不同。可以根据这种差异制定干预措施以促进健康行为。
