Zhao Yunpeng, Zhang Hansi, Huo Jinhai, Guo Yi, Wu Yonghui, Prosperi Mattia, Bian Jiang
University of Florida, Gainesville, Florida, USA.
AMIA Jt Summits Transl Sci Proc. 2020 May 30;2020:730-739. eCollection 2020.
Palliative care is a specialized service with proven efficacy in improving patients' quality-of-life. Nevertheless, lack of awareness and misunderstanding limits its adoption. Research is urgently needed to understand the determinants (e.g., knowledge) related to its adoption. Traditionally, these determinants are measured with questionnaires. In this study, we explored Twitter to reveal these determinants guided by the Integrated Behavioral Model. A secondary goal is to assess the feasibility of extracting user demographics from Twitter data-a significant shortcoming in existing studies that limits our ability to explore more fine-grained research questions (e.g., gender difference). Thus, we collected, preprocessed, and geocoded palliative care-related tweets from 2013 to 2019 and then built classifiers to: 1) categorize tweets into promotional vs. consumer discussions, and 2) extract user gender. Using topic modeling, we explored whether the topics learned from tweets are comparable to responses of palliative care-related questions in the Health Information National Trends Survey.
姑息治疗是一项在改善患者生活质量方面已被证明具有疗效的专业服务。然而,缺乏认知和误解限制了其应用。迫切需要开展研究以了解与采用该疗法相关的决定因素(例如知识)。传统上,这些决定因素是通过问卷调查来衡量的。在本研究中,我们以整合行为模型为指导,通过推特来揭示这些决定因素。第二个目标是评估从推特数据中提取用户人口统计学信息的可行性,这是现有研究中的一个重大缺陷,限制了我们探索更细化研究问题(例如性别差异)的能力。因此,我们收集、预处理并对2013年至2019年与姑息治疗相关的推文进行了地理编码,然后构建分类器以:1)将推文分类为宣传性讨论与消费者讨论,以及2)提取用户性别。通过主题建模,我们探讨了从推文中学到的主题是否与《健康信息国家趋势调查》中与姑息治疗相关问题的回答具有可比性。