应用LDA模型在X社交网络上的远程医疗对话中识别主题
Mario Sierra Martín1, Fang-Wei Chen2, Pilar Alarcón Urbistondo2
1Faculty of Economic and Business Sciences, University of Malaga, Andalucía Tech, Campus de Ejido, 6, Málaga, 29013, Spain. msierra@uma.es.
BMC health services research
|March 13, 2025
概括
社交媒体评论显示,患者和专业人士主要讨论一般远程医疗主题,重点关注方便性和可访问性. 这些数据为改善远程医疗保健沟通策略提供了见解.
科学领域:
- 数字健康与远程医疗
- 社交媒体分析
- 自然语言处理自然语言处理.
背景情况:
- 后COVID-19时代需要在医疗保健中增加数字媒体的使用.
- 远程医疗,通过ICT进行远程健康实践,已经呈指数级增长.
- 患者和专业人士越来越多地使用社交媒体平台进行与健康有关的讨论.
研究的目的:
- 分析用户在社交媒体上关于远程医疗的评论.
- 确定远程医疗消费者讨论的关键主题,关注点和好处.
- 为增强远程医疗传播策略提供见解.
主要方法:
- 一种混合方法的方法,结合了定性和定量技术.
- 使用自然语言处理 (NLP) 来分析来自"X" (以前的Twitter) 的156,633条评论.
- 应用隐性迪里克莱特分配 (LDA) 机器学习模型来识别主题.
主要成果:
- 常见的关键词包括"健康"",服务"",医生"和"患者".
- 确定了四个主要方面:一般信息,所寻求的利益,具体信息和专业问题.
- 60.1%的评论集中在通用远程医疗主题,易用性和服务信息上;具体的健康问题不太频繁.
结论:
- 像"X"这样的社交媒体平台主要用于一般远程医疗查询,强调方便性和可访问性.
- 这项研究为改善远程医疗沟通和发展战略提供了宝贵的见解.
- 了解社交网络上的受众偏好对于有效的远程医疗推广至关重要.
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