收集推特数据用于研究残疾人群体的运动行为:Python中的介绍和教程
Nicholas E Fears1,2, Riya Chatterjee1, Priscila M Tamplain3
1School of Kinesiology, University of Michigan, Ann Arbor, MI, USA.
Journal of motor learning and development
|January 29, 2024
概括
研究人员可以使用Twitter数据来了解不同临床群体的生活经验. 本框架详细介绍了如何收集和分析社交媒体数据,以深入了解神经发育状况和生活质量.
科学领域:
- 计算社会科学 计算社会科学
- 数字健康数字健康
- 临床信息学 临床信息学
背景情况:
- 社交媒体平台为临床人群中的个人经验提供了丰富的数据.
- 公开可用的数据可以阐明关于特定健康特征的自我倡导观点.
- 了解生活经验对于改善生活质量至关重要.
研究的目的:
- 为临床研究收集和分析Twitter数据提供一个框架.
- 证明社交媒体数据对于理解神经发育状况的有用性.
- 为研究人员提供一种成本效益高,灵活的方法.
主要方法:
- 使用Twitter免费的学术研究人员应用程序编程界面 (API).
- 采用Python,一个免费的,开源的编程语言,用于数据收集和分析.
- 制定适用于不同临床群体和特征的框架.
主要成果:
- 创建了一个与神经发育条件中的运动差异相关的Twitter数据样本数据集.
- 该框架促进了对个人表达和社区讨论的审查.
- 分析揭示了对特定诊断群体相关的生活经验的洞察力.
结论:
- 社交媒体数据,特别是来自Twitter的数据,为临床人群提供了有价值的见解.
- 拟议的框架为研究人员提供了一种可访问和有效的方法.
- 使用该框架的进一步研究可以增强对患有临床疾病的个体的理解和改善生活质量.
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