通过推特的时间主题建模来检测和跟踪抑郁症:来自180天研究的见解
Ranganathan Chandrasekaran1,2, Suhas Kotaki3, Abhilash Hosaagrahaara Nagaraja3
1Department of Information & Decision Sciences, University of Illinois at Chicago, Chicago, IL, USA. ranga@uic.edu.
Npj mental health research
|December 6, 2024
概括
社交媒体分析揭示了临床诊断后与抑郁症相关的语言模式的变化. 这项研究表明,在线行为如何有助于跟踪心理健康状况和治疗有效性.
科学领域:
- 计算社会科学 计算社会科学
- 心理健康信息学心理健康信息学
- 数字精神病学数字精神病学
背景情况:
- 抑郁症影响全球超过2.8亿人,由于耻辱和认识差距,大量未被诊断/未治疗的病例.
- 像X (以前的Twitter) 这样的社交媒体平台为监测抑郁症指标提供了机会.
- 分析用户生成的内容可以提供对心理健康趋势和个人经验的见解.
研究的目的:
- 在临床抑郁症诊断之前和之后,分析X (以前的Twitter) 上语言和主题的变化.
- 开发和评估机器学习模型,根据他们的推特来区分抑郁和非抑郁的用户.
- 评估社交媒体数据对追踪抑郁症标志物和治疗相关讨论的潜力.
主要方法:
- 收集了来自229名被诊断用户的246,637条推文和来自对照组的284,772条推文.
- 使用CoreEx主题建模来识别用户讨论中的七个关键主题.
- 使用条件后勤回归和机器学习分类器 (SVM,Naive Bayes,后勤回归) 进行分析.
主要成果:
- 七个不同的主题出现了:原因,身体/精神症状,亵,治疗,应对和生活方式.
- 机器学习模型,特别是物流回归和支持矢量机 (SVM),在区分用户群体方面表现出有效性.
- 在诊断后观察到主题内容的显著转变,表明与病情相关的用户表达的变化.
结论:
- X (以前的Twitter) 数据有潜力监测抑郁症及其相关症状的演变.
- 社交媒体分析可以跟踪抑郁症患者应对策略和治疗参与度的变化.
- 通过社交媒体进行数字表型化提供了一种新的方法来理解和潜在地干预心理健康状况.
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