在社交网络服务的不同子领域理解心理健康问题:基于文本的Reddit帖子的计算分析
Seoyun Kim1, Junyeop Cha1, Dongjae Kim1
1Department of Applied Artificial Intelligence, Sungkyunkwan University, Seoul, Republic of Korea.
Journal of medical Internet research
|November 30, 2023
概括
社交网络服务透露了精神健康状况中不同的语言模式. 机器学习可以识别这些独特的语言标记,用于在线心理健康支持.
科学领域:
- 计算语言学 计算语言学
- 精神病学是一个精神病学.
- 社交媒体分析 社交媒体分析
背景情况:
- 社交网络服务 (SNSs) 越来越多地用于分享情感和寻求心理健康建议.
- 瑞迪特 (Reddit) 举办了许多专门讨论心理健康的子瑞迪特,提供了丰富的数据来源.
研究的目的:
- 分析各种精神障碍子的用户帖子中的语言特征.
- 通过比较这些语言特征与非精神疾病子标签来验证这些语言特征的独特性.
- 通过机器学习来确认这些语言差异是可以学习的.
主要方法:
- 采集和分析了300多万条Reddit帖子,使用语言调查和词汇计数 (LIWC) 软件.
- 采用统计分析 (一向ANOVA) 和机器学习 (BERT嵌入,集群) 来识别语言模式.
- 心理健康子reddits中的词汇和语义特征与与之无关的子reddits进行了比较.
主要成果:
- 在各种心理健康状况下,在各个分支中发现了显著的语言差异 (P<.001).
- 对于每一个心理健康问题,LIWC的分析揭示了不同的词数和情绪模式.
- 聚类方法成功地确定了每个子reddit组的独特特征,通过Davies-Bouldin和轮得分进行验证.
结论:
- 对在线文本的多方面的分析提供了对具有特定心理健康问题的个人的语言特征的洞察.
- 这些发现可以帮助临床医生了解患者的心理健康,并为在线干预策略提供信息.
- 促进在语言分析和机器学习领域的进一步研究,以识别脆弱的在线群体.
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