自然语言处理使用离散数学分析揭示了社交媒体上的疼痛沟通的网络结构
Nobuo Okui1,2,3, Shigeo Horie4,5
1Data Science and Informatics for Genetic Disorders, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan. okuinobuo@gmail.com.
Scientific reports
|August 9, 2025
概括
社交媒体上的疼痛讨论揭示了复杂的语言网络. 对Reddit评论的网络分析确定了"疼痛"和"头痛"等核心术语,揭示了不同的专题社区,并为临床沟通策略提供了信息.
科学领域:
- 计算语言学 计算语言学
- 社交网络分析 社交网络分析
- 数字健康数字健康
背景情况:
- 在线平台促进了疼痛经历的自然表达.
- 了解疼痛话语的网络结构至关重要,但未被充分探索.
研究的目的:
- 分析社交媒体上与疼痛相关的讨论的语义网络结构.
- 识别在线疼痛话语中的关键术语,社区结构和语言模式.
主要方法:
- 自然语言处理和网络分析应用于57,000条Reddit评论.
- 用于构建和分析一个由5,630个节点和86,972个边缘组成的网络的离散数学原理.
- 用于绘制语言关系的中心度和社区检测算法.
主要成果:
- 疼痛话语网络显示低密度 (0.0055) 和高集群 (0.7700),表明不同的主题社区.
- 术语"疼痛"是中心的 (中心度为0.821429),定语义领域,而诸如"头痛"这样的术语则充当桥梁节点.
- 社区检测发现了12个集群,其中最大的集群包含1021个节点,突出了不同的疼痛传播方面. 像"燃烧"这样的比喻术语显示出不同的共同发生模式.
结论:
- 网络分析揭示了在线疼痛话语的复杂语义结构.
- 核心疼痛术语表现出稳定的中心性,而外围术语则更加可变.
- 研究结果表明,社交媒体网络分析可以增强临床沟通和症状评估.
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