从社交媒体中使用生物医学知识图形的度量骨干进行专注的数字队列选择
Ziqi Guo1, Jack Felag1, Jordan C Rozum1
1School of Systems Science & Industrial Engineering, Binghamton University, Binghamton, NY, USA.
Journal of biomedical informatics
|June 3, 2025
概括
研究人员开发了一种使用知识图的新方法,以识别在社交媒体上讨论的相关用户. 这种方法可以过掉不相关的内容,确保医学研究的聚焦队列.
科学领域:
- 计算语言学计算语言学
- 社交媒体分析.
- 生物医学信息学是生物医学信息学.
背景情况:
- 社交媒体数据为健康研究提供了大量的数字队列.
- 鉴定特定健康问题的相关用户是具有挑战性的,因为平台上的话语普遍性各不相同.
- 像X (以前的Twitter) 和Instagram这样的一般平台不同于像Reddit子组和患者倡导网站这样的专业论坛.
研究的目的:
- 开发和测试一种用于过社交媒体上相关用户的一般方法,用于生物医学研究.
- 创建专注的用户队伍来研究特定的健康问题,使用论作为案例研究.
主要方法:
- 从使用医学术语词典的社交媒体文本中构建知识图 (KG).
- 应用了计量骨干计算来分散KG并识别核心话语结构.
- 将用户对KG骨干的贡献与一般和专注的平台进行了比较.
- 使用人类注释来验证用户相关性和术语使用一致性.
主要成果:
- 以为重点的社交媒体用户比一般用途的社交媒体用户在很大程度上为KG的骨干做出了贡献.
- 不为脊柱贡献的用户更有可能滥用生物医学术语.
- 计量骨干方法成功地过了不相关的用户和内容.
结论:
- 计量骨干方法有效地产生了针对生物医学话语的专注用户队伍.
- 这种方法保留了具有宝贵见解的低参与度用户,并过了高参与度的无关用户.
- 这种方法是无参数的,以代数原理为基础的,不需要分类器或人类策划.
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