论文标题
社交媒体报告偏头痛报告的可推广的自然语言处理框架
Generalizable Natural Language Processing Framework for Migraine Reporting from Social Media
论文作者
论文摘要
偏头痛是一种高尚和致残的神经系统疾病。但是，现实世界中的信息偏头痛管理可能仅限于传统的健康信息来源。在本文中，我们（i）验证了由偏头痛患者自我报告的社交媒体（Twitter和Reddit）上有大量与偏头痛有关的聊天。 （ii）开发一个与平台无关的文本分类系统，用于自动检测自我报告的偏头痛相关的帖子，以及（iii）对自我报告的帖子进行分析，以评估社交媒体对研究此问题的实用性。我们手动注释了5750个Twitter帖子和302个Reddit帖子。我们的系统在Twitter上的F1得分为0.90，在Reddit上获得了0.93。我们“偏头痛队列”发布的信息的分析表明，存在有关偏头痛疗法和与之相关的患者情感的大量相关信息。我们的研究构成了使用社交媒体数据对偏头痛相关信息进行深入分析的基础。
Migraine is a high-prevalence and disabling neurological disorder. However, information migraine management in real-world settings could be limited to traditional health information sources. In this paper, we (i) verify that there is substantial migraine-related chatter available on social media (Twitter and Reddit), self-reported by migraine sufferers; (ii) develop a platform-independent text classification system for automatically detecting self-reported migraine-related posts, and (iii) conduct analyses of the self-reported posts to assess the utility of social media for studying this problem. We manually annotated 5750 Twitter posts and 302 Reddit posts. Our system achieved an F1 score of 0.90 on Twitter and 0.93 on Reddit. Analysis of information posted by our 'migraine cohort' revealed the presence of a plethora of relevant information about migraine therapies and patient sentiments associated with them. Our study forms the foundation for conducting an in-depth analysis of migraine-related information using social media data.
