用NLP分析红迪网疼痛讨论，发现女性更倾向表达情绪，且偏头痛更常见
Exploring Gender Differences in Chronic Pain Discussions on Reddit
- 用隐属性模型+CNN分类用户性别，F1达0.86
- 女性发帖更情绪化，偏头痛和鼻窦炎在女性中更普遍
- 揭示药物效果存在性别差异，适合医学与社会学研究者
疼痛是人类存在的固有部分，表现为生理与心理体验，可分为急性或慢性。多年来，科学界对疼痛成因及治疗进行了广泛研究，但早期研究常忽视性别因素。本研究利用自然语言处理（NLP）分析个体疼痛经历，重点关注性别差异。通过基于用户名聚合帖子，采用隐属性模型-卷积神经网络（HAM-CNN）成功将帖子分类为男性与女性语料库，F1得分为0.86。分析显示，女性帖子更侧重情感表达。此外，研究发现偏头痛与鼻窦炎在女性中更为常见，并探讨了止痛药在不同性别中的影响差异。
原文摘要 · Abstract (English)
Pain is an inherent part of human existence, manifesting as both physical and emotional experiences, and can be categorized as either acute or chronic. Over the years, extensive research has been conducted to understand the causes of pain and explore potential treatments, with contributions from various scientific disciplines. However, earlier studies often overlooked the role of gender in pain experiences. In this study, we utilized Natural Language Processing (NLP) to analyze and gain deeper insights into individuals' pain experiences, with a particular focus on gender differences. We successfully classified posts into male and female corpora using the Hidden Attribute Model-Convolutional Neural Network (HAM-CNN), achieving an F1 score of 0.86 by aggregating posts based on usernames. Our analysis revealed linguistic differences between genders, with female posts tending to be more emotionally focused. Additionally, the study highlighted that conditions such as migraine and sinusitis are more prevalent among females and explored how pain medication affects individuals differently based on gender.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。