[论文解读] Symptom extraction from the narratives of personal experiences with COVID-19 on Reddit

Curtis Murray, Lewis Mitchell+2 more|arXiv (Cornell University)|May 21, 2020

Mental Health via Writing参考文献 27被引用 13

一句话总结

本研究通过在14天自我报告时间线上对新冠阳性个体的Reddit叙述进行主题建模与情感分析，提取症状进展与情绪动态。结果显示，早期症状（如发热、咳嗽）在初期达到高峰，呼吸问题在第10天左右上升，负面情绪与症状严重程度强相关，为患者体验及潜在心理健康支持需求提供了洞见。

ABSTRACT

Social media discussion of COVID-19 provides a rich source of information into how the virus affects people's lives that is qualitatively different from traditional public health datasets. In particular, when individuals self-report their experiences over the course of the virus on social media, it can allow for identification of the emotions each stage of symptoms engenders in the patient. Posts to the Reddit forum r/COVID19Positive contain first-hand accounts from COVID-19 positive patients, giving insight into personal struggles with the virus. These posts often feature a temporal structure indicating the number of days after developing symptoms the text refers to. Using topic modelling and sentiment analysis, we quantify the change in discussion of COVID-19 throughout individuals' experiences for the first 14 days since symptom onset. Discourse on early symptoms such as fever, cough, and sore throat was concentrated towards the beginning of the posts, while language indicating breathing issues peaked around ten days. Some conversation around critical cases was also identified and appeared at a roughly constant rate. We identified two clear clusters of positive and negative emotions associated with the evolution of these symptoms and mapped their relationships. Our results provide a perspective on the patient experience of COVID-19 that complements other medical data streams and can potentially reveal when mental health issues might appear.

研究动机与目标

• 通过Reddit上的第一人称叙述，理解新冠患者体验的演变过程。
• 识别症状发作后14天内症状报告与情绪状态的时间模式。
• 探讨情感分析与主题建模如何揭示与症状进展相关的情绪弧线。
• 通过捕捉疾病主观体验，为传统公共卫生数据集提供互补的数据流。

提出的方法

• 从r/COVID19Positive版块收集1,045篇带有“Tested Positive - Me”或“Tested Positive”标签的Reddit帖子。
• 使用正则表达式提取日期参考，为每篇帖子标注其参考天数（症状发作日）。
• 应用潜在狄利克雷分布（LDA）进行主题建模，以识别随时间演变的症状相关主题。
• 使用NRC词典进行情感分析，评估帖子中的情感基调。
• 计算主题与情感得分之间的皮尔逊相关系数，以探索其关联性。
• 采用层次聚类与多维尺度分析（MDS）可视化主题-情感结构。

实验结果

研究问题

• RQ1 在自我报告的Reddit叙述中，症状相关主题在发病后14天内如何演变？
• RQ2 患者在病程中的情绪情感随时间呈现何种模式？
• RQ3 特定症状（如发热、呼吸问题）与情感变化在时间上如何关联？
• RQ4 当症状与情绪随时间共同分析时，会浮现哪些症状与情绪聚类？
• RQ5 社交媒体叙述如何在临床数据之外，增进公共卫生理解？

主要发现

• 发热、咳嗽和咽喉痛等早期症状在症状发作后第3至5天被最频繁提及。
• 与呼吸相关的症状（如“breathing”、“chest”、“lungs”）在第10天左右达到高峰，表明其为后期症状群。
• 负面情绪（包括恐惧与焦虑）在整个病程中保持高位，且在第10天左右出现显著加剧。
• 与呼吸系统症状相关的主题与整体情感得分之间观察到强烈的负相关性。
• 识别出两个明显的情绪聚类：一个与早期疾病相关，另一个与严重或持续症状相关。
• 分析显示，关于重症病例的讨论频率保持稳定，表明公众对严重后果的持续关注。