Suppr超能文献

探讨与 COVID-19 相关应激源:主题建模研究。

Exploring COVID-19-Related Stressors: Topic Modeling Study.

机构信息

Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada.

Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, ON, Canada.

出版信息

J Med Internet Res. 2022 Jul 13;24(7):e37142. doi: 10.2196/37142.

Abstract

BACKGROUND

The COVID-19 pandemic has affected the lives of people globally for over 2 years. Changes in lifestyles due to the pandemic may cause psychosocial stressors for individuals and could lead to mental health problems. To provide high-quality mental health support, health care organizations need to identify COVID-19-specific stressors and monitor the trends in the prevalence of those stressors.

OBJECTIVE

This study aims to apply natural language processing (NLP) techniques to social media data to identify the psychosocial stressors during the COVID-19 pandemic and to analyze the trend in the prevalence of these stressors at different stages of the pandemic.

METHODS

We obtained a data set of 9266 Reddit posts from the subreddit \rCOVID19_support, from February 14, 2020, to July 19, 2021. We used the latent Dirichlet allocation (LDA) topic model to identify the topics that were mentioned on the subreddit and analyzed the trends in the prevalence of the topics. Lexicons were created for each of the topics and were used to identify the topics of each post. The prevalences of topics identified by the LDA and lexicon approaches were compared.

RESULTS

The LDA model identified 6 topics from the data set: (1) "fear of coronavirus," (2) "problems related to social relationships," (3) "mental health symptoms," (4) "family problems," (5) "educational and occupational problems," and (6) "uncertainty on the development of pandemic." According to the results, there was a significant decline in the number of posts about the "fear of coronavirus" after vaccine distribution started. This suggests that the distribution of vaccines may have reduced the perceived risks of coronavirus. The prevalence of discussions on the uncertainty about the pandemic did not decline with the increase in the vaccinated population. In April 2021, when the Delta variant became prevalent in the United States, there was a significant increase in the number of posts about the uncertainty of pandemic development but no obvious effects on the topic of fear of the coronavirus.

CONCLUSIONS

We created a dashboard to visualize the trend in the prevalence of topics about COVID-19-related stressors being discussed on a social media platform (Reddit). Our results provide insights into the prevalence of pandemic-related stressors during different stages of the COVID-19 pandemic. The NLP techniques leveraged in this study could also be applied to analyze event-specific stressors in the future.

摘要

背景

COVID-19 大流行已经影响了全球两年多的人们的生活。由于大流行导致的生活方式的改变可能会给个人带来心理社会压力源,并可能导致心理健康问题。为了提供高质量的心理健康支持,医疗保健组织需要确定 COVID-19 特有的压力源,并监测这些压力源的流行趋势。

目的

本研究旨在应用自然语言处理(NLP)技术对社交媒体数据进行分析,以确定 COVID-19 大流行期间的心理社会压力源,并分析大流行不同阶段这些压力源的流行趋势。

方法

我们从 2020 年 2 月 14 日至 2021 年 7 月 19 日从 Reddit 子版块 COVID19_support 中获取了 9266 个 Reddit 帖子的数据集。我们使用潜在狄利克雷分配(LDA)主题模型来识别子版块上提到的主题,并分析主题的流行趋势。为每个主题创建了词汇表,并用于识别每个帖子的主题。比较了 LDA 和词汇方法识别的主题的流行度。

结果

LDA 模型从数据集中识别出 6 个主题:(1)“对冠状病毒的恐惧”,(2)“与社会关系有关的问题”,(3)“心理健康症状”,(4)“家庭问题”,(5)“教育和职业问题”和(6)“对大流行发展的不确定性”。根据结果,疫苗分发开始后,关于“对冠状病毒的恐惧”的帖子数量明显下降。这表明疫苗的分发可能降低了对冠状病毒的感知风险。随着接种人口的增加,对大流行不确定性的讨论的流行度并没有下降。2021 年 4 月,当 Delta 变体在美国流行时,关于大流行发展不确定性的帖子数量显著增加,但对冠状病毒恐惧的主题没有明显影响。

结论

我们创建了一个仪表板来可视化社交媒体平台(Reddit)上讨论的与 COVID-19 相关压力源主题的流行趋势。我们的研究结果提供了在 COVID-19 大流行不同阶段与大流行相关压力源的流行情况的见解。本研究中利用的 NLP 技术也可用于分析未来特定事件的压力源。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验