基于Reddit评论的COVID-19对心理健康的影响分析

Justin Q Chen, Kevin Qi, Aaron Zhang, M. Shalaginov, TingyingHelen Zeng
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摘要

随着COVID-19疫情继续改变日常生活的关键方面,许多人怀疑该病毒也对心理健康产生了相当大的影响。本研究使用自然语言处理(NLP)和机器学习对Reddit网站的评论进行处理,以确定COVID-19大流行对5个心理健康社区的影响:r/anxiety, r/depression, r/SuicideWatch, r/mentalhealth和r/COVID19_support。通过应用支持向量机,我们从数据中提取特征,以确定这些子reddit在COVID-19大流行期间最努力解决的问题。然后,我们使用长短期记忆(LSTM)递归神经网络来研究大流行期间reddit每个子版块的情绪变化。结果表明,在所研究的潜在因素中,孤立感对COVID-19期间的心理健康影响最大。此外,r/COVID19_support用户的平均情绪与美国每月新增COVID-19病例数呈反比关系。通过这项研究,我们揭示了支持向量机和LSTM神经网络在分析与COVID-19相关的社交媒体评论中的心理健康方面的有效性。随着COVID-19大流行的进展和更多数据的获得,本研究中提出的过程可以深入了解受COVID-19影响最大的精神卫生社区,以及造成最多精神卫生问题的大流行的影响。这些发现可能为政策制定者和心理健康医生提供有价值的信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
COVID-19 Impact on Mental Health Analysis based on Reddit Comments
As the COVID-19 outbreak continues to change crucial aspects of daily life, many suspect that the virus has also had a considerable impact on mental health. This study uses natural language processing (NLP) and machine learning on comments from the website Reddit to determine the effects of the COVID-19 pandemic on 5 mental health communities: r/anxiety, r/depression, r/SuicideWatch, r/mentalhealth, and r/COVID19_support. By applying a support vector machine, we extracted features from the data to determine the issues that these subreddits were struggling with the most during the COVID-19 pandemic. We then used a long short-term memory (LSTM) recurrent neural network to study the change in sentiment of each subreddit over the course of the pandemic. Results indicated that, out of the potential factors studied, feelings of isolation had the most impact on mental health during COVID-19. Additionally, the average sentiment of users from r/COVID19_support has an inverse relationship with the number of new COVID-19 cases per month in the United States. Through this research, we revealed the effectiveness of support vector machines and LSTM neural networks in analyzing mental health in social media comments related to COVID-19. As the COVID-19 pandemic progresses and more data becomes available, processes like the one presented in this research can provide insight into the mental health communities that are most influenced by COVID-19 and the effects of the pandemic that cause the most mental health issues. These findings may produce valuable information for policymakers and mental health physicians.
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