Department of Computer Science, Kent State University, Kent, OH, United States.
Center for Public Policy and Health, Kent State University, Kent, OH, United States.
J Med Internet Res. 2023 Jul 12;25:e46867. doi: 10.2196/46867.
BACKGROUND: The COVID-19 pandemic has resulted in heightened levels of depression, anxiety, and other mental health issues due to sudden changes in daily life, such as economic stress, social isolation, and educational irregularity. Accurately assessing emotional and behavioral changes in response to the pandemic can be challenging, but it is essential to understand the evolving emotions, themes, and discussions surrounding the impact of COVID-19 on mental health. OBJECTIVE: This study aims to understand the evolving emotions and themes associated with the impact of COVID-19 on mental health support groups (eg, r/Depression and r/Anxiety) on Reddit (Reddit Inc) during the initial phase and after the peak of the pandemic using natural language processing techniques and statistical methods. METHODS: This study used data from the r/Depression and r/Anxiety Reddit communities, which consisted of posts contributed by 351,409 distinct users over a period spanning from 2019 to 2022. Topic modeling and Word2Vec embedding models were used to identify key terms associated with the targeted themes within the data set. A range of trend and thematic analysis techniques, including time-to-event analysis, heat map analysis, factor analysis, regression analysis, and k-means clustering analysis, were used to analyze the data. RESULTS: The time-to-event analysis revealed that the first 28 days following a major event could be considered a critical window for mental health concerns to become more prominent. The theme trend analysis revealed key themes such as economic stress, social stress, suicide, and substance use, with varying trends and impacts in each community. The factor analysis highlighted pandemic-related stress, economic concerns, and social factors as primary themes during the analyzed period. Regression analysis showed that economic stress consistently demonstrated the strongest association with the suicide theme, whereas the substance theme had a notable association in both data sets. Finally, the k-means clustering analysis showed that in r/Depression, the number of posts related to the "depression, anxiety, and medication" cluster decreased after 2020, whereas the "social relationships and friendship" cluster showed a steady decrease. In r/Anxiety, the "general anxiety and feelings of unease" cluster peaked in April 2020 and remained high, whereas the "physical symptoms of anxiety" cluster showed a slight increase. CONCLUSIONS: This study sheds light on the impact of COVID-19 on mental health and the related themes discussed in 2 web-based communities during the pandemic. The results offer valuable insights for developing targeted interventions and policies to support individuals and communities in similar crises.
背景:由于日常生活的突然变化,如经济压力、社会隔离和教育不规律,COVID-19 大流行导致抑郁、焦虑和其他心理健康问题的水平升高。准确评估对大流行的情绪和行为变化具有挑战性,但了解围绕 COVID-19 对心理健康影响的不断变化的情绪、主题和讨论至关重要。 目的:本研究旨在使用自然语言处理技术和统计方法,了解在 COVID-19 大流行期间,使用 Reddit(Reddit Inc)上的心理健康支持小组(例如 r/Depression 和 r/Anxiety)的情绪和主题的演变。 方法:本研究使用了 r/Depression 和 r/Anxiety Reddit 社区的数据,该数据由 2019 年至 2022 年期间 351,409 位不同用户贡献的帖子组成。主题建模和 Word2Vec 嵌入模型用于识别与数据集内目标主题相关的关键词。使用了一系列趋势和主题分析技术,包括时间事件分析、热图分析、因子分析、回归分析和 k-均值聚类分析来分析数据。 结果:时间事件分析表明,在重大事件发生后的前 28 天可能是心理健康问题更加突出的关键窗口。主题趋势分析揭示了经济压力、社会压力、自杀和药物使用等关键主题,每个社区的趋势和影响都有所不同。因子分析突出了大流行相关压力、经济问题和社会因素是分析期间的主要主题。回归分析表明,经济压力与自杀主题始终具有最强的关联,而药物主题在两个数据集都有显著关联。最后,k-均值聚类分析表明,在 r/Depression 中,与“抑郁、焦虑和药物”集群相关的帖子数量在 2020 年后减少,而“社会关系和友谊”集群则呈稳步下降趋势。在 r/Anxiety 中,“一般焦虑和不安感”集群在 2020 年 4 月达到峰值并保持高位,而“焦虑的身体症状”集群则略有增加。 结论:本研究揭示了 COVID-19 对心理健康的影响以及大流行期间这两个基于网络的社区中讨论的相关主题。研究结果为制定有针对性的干预措施和政策,为类似危机中的个人和社区提供支持提供了有价值的见解。
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