COVID-19疫情期间LGBTQ在线社区经历的少数群体压力
Minority Stress Experienced by LGBTQ Online Communities during the COVID-19 Pandemic
AI总结：
本研究利用疫情前后Twitter数据训练分类器识别LGBTQ人群的少数群体压力，发现愤怒词汇关联增强，并通过因果分析揭示其认知词汇增加、积极情绪恶化，为公共卫生支持提供依据。
摘要：
COVID-19疫情对少数群体（如LGBTQ社区成员，即女同性恋、男同性恋、双性恋、跨性别者和酷儿）的生活造成了不成比例的影响，这源于他们先前存在的社会劣势和健康差距。尽管已有大量研究关注COVID-19疫情对普通人群生活不同方面的影响，但针对LGBTQ人群的研究却很少。在本文中，我们利用疫情前和疫情期间的两个数据集，开发并评估了两组机器学习分类器，以识别表现出少数群体压力的Twitter帖子，这种压力是LGBTQ人群因其性别和性身份而面临的独特压力。我们证明，我们最佳的疫情前和疫情期间模型在检测包含少数群体压力的帖子方面表现出强大且稳定的性能。我们调查了疫情前和疫情期间少数群体压力帖子的语言差异。我们发现，愤怒词汇与COVID-19疫情期间的少数群体压力密切相关。我们通过采用基于倾向评分的匹配进行因果分析，探讨了疫情对LGBTQ人群情绪状态的影响。结果表明，与具有相似疫情前行为特征的普通人群组相比，LGBTQ人群在认知词汇的使用上增加更多，而在积极情绪词汇的使用上可观察属性有所恶化。我们的发现对公共卫生领域和政策制定者具有重要意义，以便在未来危机期间为LGBTQ人群提供充分支持，特别是在心理健康方面。
英文摘要：
The COVID-19 pandemic has disproportionately impacted the lives of minorities, such as members of the LGBTQ community (lesbian, gay, bisexual, transgender, and queer) due to pre-existing social disadvantages and health disparities. Although extensive research has been carried out on the impact of the COVID-19 pandemic on different aspects of the general population's lives, few studies are focused on the LGBTQ population. In this paper, we develop and evaluate two sets of machine learning classifiers using a pre-pandemic and a during-pandemic dataset to identify Twitter posts exhibiting minority stress, which is a unique pressure faced by the members of the LGBTQ population due to their sexual and gender identities. We demonstrate that our best pre- and during-pandemic models show strong and stable performance for detecting posts that contain minority stress. We investigate the linguistic differences in minority stress posts across pre- and during-pandemic periods. We find that anger words are strongly associated with minority stress during the COVID-19 pandemic. We explore the impact of the pandemic on the emotional states of the LGBTQ population by adopting propensity score-based matching to perform a causal analysis. The results show that the LGBTQ population have a greater increase in the usage of cognitive words and worsened observable attribute in the usage of positive emotion words than the group of the general population with similar pre-pandemic behavioral attributes. Our findings have implications for the public health domain and policy-makers to provide adequate support, especially with respect to mental health, to the LGBTQ population during future crises.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
