ArXiv Preprint
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 identify a
group of Twitter users who self-disclose to belong to the LGBTQ community. 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. For this task, we collect
a set of 20,593,823 posts by 7,241 self-disclosed LGBTQ users and annotate a
randomly selected subset of 2800 posts. We demonstrate that our best
pre-pandemic 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 conducting controlled comparisons with the
general population. We adopt propensity score-based matching to perform a
causal analysis. The results show that the LBGTQ 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.
Yunhao Yuan, Gaurav Verma, Barbara Keller, Talayeh Aledavood
2022-05-19
