  Background: The COVID-19 pandemic has affected our society and human
well-being in various ways. In this study, we investigate how the pandemic has
influenced people's emotions and psychological states compared to a
pre-pandemic period using real-world data from social media.
  Method: We collected Reddit social media data from 2019 (pre-pandemic) and
2020 (pandemic) from the subreddits communities associated with eight
universities. We applied the pre-trained Robustly Optimized BERT pre-training
approach (RoBERTa) to learn text embedding from the Reddit messages, and
leveraged the relational information among posted messages to train a graph
attention network (GAT) for sentiment classification. Finally, we applied model
stacking to combine the prediction probabilities from RoBERTa and GAT to yield
the final classification on sentiment. With the model-predicted sentiment
labels on the collected data, we used a generalized linear mixed-effects model
to estimate the effects of pandemic and in-person teaching during the pandemic
on sentiment.
  Results: The results suggest that the odds of negative sentiments in 2020
(pandemic) were 25.7% higher than the odds in 2019 (pre-pandemic) with a
$p$-value $<0.001$; and the odds of negative sentiments associated in-person
learning were 48.3% higher than with remote learning in 2020 with a $p$-value
of 0.029.
  Conclusions: Our study results are consistent with the findings in the
literature on the negative impacts of the pandemic on people's emotions and
psychological states. Our study contributes to the growing real-world evidence
on the various negative impacts of the pandemic on our society; it also
provides a good example of using both ML techniques and statistical modeling
and inference to make better use of real-world data.
