  The COVID-19 pandemic has severely affected people's daily lives and caused
tremendous economic loss worldwide. However, its influence on people's mental
health conditions has not received as much attention. To study this subject, we
choose social media as our main data resource and create by far the largest
English Twitter depression dataset containing 2,575 distinct identified
depression users with their past tweets. To examine the effect of depression on
people's Twitter language, we train three transformer-based depression
classification models on the dataset, evaluate their performance with
progressively increased training sizes, and compare the model's "tweet
chunk"-level and user-level performances. Furthermore, inspired by
psychological studies, we create a fusion classifier that combines deep
learning model scores with psychological text features and users' demographic
information and investigate these features' relations to depression signals.
Finally, we demonstrate our model's capability of monitoring both group-level
and population-level depression trends by presenting two of its applications
during the COVID-19 pandemic. We hope this study can raise awareness among
researchers and the general public of COVID-19's impact on people's mental
health.
