  Social media discussion of COVID-19 provides a rich source of information
into how the virus affects people's lives that is qualitatively different from
traditional public health datasets. In particular, when individuals self-report
their experiences over the course of the virus on social media, it can allow
for identification of the emotions each stage of symptoms engenders in the
patient. Posts to the Reddit forum r/COVID19Positive contain first-hand
accounts from COVID-19 positive patients, giving insight into personal
struggles with the virus. These posts often feature a temporal structure
indicating the number of days after developing symptoms the text refers to.
Using topic modelling and sentiment analysis, we quantify the change in
discussion of COVID-19 throughout individuals' experiences for the first 14
days since symptom onset. Discourse on early symptoms such as fever, cough, and
sore throat was concentrated towards the beginning of the posts, while language
indicating breathing issues peaked around ten days. Some conversation around
critical cases was also identified and appeared at a roughly constant rate. We
identified two clear clusters of positive and negative emotions associated with
the evolution of these symptoms and mapped their relationships. Our results
provide a perspective on the patient experience of COVID-19 that complements
other medical data streams and can potentially reveal when mental health issues
might appear.

Computation and Language;Social and Information Networks;Applications