Workshop Proceedings of the 15th International AAAI Conference on Web and Social Media
Workshop: Mining Actionable Insights from Social Networks: Special Edition on Healthcare Social AnalyticsDOI: 10.36190/2021.71
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. 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. This paper aims to quantify the change in discourse throughout a collective timeline of experiences being COVID-19 positive. We collected discourse in the form of Reddit posts from /r/COVID19Positive (4,610 posts from March 14th, 2020 to May 12th, 2020), and filter to capture stories from people who tested positive for COVID-19. We exploit the temporal structure exhibited in these diarised posts to obtain a collective timeline of experiences. 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 timeline, while language indicating breathing issues peaked around ten days after developing symptoms. 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.