Name
Abstract | ||
|---|---|---|
Since the beginning of the COVID-19 pandemic, patients shared their personal experiences of the viral infection on social media. Gathering their symptomatic experiences reported on Twitter may help better understand the infectious disease and supplement our knowledge of the disease gathered by healthcare workers. In this study, we identified personal experience tweets related to COVID-19 infection using a pre-trained and fine-tuned language model, and annotated the machine-identified tweets in order to extract the information of infection status, symptom concepts, and the days the symptomatic experience occurred. Our result shows that the top 10 most common symptoms mentioned in the collected Twitter data are in line with those published by WHO and CDC. The symptoms along with the day information appear to provide additional insight on how the infection progresses in infected individuals. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.3233/SHTI220552 | Medical Informatics Europe (MIE) |
Keywords | DocType | Volume |
COVID-19 symptoms,Novel coronavirus,Transformer-based language model,Twitter data,personal health experience | Conference | 294 |
ISSN | Citations | PageRank |
1879-8365 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Keyuan Jiang | 1 | 0 | 0.34 |
| Minghao Zhu | 2 | 0 | 0.34 |
| Gordon R Bernard | 3 | 0 | 0.34 |