Objective To mine Twitter to quantitatively analyze COVID-19 symptoms self-reported by users, compare symptom distributions against clinical studies, and create a symptom lexicon for the research community. Materials and methods We retrieved tweets using COVID-19-related keywords, and performed several layers of semi-automatic filtering to curate self-reports of positive-tested users. We extracted COVID-19-related symptoms mentioned by the users, mapped them to standard IDs, and compared the distributions with multiple studies conducted in clinical settings. Results We identified 203 positive-tested users who reported 932 symptoms using 598 unique expressions. The most frequently-reported symptoms were fever/pyrexia (65%), cough (56%), body aches/pain (40%), headache (35%), fatigue (35%), and dyspnea (34%) amongst users who reported at least 1 symptom. Mild symptoms, such as anosmia (26%) and ageusia (24%) were frequently reported on Twitter, but not in clinical studies. Conclusion The spectrum of COVID-19 symptoms identified from Twitter may complement those identified in clinical settings.