Figure Correction: Using Social Media to Help Understand Patient-Reported Health Outcomes of Post-COVID-19 Condition: Natural Language Processing Approach

Abstract

[This corrects the article DOI: 10.2196/45767.].

Figure 2

The occurrence frequency of the most prevailing extracted symptom and condition terms in Twitter and Reddit data with occurrence frequency greater than 1% (n>350 for Twitter, and n>4000 for Reddit). Normalized terms are the raw terms that were normalized (after a 2-step normalization process, as shown in Figure 1) to the 203 standardized unique concepts derived from a web-based survey of 3762 patients with post–COVID-19 condition [3]. For instance, “my tiredness” is normalized into “fatigue.” Grouped terms are the normalized terms that were further categorized based on the affected organ system established by Davis et al [3]. Novel terms are the mapped terms that we had not normalized to the 203 standardized unique concepts because they were neither reported nor categorized in the survey study [3]. HEENT: head, eyes, ears, nose, and throat.

Figure 3

Co-occurrence frequency of normalized post–COVID-19 condition terms in Twitter (A) which is higher than 50% and Reddit (B) which is higher than 10% data. Higher values are shown by the intensity of pink and blue shading. Normalized terms are the raw terms that were normalized (after a 2-step normalization process, as shown in Figure 1) to the 203 standardized unique concepts derived from a web-based survey of 3762 patients with post–COVID-19 condition [3]. For instance, “my tiredness” is normalized into “fatigue”. Please see Multimedia Appendix 2 for a larger version.