| Literature DB >> 33577082 |
R Drozdowski1, C Gronbeck1, H Feng2.
Abstract
Entities:
Mesh:
Substances:
Year: 2021 PMID: 33577082 PMCID: PMC8013391 DOI: 10.1111/ced.14608
Source DB: PubMed Journal: Clin Exp Dermatol ISSN: 0307-6938 Impact factor: 4.481
Figure 1Overall volume, topic distribution and content of mask‐related acne Tweets in September 2020, stratified by author type. For each author type, the total number of Tweets is denoted at the top of each bar in the figure. The distribution of Tweets is specifically delineated for each author type. Tweets were classified based on common ideas and themes, which are further described in Table S1.
Demographic distribution of patient and nonpatient authors posting Tweets regarding mask‐related acne.
| Patient Tweets, | Nonpatient Tweets, | |
| Total Tweets | 475 (100.0) | 215 (100.0) |
| Demographic attribute | ||
| Gender | ||
| Male | 56 (11.8) | 5 (2.3) |
| Female | 412 (86.7) | 69 (32.4) |
| Genderqueer or nonbinary | 2 (0.4) | 1 (0.5) |
| Not available | 5 (1.1) | 1 (0.5) |
| Not applicable | 0 (0) | 139 (64.4) |
| Race | ||
| White/Hispanic | 313 (65.9) | 30 (13.9) |
| Person of colour | 146 (30.7) | 42 (19.9) |
| Not available | 16 (3.4) | 4 (1.9) |
| Not applicable | 0 (0) | 139 (64.4) |
a Those that were difficult to ascertain using publicly available information;
b pertaining to Twitter accounts of groups or organizations.