Table 2.
Tweet counts from network data after manual sentiment annotation.
| mHealth App | Tweet Sentiment | Total (%) | ||||
|---|---|---|---|---|---|---|
| Positive (%) | Neutral (%) | Negative (%) | Indeterminate (%) | Sarcasm (%) | ||
| Sehha | 7 (16.7%) | 28 (66.7%) | 3 (7.1%) | 1 (2.4%) | 3 (7.1%) | 42 (0.8%) |
| Mawid | 8 (6.3%) | 89 (69.5%) | 22 (17.2%) | 5 (3.9%) | 4 (3.1%) | 128 (2.5%) |
| Sehhaty | 8 (6.3%) | 98 (77.2%) | 13 (10.2%) | 5 (3.9%) | 3 (2.4%) | 127 (2.5%) |
| Tetamman | 164 (35.9%) | 257 (56.2%) | 23 (5.0%) | 3 (0.7%) | 10 (2.2%) | 457 (9.1%) |
| Tawakkalna | 49 (1.4%) | 1292 (37.7%) | 143 (4.2%) | 1920 (56.0%) | 22 (0.6%) | 3426 (67.9%) |
| Tabaud | 9 (1.0%) | 657 (75.7%) | 2 (0.2%) | 189 (21.8%) | 11 (1.3%) | 868 (17.2%) |
| Total | 245 (4.9%) | 2421 (48.0%) | 206 (4.1%) | 2123 (42.1%) | 53 (1.0%) | 5048 (100%) |