Table 4.
Latent Dirichlet allocation topic modeling for all comments, showing comment distribution, Facebook metrics, and sentiments from Linguistic Inquiry and Word Count (LIWC; N=51,835).
| Topic number | Topic name | Facebook metricsa—comments, n (%) | Sentiments from LIWCb | ||
|
|
|
|
Word count, mean (SD) | Positive emotion percentage, mean (SD) | Negative emotion percentage, mean (SD) |
| 1 | Sharing personal experiences | 14,000 (27) | 33.72 (46.15) | 4.29 (11.34) | 3.41 (7.40) |
| 2 | Survivor stories | 7026 (13.6) | 18.95 (22.61) | 5.93 (13.98) | 2.18 (4.49) |
| 3 | Risk-reduction discussion | 7080 (13.7) | 16.39 (25.26) | 4.94 (10.93) | 3.39 (6.24) |
| 4 | Religious content | 11,254 (21.7) | 10.03 (13.90) | 23.44 (22.05) | 0.91 (5.12) |
| 5 | Asking medical questions | 5964 (11.5) | 9.33 (9.21) | 4.87 (15.11) | 6.93 (9.37) |
| 6 | Sharing appreciation and information | 6511 (12.6) | 5.71 (10.08) | 23.87 (26.99) | 0.89 (4.11) |
aData collected in November 2021.
bPositive and negative emotions represent the percentage of words in a post that appear in the dictionary indicating positive and negative emotions.