Table 3.
Twitter-derived sentiment with CVD outcomes stratified by sex, weighteda
| CVD outcomes | Twitter Sentimentc | Male | Female |
|---|---|---|---|
| Prevalence Ratio (95% CI)b | Prevalence Ratio (95% CI)b | ||
| Hypertension | Positive tweets | ||
| 3rd tertile (highest) | 0.96 (0.92, 0.99) | 0.98 (0.94, 1.01) | |
| 2nd tertile | 0.97 (0.94, 1.00) | 0.98 (0.95, 1.01) | |
| Negative tweets | |||
| 3rd tertile (highest) | 1.10 (1.06, 1.15) | 1.13 (1.08, 1.17) | |
| 2nd tertile | 1.05 (1.02, 1.08) | 1.07 (1.04, 1.11) | |
| N | 188,772 | 238,587 | |
| Diabetes | Positive tweets | ||
| 3rd tertile (highest) | 0.91 (0.84, 0.98) | 0.98 (0.91, 1.05) | |
| 2nd tertile | 0.95 (0.89, 1.01) | 0.94 (0.89, 1.01) | |
| Negative tweets | |||
| 3rd tertile (highest) | 1.17 (1.07, 1.27) | 1.13 (1.04, 1.23) | |
| 2nd tertile | 1.03 (0.97, 1.09) | 1.03 (0.97, 1.10) | |
| N | 188,586 | 238,405 | |
| Obesity | Positive tweets | ||
| 3rd tertile (highest) | 0.97 (0.93, 1.01) | 0.97 (0.93, 1.01) | |
| 2nd tertile | 1.01 (0.97, 1.05) | 0.94 (0.91, 0.98) | |
| Negative tweets | |||
| 3rd tertile (highest) | 1.16 (1.11, 1.22) | 1.12 (1.06, 1.17) | |
| 2nd tertile | 1.07 (1.03, 1.11) | 1.03 (0.99, 1.07) | |
| N | 188,898 | 238,707 | |
| Stroke | Positive tweets | ||
| 3rd tertile (highest) | 0.92 (0.79, 1.07) | 0.86 (0.77, 0.97) | |
| 2nd tertile | 0.89 (0.79, 1.02) | 0.86 (0.75, 0.99) | |
| Negative tweets | |||
| 3rd tertile (highest) | 1.27 (1.08, 1.50) | 1.33 (1.14, 1.55) | |
| 2nd tertile | 1.12 (1.00, 1.26) | 1.13 (1.01, 1.27) | |
| N | 188,853 | 238,677 | |
| MI | Positive tweets | ||
| 3rd tertile (highest) | 0.89 (0.80, 0.99) | 0.92 (0.81, 1.06) | |
| 2nd tertile | 0.93 (0.84, 1.02) | 0.93 (0.82, 1.05) | |
| Negative tweets | |||
| 3rd tertile (highest) | 1.14 (1.01, 1.28) | 1.14 (0.98, 1.33) | |
| 2nd tertile | 1.06 (0.97, 1.16) | 1.10 (0.99, 1.23) | |
| N | 188,850 | 238,672 | |
| CHD | Positive tweets | ||
| 3rd tertile (highest) | 0.95 (0.85, 1.05) | 0.94 (0.82, 1.07) | |
| 2nd tertile | 0.93 (0.84, 1.04) | 1.02 (0.91, 1.15) | |
| Negative tweets | |||
| 3rd tertile (highest) | 1.04 (0.93, 1.17) | 1.16 (1.00, 1.34) | |
| 2nd tertile | 1.03 (0.94, 1.12) | 1.11 (1.00, 1.24) | |
| N | 188,819 | 238,646 | |
| Any CVD | Positive tweets | ||
| 3rd tertile (highest) | 0.90 (0.83, 0.97) | 0.90 (0.82, 0.98) | |
| 2nd tertile | 0.89 (0.84, 0.96) | 0.91 (0.85, 0.99) | |
| Negative tweets | |||
| 3rd tertile (highest) | 1.13 (1.04, 1.23) | 1.20 (1.09, 1.32) | |
| 2nd tertile | 1.06 (1.00, 1.12) | 1.10 (1.02, 1.18) | |
| N | 188,898 | 238,707 | |
Data source for health outcome: BRFSS 2017
Adjusted Poisson regression models were run for each outcome separately. Models controlled for individual level age, race, marriage as well as state level percent of non-Hispanic white, percent of non-Hispanic black, percent of Hispanic, and median household income. Twitter-derived sentiment were categorized into tertiles, with the lowest tertile serving as the referent group. Analyses accounted for survey weights and complex survey design to produce nationally representative estimates.
Twitter sentiment including % positive tweets and % negative tweets were constructed at state level with all the race-related tweets. We categorized % positive tweets and % negative tweets into tertiles with the lowest levels as the references.