Table 4.
Predictors of Tweeting Ch-word among first-time Ch-word users after the first local COVID-19 diagnosis.
| (1) | (2) | |
|---|---|---|
| VARIABLES | P(ch-word) (t+1) | P(ch-word) (t+1) |
| Anti-Asian user(t) | 0.281*** | 0.259*** |
| (0.072) | (0.072) | |
| Anti-minority(t) | 1.156 | 1.112 |
| (1.360) | (1.368) | |
| COVID consp.(t) | 1.314 | 1.177 |
| (1.861) | (1.845) | |
| Trump(t) | 0.325*** | 0.297** |
| (0.122) | (0.122) | |
| McCarthy(t) | 0.184 | 0.097 |
| (0.456) | (0.456) | |
| McConnell(t) | 1.969*** | 2.045*** |
| (0.351) | (0.449) | |
| Pelosi(t) | 0.373 | 0.419 |
| (0.284) | (0.285) | |
| Schumer(t) | 0.031 | 0.106 |
| (0.379) | (0.379) | |
| CBS(t) | 0.636 | 0.692 |
| (0.824) | (0.825) | |
| CNN(t) | 0.164 | 0.173 |
| (0.277) | (0.277) | |
| Fox(t) | 0.430 | 0.386 |
| (0.348) | (0.346) | |
| Account years | 0.002 | 0.002 |
| (0.005) | (0.005) | |
| Log(followers) | 0.032** | 0.031** |
| (0.013) | (0.013) | |
| Log(followings) | 0.010 | 0.009 |
| (0.020) | (0.020) | |
| New diagnoses | 0.000 | |
| (0.000) | ||
| New deaths | 0.000 | |
| (0.000) | ||
| Observations | 174,164 | 174,164 |
| R-squared | 0.002 | 0.004 |
| Outcome mean | 1.251 | 1.251 |
Notes: This table presents the relationship between first-time ch-word users’ likelihood of tweeting the ch-word (in percentage point) and their Twitter activity in the day before, as well as their baseline characteristics. See note to Fig. 4 for definitions of the independent variables. The data is at the userday level, and all regressions control for county, year-of-week, and day-of-week fixed effects. Standard errors are clustered by user. *** 0.01, ** 0.05, * 0.1.