Covid Media Manuscript Analyses for Study 1 (US vs. UK)

Key points: (i) Overall effect, (ii) Cross country generalization, not limited to Fox

0. import data sets

~~ DESCRIPTIVE STATS ~~

1. demographics

a. sample size

table(!is.na(d1$participant))

## 
## TRUE 
## 4836

table(!is.na(d1$participant[d1$country_factor == "US"]))

## 
## TRUE 
## 3316

table(!is.na(d1$participant[d1$country_factor == "UK"]))

## 
## TRUE 
## 1520

b. age

describe(d1$age)

describe(d1$age[d1$country_factor == "US"])

describe(d1$age[d1$country_factor == "UK"])

c. gender

(t <- table(d1$gender_factor))

## 
## custom female   male 
##     16   2480   2053

round(prop.table(t), 3)

## 
## custom female   male 
##  0.004  0.545  0.451

(t <- table(d1$gender_factor[d1$country_factor == "US"]))

## 
## custom female   male 
##      6   1564   1477

round(prop.table(t), 3)

## 
## custom female   male 
##  0.002  0.513  0.485

(t <- table(d1$gender_factor[d1$country_factor == "UK"]))

## 
## custom female   male 
##     10    916    576

round(prop.table(t), 3)

## 
## custom female   male 
##  0.007  0.610  0.383

d. party identity

(t <- table(d1$party_factor))

## 
##    Democrat Independent  Republican 
##        2268         916        1470

round(prop.table(t), 3)

## 
##    Democrat Independent  Republican 
##       0.487       0.197       0.316

(t <- table(d1$party_factor[d1$country_factor == "US"]))

## 
##    Democrat Independent  Republican 
##        1402         627        1118

round(prop.table(t), 3)

## 
##    Democrat Independent  Republican 
##       0.446       0.199       0.355

(t <- table(d1$party_factor[d1$country_factor == "UK"]))

## 
##    Democrat Independent  Republican 
##         866         289         352

round(prop.table(t), 3)

## 
##    Democrat Independent  Republican 
##       0.575       0.192       0.234

e. education

describe(d1$education) 

describe(d1$education[d1$country_factor == "US"]) 

describe(d1$education[d1$country_factor == "UK"]) 

2. variables of interest

a. media exposure

Not included here, media exposure asks participants to “Consider each of the media sources below. In general, how much do you get news about Covid-19 from each source?” on a scale from 1 (Not at all) to 3 (Somewhat) to 5 (A great deal).

b. analytical media index

The first step to creating the analytical media index is multiplying individual analytic thinking scores for each media outlet by participant rated exposure to that outlet, Then taking the proportion of each of these products (i.e., dividing by the 12 possible US outlets to be exposed to, or 8 outlets for the UK)

foxAnalyticalIndex = (foxNewsExposure x foxAnalyticalScore) (foxAnalyticalIndex + cnnAnalyticalIndex + msnbcAnalyticalIndex + …) / 12 total outlets

describe(d1$index_ANexp)

describe(d1$index_ANexp[d1$country_factor == "US"])

describe(d1$index_ANexp[d1$country_factor == "UK"])

c. symbolic ideology

symbolic ideology is an average of three items. They ask “How liberal/conservative…” (1) in general, (2) on social issues, and (3) on economic issues. Participants answered on a scale from -3 (Very liberal) to 0 (Moderate) to +3 (Very conservative).

psych::alpha (data.frame(
  d1$symbolic_beliefs_1[d1$country_factor == "US"], 
  d1$symbolic_beliefs_2[d1$country_factor == "US"], 
  d1$symbolic_beliefs_3[d1$country_factor == "US"]), cumulative = F, na.rm = T, delete = T) #alpha = .94 

## 
## Reliability analysis   
## Call: psych::alpha(x = data.frame(d1$symbolic_beliefs_1[d1$country_factor == 
##     "US"], d1$symbolic_beliefs_2[d1$country_factor == "US"], 
##     d1$symbolic_beliefs_3[d1$country_factor == "US"]), cumulative = F, 
##     na.rm = T, delete = T)
## 
##   raw_alpha std.alpha G6(smc) average_r S/N    ase  mean  sd median_r
##       0.94      0.94    0.92      0.85  17 0.0017 0.089 1.6     0.86
## 
##  lower alpha upper     95% confidence boundaries
## 0.94 0.94 0.95 
## 
##  Reliability if an item is dropped:
##                                                  raw_alpha std.alpha G6(smc)
## d1.symbolic_beliefs_1.d1.country_factor.....US..      0.89      0.89    0.80
## d1.symbolic_beliefs_2.d1.country_factor.....US..      0.92      0.92    0.86
## d1.symbolic_beliefs_3.d1.country_factor.....US..      0.94      0.94    0.89
##                                                  average_r S/N alpha se var.r
## d1.symbolic_beliefs_1.d1.country_factor.....US..      0.80   8   0.0039    NA
## d1.symbolic_beliefs_2.d1.country_factor.....US..      0.86  12   0.0027    NA
## d1.symbolic_beliefs_3.d1.country_factor.....US..      0.89  16   0.0020    NA
##                                                  med.r
## d1.symbolic_beliefs_1.d1.country_factor.....US..  0.80
## d1.symbolic_beliefs_2.d1.country_factor.....US..  0.86
## d1.symbolic_beliefs_3.d1.country_factor.....US..  0.89
## 
##  Item statistics 
##                                                     n raw.r std.r r.cor r.drop
## d1.symbolic_beliefs_1.d1.country_factor.....US.. 3162  0.97  0.97  0.95   0.92
## d1.symbolic_beliefs_2.d1.country_factor.....US.. 3163  0.95  0.95  0.91   0.88
## d1.symbolic_beliefs_3.d1.country_factor.....US.. 3163  0.93  0.93  0.88   0.85
##                                                    mean  sd
## d1.symbolic_beliefs_1.d1.country_factor.....US..  0.060 1.7
## d1.symbolic_beliefs_2.d1.country_factor.....US.. -0.067 1.7
## d1.symbolic_beliefs_3.d1.country_factor.....US..  0.272 1.7
## 
## Non missing response frequency for each item
##                                                    -3   -2   -1    0    1    2
## d1.symbolic_beliefs_1.d1.country_factor.....US.. 0.08 0.13 0.09 0.38 0.09 0.13
## d1.symbolic_beliefs_2.d1.country_factor.....US.. 0.10 0.14 0.12 0.33 0.10 0.13
## d1.symbolic_beliefs_3.d1.country_factor.....US.. 0.06 0.11 0.09 0.35 0.13 0.16
##                                                     3 miss
## d1.symbolic_beliefs_1.d1.country_factor.....US.. 0.09 0.05
## d1.symbolic_beliefs_2.d1.country_factor.....US.. 0.09 0.05
## d1.symbolic_beliefs_3.d1.country_factor.....US.. 0.11 0.05

psych::alpha (data.frame(
  d1$symbolic_beliefs_1[d1$country_factor == "UK"], 
  d1$symbolic_beliefs_2[d1$country_factor == "UK"], 
  d1$symbolic_beliefs_3[d1$country_factor == "UK"]), cumulative = F, na.rm = T, delete = T)  

## 
## Reliability analysis   
## Call: psych::alpha(x = data.frame(d1$symbolic_beliefs_1[d1$country_factor == 
##     "UK"], d1$symbolic_beliefs_2[d1$country_factor == "UK"], 
##     d1$symbolic_beliefs_3[d1$country_factor == "UK"]), cumulative = F, 
##     na.rm = T, delete = T)
## 
##   raw_alpha std.alpha G6(smc) average_r S/N    ase  mean  sd median_r
##       0.92      0.92     0.9      0.79  12 0.0037 -0.61 1.4     0.81
## 
##  lower alpha upper     95% confidence boundaries
## 0.91 0.92 0.93 
## 
##  Reliability if an item is dropped:
##                                                  raw_alpha std.alpha G6(smc)
## d1.symbolic_beliefs_1.d1.country_factor.....UK..      0.83      0.83    0.72
## d1.symbolic_beliefs_2.d1.country_factor.....UK..      0.90      0.90    0.81
## d1.symbolic_beliefs_3.d1.country_factor.....UK..      0.92      0.92    0.85
##                                                  average_r  S/N alpha se var.r
## d1.symbolic_beliefs_1.d1.country_factor.....UK..      0.72  5.0   0.0085    NA
## d1.symbolic_beliefs_2.d1.country_factor.....UK..      0.81  8.7   0.0053    NA
## d1.symbolic_beliefs_3.d1.country_factor.....UK..      0.85 11.6   0.0041    NA
##                                                  med.r
## d1.symbolic_beliefs_1.d1.country_factor.....UK..  0.72
## d1.symbolic_beliefs_2.d1.country_factor.....UK..  0.81
## d1.symbolic_beliefs_3.d1.country_factor.....UK..  0.85
## 
##  Item statistics 
##                                                     n raw.r std.r r.cor r.drop
## d1.symbolic_beliefs_1.d1.country_factor.....UK.. 1509  0.96  0.96  0.94   0.90
## d1.symbolic_beliefs_2.d1.country_factor.....UK.. 1509  0.92  0.92  0.87   0.82
## d1.symbolic_beliefs_3.d1.country_factor.....UK.. 1509  0.91  0.91  0.83   0.79
##                                                   mean  sd
## d1.symbolic_beliefs_1.d1.country_factor.....UK.. -0.64 1.4
## d1.symbolic_beliefs_2.d1.country_factor.....UK.. -0.84 1.5
## d1.symbolic_beliefs_3.d1.country_factor.....UK.. -0.35 1.5
## 
## Non missing response frequency for each item
##                                                    -3   -2   -1    0    1    2
## d1.symbolic_beliefs_1.d1.country_factor.....UK.. 0.09 0.24 0.19 0.27 0.12 0.07
## d1.symbolic_beliefs_2.d1.country_factor.....UK.. 0.13 0.25 0.20 0.24 0.10 0.06
## d1.symbolic_beliefs_3.d1.country_factor.....UK.. 0.07 0.18 0.19 0.29 0.16 0.09
##                                                     3 miss
## d1.symbolic_beliefs_1.d1.country_factor.....UK.. 0.01 0.01
## d1.symbolic_beliefs_2.d1.country_factor.....UK.. 0.01 0.01
## d1.symbolic_beliefs_3.d1.country_factor.....UK.. 0.02 0.01

describe(d1$ideology)

describe(d1$ideology[d1$country_factor == "US"])

describe(d1$ideology[d1$country_factor == "UK"])

d. trust in experts

trust in experts is an average of 3-items asking about how much participants trust experts, medicine, economics, public health, and science. The trust response scale ranges from -3 (strongly distrust) to 0 (Neither trust nor distrust) to (Strongly trust)

describe(d1$trustExpert)

describe(d1$trustExpert[d1$country_factor == "US"], na.rm = T)

describe(d1$trustExpert[d1$country_factor == "UK"], na.rm = T)

3. LIWC media measures

a. Analytical thinking scales

Media outlet LIWC analytic thinking scores captures the degree to which people use words that suggest formal, logical, and hierarchical thinking patterns. Column two is raw scores, and column three is standardized scores.

i. US

iii. UK

iv. pairwise correlation for LIWC variables

- US

- UK

- both countries

4. outcome measures

Participants were asked “would you get a Covid-19 vaccine?” and answered on a scale from -3 (Definitely would not get it) to 0 (Undecided) to +3 (Definitely would get it).

describe(d1$vaxxIntentions)

describe(d1$vaxxIntentions[d1$country_factor == "US"])

describe(d1$vaxxIntentions[d1$country_factor == "UK"])

5. pairwise correlation table for IVs, DVs, and covariates

a. US

b. UK

~~ ANALYSES ~~

6. Analyses for Study 1 US + UK (July-August November)

a. More favorable vaccine intention among older adults, more educated, and more liberal, difference between US and UK

vaxxIntentions ~ USvUK * (ideology.z + age.z + education.z)

	vaxxIntentions
Predictors	Est	SE	CI	t	p	df
(Intercept)	0.941	0.037	0.869 – 1.013	25.747	<0.001	4461.000
USvUK	0.749	0.073	0.606 – 0.893	10.251	<0.001	4461.000
ideology.z	-0.375	0.035	-0.444 – -0.306	-10.634	<0.001	4461.000
age.z	0.220	0.037	0.147 – 0.293	5.913	<0.001	4461.000
education.z	0.082	0.035	0.013 – 0.152	2.324	0.020	4461.000
USvUK * ideology.z	0.143	0.070	0.005 – 0.281	2.028	0.043	4461.000
USvUK * age.z	-0.346	0.074	-0.492 – -0.200	-4.650	<0.001	4461.000
USvUK * education.z	-0.196	0.071	-0.335 – -0.057	-2.770	0.006	4461.000
Observations	4469
R2 / R2 adjusted	0.094 / 0.093

## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

i. country simple slopes

	vaxxIntentions						vaxxIntentions
Predictors	Est	SE	CI	t	p	df	Est	SE	CI	t	p	df
(Intercept)	0.566	0.038	0.492 – 0.641	14.915	<0.001	4461.000	1.315	0.062	1.193 – 1.438	21.065	<0.001	4461.000
US_0	0.749	0.073	0.606 – 0.893	10.251	<0.001	4461.000
ideology.z	-0.446	0.036	-0.516 – -0.377	-12.553	<0.001	4461.000	-0.303	0.061	-0.423 – -0.184	-4.983	<0.001	4461.000
age.z	0.393	0.038	0.318 – 0.468	10.278	<0.001	4461.000	0.047	0.064	-0.078 – 0.172	0.736	0.462	4461.000
education.z	0.181	0.035	0.112 – 0.250	5.133	<0.001	4461.000	-0.016	0.062	-0.137 – 0.105	-0.257	0.797	4461.000
US_0 * ideology.z	0.143	0.070	0.005 – 0.281	2.028	0.043	4461.000
US_0 * age.z	-0.346	0.074	-0.492 – -0.200	-4.650	<0.001	4461.000
US_0 * education.z	-0.196	0.071	-0.335 – -0.057	-2.770	0.006	4461.000
UK_0							-0.749	0.073	-0.893 – -0.606	-10.251	<0.001	4461.000
UK_0 * ideology.z							-0.143	0.070	-0.281 – -0.005	-2.028	0.043	4461.000
UK_0 * age.z							0.346	0.074	0.200 – 0.492	4.650	<0.001	4461.000
UK_0 * education.z							0.196	0.071	0.057 – 0.335	2.770	0.006	4461.000
Observations	4469						4469
R2 / R2 adjusted	0.094 / 0.093						0.094 / 0.093

b. analytical media interacts with demographics

vaxxIntentions ~ analytical index * (ideology + USvUK + age + education)

	vaxxIntentions
Predictors	Est	SE	CI	t	p	df
(Intercept)	1.013	0.032	0.950 – 1.076	31.732	<0.001	4459.000
index_ANexp.z	0.308	0.035	0.239 – 0.376	8.818	<0.001	4459.000
ideology.z	-0.361	0.031	-0.421 – -0.300	-11.664	<0.001	4459.000
USvUK	0.888	0.069	0.752 – 1.023	12.835	<0.001	4459.000
age.z	0.343	0.032	0.280 – 0.407	10.624	<0.001	4459.000
education.z	0.160	0.031	0.101 – 0.220	5.253	<0.001	4459.000
index_ANexp.z * ideology.z	0.132	0.029	0.076 – 0.188	4.625	<0.001	4459.000
index_ANexp.z * USvUK	-0.156	0.073	-0.299 – -0.014	-2.152	0.031	4459.000
index_ANexp.z * age.z	-0.059	0.034	-0.126 – 0.008	-1.729	0.084	4459.000
index_ANexp.z * education.z	-0.063	0.026	-0.114 – -0.012	-2.404	0.016	4459.000
Observations	4469
R2 / R2 adjusted	0.123 / 0.122

## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.

## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

i. country simple slopes

	vaxxIntentions						vaxxIntentions
Predictors	Est	SE	CI	t	p	df	Est	SE	CI	t	p	df
(Intercept)	0.569	0.038	0.496 – 0.643	15.162	<0.001	4459.000	1.457	0.055	1.349 – 1.564	26.508	<0.001	4459.000
index_ANexp.z	0.386	0.035	0.316 – 0.455	10.879	<0.001	4459.000	0.229	0.062	0.108 – 0.351	3.714	<0.001	4459.000
ideology.z	-0.361	0.031	-0.421 – -0.300	-11.664	<0.001	4459.000	-0.361	0.031	-0.421 – -0.300	-11.664	<0.001	4459.000
US_0	0.888	0.069	0.752 – 1.023	12.835	<0.001	4459.000
age.z	0.343	0.032	0.280 – 0.407	10.624	<0.001	4459.000	0.343	0.032	0.280 – 0.407	10.624	<0.001	4459.000
education.z	0.160	0.031	0.101 – 0.220	5.253	<0.001	4459.000	0.160	0.031	0.101 – 0.220	5.253	<0.001	4459.000
index_ANexp.z * ideology.z	0.132	0.029	0.076 – 0.188	4.625	<0.001	4459.000	0.132	0.029	0.076 – 0.188	4.625	<0.001	4459.000
index_ANexp.z * US_0	-0.156	0.073	-0.299 – -0.014	-2.152	0.031	4459.000
index_ANexp.z * age.z	-0.059	0.034	-0.126 – 0.008	-1.729	0.084	4459.000	-0.059	0.034	-0.126 – 0.008	-1.729	0.084	4459.000
index_ANexp.z * education.z	-0.063	0.026	-0.114 – -0.012	-2.404	0.016	4459.000	-0.063	0.026	-0.114 – -0.012	-2.404	0.016	4459.000
UK_0							-0.888	0.069	-1.023 – -0.752	-12.835	<0.001	4459.000
index_ANexp.z * UK_0							0.156	0.073	0.014 – 0.299	2.152	0.031	4459.000
Observations	4469						4469
R2 / R2 adjusted	0.123 / 0.122						0.123 / 0.122

c. analytical index * ideology + country

vaxxIntentions ~ analytic index * ideology + USvUK + age + education

	vaxxIntentions
Predictors	Est	SE	CI	t	p	df
(Intercept)	1.025	0.032	0.963 – 1.087	32.462	<0.001	4462.000
index_ANexp.z	0.356	0.030	0.297 – 0.414	11.944	<0.001	4462.000
ideology.z	-0.359	0.031	-0.419 – -0.299	-11.724	<0.001	4462.000
USvUK	0.902	0.069	0.767 – 1.038	13.073	<0.001	4462.000
age.z	0.346	0.032	0.282 – 0.409	10.693	<0.001	4462.000
education.z	0.142	0.030	0.084 – 0.201	4.748	<0.001	4462.000
index_ANexp.z * ideology.z	0.135	0.028	0.080 – 0.191	4.788	<0.001	4462.000
Observations	4469
R2 / R2 adjusted	0.121 / 0.120

## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

d. mediation path: analytical index -> expert trust -> vaccine intentions

i. interaction between analytic index and ideology

Model 1: vaxxIntentions ~ analytic.index.z * ideology.z + USvsUK + age.z + edu.z

Model 2: expertTrust ~ analytic.index.z * ideology.z + USvsUK + age.z + edu.z

Model 3: vaxxIntentions ~ ideology.z * (analytic.index.z + expertTrust.z) + USvsUK + age.z + edu.z

	vaxxIntentions						trustExpert						vaxxIntentions
Predictors	Est	SE	CI	t	p	df	Est	SE	CI	t	p	df	Est	SE	CI	t	p	df
(Intercept)	1.025	0.032	0.963 – 1.087	32.462	<0.001	4462.000	1.705	0.022	1.662 – 1.748	77.411	<0.001	4462.000	0.945	0.030	0.886 – 1.004	31.399	<0.001	4460.000
index_ANexp.z	0.356	0.030	0.297 – 0.414	11.944	<0.001	4462.000	0.191	0.021	0.151 – 0.232	9.208	<0.001	4462.000	0.256	0.028	0.201 – 0.311	9.135	<0.001	4460.000
ideology.z	-0.359	0.031	-0.419 – -0.299	-11.724	<0.001	4462.000	-0.313	0.021	-0.355 – -0.271	-14.668	<0.001	4462.000	-0.192	0.029	-0.250 – -0.135	-6.589	<0.001	4460.000
USvUK	0.902	0.069	0.767 – 1.038	13.073	<0.001	4462.000	0.564	0.048	0.469 – 0.658	11.710	<0.001	4462.000	0.605	0.065	0.478 – 0.733	9.288	<0.001	4460.000
age.z	0.346	0.032	0.282 – 0.409	10.693	<0.001	4462.000	0.153	0.023	0.109 – 0.197	6.798	<0.001	4462.000	0.265	0.030	0.206 – 0.324	8.771	<0.001	4460.000
education.z	0.142	0.030	0.084 – 0.201	4.748	<0.001	4462.000	0.141	0.021	0.100 – 0.182	6.749	<0.001	4462.000	0.067	0.028	0.012 – 0.122	2.395	0.017	4460.000
index_ANexp.z * ideology.z	0.135	0.028	0.080 – 0.191	4.788	<0.001	4462.000	0.096	0.020	0.058 – 0.135	4.895	<0.001	4462.000	0.088	0.027	0.035 – 0.141	3.260	0.001	4460.000
trustExpert.z													0.780	0.030	0.722 – 0.838	26.354	<0.001	4460.000
trustExpert.z * ideology.z													-0.016	0.027	-0.069 – 0.036	-0.609	0.542	4460.000
Observations	4469						4469						4469
R2 / R2 adjusted	0.121 / 0.120						0.126 / 0.125						0.240 / 0.238

ii. interaction between analytic index, ideology, and country

Model 1: vaxxIntentions ~ analytic.index.z * ideology.z * USvsUK + age.z + edu.z

Model 2: expertTrust ~ analytic.index.z * ideology.z * USvsUK + age.z + edu.z

Model 3: vaxxIntentions ~ ideology.z * (analytic.index.z + expertTrust.z) * USvsUK + age.z + edu.z

	vaxxIntentions						trustExpert						vaxxIntentions
Predictors	Est	SE	CI	t	p	df	Est	SE	CI	t	p	df	Est	SE	CI	t	p	df
(Intercept)	1.020	0.033	0.955 – 1.085	30.841	<0.001	4459.000	1.704	0.023	1.659 – 1.749	73.979	<0.001	4459.000	0.918	0.032	0.856 – 0.981	28.858	<0.001	4455.000
index_ANexp.z	0.326	0.034	0.260 – 0.393	9.605	<0.001	4459.000	0.151	0.024	0.105 – 0.197	6.374	<0.001	4459.000	0.248	0.032	0.185 – 0.310	7.795	<0.001	4455.000
ideology.z	-0.351	0.035	-0.419 – -0.283	-10.137	<0.001	4459.000	-0.293	0.024	-0.340 – -0.246	-12.146	<0.001	4459.000	-0.198	0.035	-0.267 – -0.129	-5.605	<0.001	4455.000
USvUK	0.896	0.070	0.759 – 1.033	12.802	<0.001	4459.000	0.562	0.049	0.466 – 0.658	11.521	<0.001	4459.000	0.568	0.067	0.437 – 0.700	8.461	<0.001	4455.000
age.z	0.343	0.032	0.280 – 0.407	10.599	<0.001	4459.000	0.149	0.023	0.105 – 0.193	6.610	<0.001	4459.000	0.276	0.030	0.217 – 0.336	9.088	<0.001	4455.000
education.z	0.144	0.030	0.085 – 0.203	4.799	<0.001	4459.000	0.144	0.021	0.103 – 0.185	6.889	<0.001	4459.000	0.071	0.028	0.016 – 0.126	2.526	0.012	4455.000
index_ANexp.z * ideology.z	0.123	0.035	0.055 – 0.191	3.564	<0.001	4459.000	0.079	0.024	0.032 – 0.126	3.281	0.001	4459.000	0.080	0.033	0.016 – 0.144	2.459	0.014	4455.000
index_ANexp.z * USvUK	-0.125	0.068	-0.258 – 0.008	-1.848	0.065	4459.000	-0.161	0.047	-0.254 – -0.068	-3.404	0.001	4459.000	-0.060	0.063	-0.184 – 0.064	-0.949	0.343	4455.000
ideology.z * USvUK	0.010	0.068	-0.124 – 0.144	0.146	0.884	4459.000	0.057	0.048	-0.036 – 0.151	1.207	0.228	4459.000	0.007	0.070	-0.130 – 0.143	0.095	0.924	4455.000
(index_ANexp.z ideology.z) USvUK	-0.018	0.069	-0.153 – 0.117	-0.264	0.792	4459.000	-0.033	0.048	-0.127 – 0.061	-0.687	0.492	4459.000	-0.020	0.065	-0.147 – 0.107	-0.304	0.761	4455.000
trustExpert.z													0.849	0.035	0.781 – 0.918	24.273	<0.001	4455.000
trustExpert.z * ideology.z													0.016	0.036	-0.055 – 0.088	0.441	0.659	4455.000
trustExpert.z * USvUK													0.263	0.070	0.125 – 0.400	3.745	<0.001	4455.000
(trustExpert.z ideology.z) USvUK													0.061	0.073	-0.082 – 0.204	0.834	0.404	4455.000
Observations	4469						4469						4469
R2 / R2 adjusted	0.121 / 0.120						0.129 / 0.127						0.242 / 0.240

iii. demographics

Model 1: vaxxIntentions ~ ideology.z + USvUK + age.z + education.z

Model 2: expertTrust ~ index.z + ideology.z + USvUK + age.z + education.z

Model 3: vaxxIntentions ~ expertTrust.z + ideology.z + USvUK + age.z + education.z

	vaxxIntentions						trustExpert						vaxxIntentions
Predictors	Est	SE	CI	t	p	df	Est	SE	CI	t	p	df	Est	SE	CI	t	p	df
(Intercept)	1.020	0.033	0.955 – 1.085	30.841	<0.001	4459.000	1.684	0.022	1.641 – 1.728	76.011	<0.001	4464.000	0.922	0.030	0.864 – 0.980	31.164	<0.001	4463.000
index_ANexp.z	0.326	0.034	0.260 – 0.393	9.605	<0.001	4459.000
ideology.z	-0.351	0.035	-0.419 – -0.283	-10.137	<0.001	4459.000	-0.342	0.021	-0.384 – -0.301	-16.055	<0.001	4464.000	-0.223	0.029	-0.280 – -0.166	-7.645	<0.001	4463.000
USvUK	0.896	0.070	0.759 – 1.033	12.802	<0.001	4459.000	0.524	0.048	0.430 – 0.618	10.891	<0.001	4464.000	0.528	0.065	0.401 – 0.655	8.133	<0.001	4463.000
age.z	0.343	0.032	0.280 – 0.407	10.599	<0.001	4459.000	0.132	0.023	0.088 – 0.177	5.835	<0.001	4464.000	0.237	0.030	0.178 – 0.297	7.819	<0.001	4463.000
education.z	0.144	0.030	0.085 – 0.203	4.799	<0.001	4459.000	0.141	0.021	0.099 – 0.182	6.642	<0.001	4464.000	0.064	0.028	0.008 – 0.119	2.251	0.024	4463.000
index_ANexp.z * ideology.z	0.123	0.035	0.055 – 0.191	3.564	<0.001	4459.000
index_ANexp.z * USvUK	-0.125	0.068	-0.258 – 0.008	-1.848	0.065	4459.000
ideology.z * USvUK	0.010	0.068	-0.124 – 0.144	0.146	0.884	4459.000
(index_ANexp.z ideology.z) USvUK	-0.018	0.069	-0.153 – 0.117	-0.264	0.792	4459.000
trustExpert.z													0.822	0.029	0.764 – 0.879	27.967	<0.001	4463.000
Observations	4469						4469						4469
R2 / R2 adjusted	0.121 / 0.120						0.104 / 0.104						0.224 / 0.223

e. robustness checks

i. summary LIWC variables

vaxxIntentions ~ [XXXX index] * (ideology + USvUK + age + education)

AN = analytical (main model)

AUTH = authentic

AF = affect

CL = clout

NE = negative emotion

PE = positive emotion

	vaxxIntentions			vaxxIntentions			vaxxIntentions			vaxxIntentions			vaxxIntentions
Predictors	Est	CI	p	Est	CI	p	Est	CI	p	Est	CI	p	Est	CI	p
(Intercept)	1.019	0.955 – 1.083	<0.001	1.012	0.949 – 1.074	<0.001	1.025	0.960 – 1.090	<0.001	1.019	0.955 – 1.083	<0.001	1.020	0.955 – 1.084	<0.001
ideology.z	-0.362	-0.423 – -0.302	<0.001	-0.368	-0.428 – -0.308	<0.001	-0.362	-0.423 – -0.302	<0.001	-0.364	-0.425 – -0.304	<0.001	-0.360	-0.420 – -0.299	<0.001
USvUK	0.935	0.796 – 1.074	<0.001	0.865	0.730 – 1.000	<0.001	0.956	0.816 – 1.096	<0.001	0.932	0.793 – 1.071	<0.001	0.939	0.800 – 1.079	<0.001
age.z	0.338	0.274 – 0.401	<0.001	0.345	0.282 – 0.408	<0.001	0.342	0.278 – 0.405	<0.001	0.339	0.275 – 0.402	<0.001	0.336	0.273 – 0.399	<0.001
education.z	0.162	0.102 – 0.222	<0.001	0.160	0.100 – 0.220	<0.001	0.160	0.100 – 0.220	<0.001	0.163	0.103 – 0.223	<0.001	0.161	0.101 – 0.221	<0.001
index_AFexp.z	0.325	0.252 – 0.398	<0.001
index_AFexp.z * ideology.z	0.144	0.088 – 0.200	<0.001
index_AFexp.z * USvUK	-0.105	-0.256 – 0.047	0.175
index_AFexp.z * age.z	-0.040	-0.107 – 0.026	0.231
index_AFexp.z * education.z	-0.059	-0.111 – -0.008	0.023
index_AUTHexp.z				0.319	0.250 – 0.388	<0.001
index_AUTHexp.z * ideology.z				0.136	0.080 – 0.192	<0.001
index_AUTHexp.z * USvUK				-0.109	-0.252 – 0.034	0.136
index_AUTHexp.z * age.z				-0.058	-0.125 – 0.009	0.089
index_AUTHexp.z * education.z				-0.065	-0.116 – -0.015	0.011
index_CLexp.z							0.336	0.260 – 0.413	<0.001
index_CLexp.z * ideology.z							0.138	0.082 – 0.194	<0.001
index_CLexp.z * USvUK							-0.069	-0.226 – 0.088	0.388
index_CLexp.z * age.z							-0.042	-0.108 – 0.024	0.210
index_CLexp.z * education.z							-0.057	-0.108 – -0.007	0.027
index_PEexp.z										0.324	0.251 – 0.397	<0.001
index_PEexp.z * ideology.z										0.145	0.089 – 0.201	<0.001
index_PEexp.z * USvUK										-0.098	-0.249 – 0.053	0.204
index_PEexp.z * age.z										-0.041	-0.107 – 0.026	0.230
index_PEexp.z * education.z										-0.060	-0.112 – -0.009	0.021
index_NEexp.z													0.326	0.254 – 0.399	<0.001
index_NEexp.z * ideology.z													0.143	0.087 – 0.199	<0.001
index_NEexp.z * USvUK													-0.113	-0.265 – 0.039	0.145
index_NEexp.z * age.z													-0.041	-0.107 – 0.025	0.227
index_NEexp.z * education.z													-0.058	-0.110 – -0.006	0.027
Observations	4469			4469			4469			4469			4469
R2 / R2 adjusted	0.124 / 0.122			0.124 / 0.122			0.123 / 0.122			0.124 / 0.122			0.124 / 0.122

ii. difference score: analytical-[XXXX] index * ideology + age + education

vaxxIntentions ~ difference score index * ideology * USvUK + age + education

model 1 = analytic - authenticity

model 2 = analytic - clout

model 3 = main model

	vaxxIntentions				vaxxIntentions				vaxxIntentions
Predictors	Est	CI	t	p	Est	CI	t	p	Est	CI	t	p
(Intercept)	1.020	0.955 – 1.085	30.655	<0.001	1.049	0.977 – 1.122	28.472	<0.001	1.020	0.955 – 1.085	30.841	<0.001
USvUK	0.908	0.770 – 1.046	12.892	<0.001	0.596	0.447 – 0.745	7.863	<0.001	0.896	0.759 – 1.033	12.802	<0.001
age.z	0.341	0.278 – 0.405	10.531	<0.001	0.365	0.300 – 0.430	11.062	<0.001	0.343	0.280 – 0.407	10.599	<0.001
education.z	0.145	0.086 – 0.204	4.827	<0.001	0.133	0.073 – 0.192	4.384	<0.001	0.144	0.085 – 0.203	4.799	<0.001
ideology.z	-0.346	-0.415 – -0.278	-9.975	<0.001	-0.381	-0.458 – -0.304	-9.681	<0.001	-0.351	-0.419 – -0.283	-10.137	<0.001
ideology.z * USvUK	0.011	-0.123 – 0.146	0.167	0.867	-0.047	-0.200 – 0.106	-0.601	0.548	0.010	-0.124 – 0.144	0.146	0.884
index_AN_AUTH_exp.z	0.318	0.251 – 0.384	9.389	<0.001
index_AN_AUTH_exp.z * ideology.z	0.117	0.050 – 0.184	3.415	0.001
index_AN_AUTH_exp.z * USvUK	-0.154	-0.286 – -0.021	-2.274	0.023
(index_AN_AUTH_exp.z ideology.z) USvUK	-0.031	-0.165 – 0.103	-0.455	0.649
index_AN_CL_exp.z					0.359	0.285 – 0.433	9.494	<0.001
index_AN_CL_exp.z * ideology.z					0.053	-0.017 – 0.122	1.484	0.138
index_AN_CL_exp.z * USvUK					-0.413	-0.560 – -0.266	-5.514	<0.001
(index_AN_CL_exp.z ideology.z) USvUK					0.021	-0.117 – 0.160	0.302	0.763
index_ANexp.z									0.326	0.260 – 0.393	9.605	<0.001
index_ANexp.z * ideology.z									0.123	0.055 – 0.191	3.564	<0.001
index_ANexp.z * USvUK									-0.125	-0.258 – 0.008	-1.848	0.065
(index_ANexp.z ideology.z) USvUK									-0.018	-0.153 – 0.117	-0.264	0.792
Observations	4469				4469				4469
R2 / R2 adjusted	0.121 / 0.119				0.107 / 0.105				0.121 / 0.120

iii. sum of media index

vaxxIntentions ~ sum media consumption * ideology * USvUK + age + education

vs. main model

	vaxxIntentions				vaxxIntentions
Predictors	Est	CI	t	p	Est	CI	t	p
(Intercept)	0.613	0.512 – 0.714	11.877	<0.001	1.020	0.955 – 1.085	30.841	<0.001
sum.exp	0.041	0.031 – 0.051	8.158	<0.001
ideology.z	-0.512	-0.618 – -0.407	-9.486	<0.001	-0.351	-0.419 – -0.283	-10.137	<0.001
USvUK	1.033	0.827 – 1.240	9.801	<0.001	0.896	0.759 – 1.033	12.802	<0.001
age.z	0.341	0.278 – 0.404	10.536	<0.001	0.343	0.280 – 0.407	10.599	<0.001
education.z	0.145	0.086 – 0.204	4.824	<0.001	0.144	0.085 – 0.203	4.799	<0.001
sum.exp * ideology.z	0.017	0.006 – 0.027	3.164	0.002
sum.exp * USvUK	0.007	-0.013 – 0.027	0.678	0.498
ideology.z * USvUK	0.037	-0.172 – 0.247	0.349	0.727	0.010	-0.124 – 0.144	0.146	0.884
(sum.exp * ideology.z) * USvUK	0.006	-0.014 – 0.026	0.575	0.565
index_ANexp.z					0.326	0.260 – 0.393	9.605	<0.001
index_ANexp.z * ideology.z					0.123	0.055 – 0.191	3.564	<0.001
index_ANexp.z * USvUK					-0.125	-0.258 – 0.008	-1.848	0.065
(index_ANexp.z ideology.z) USvUK					-0.018	-0.153 – 0.117	-0.264	0.792
Observations	4469				4469
R2 / R2 adjusted	0.122 / 0.120				0.121 / 0.120

iv. ethnicity included US only

vaxxIntentions ~ index_ANexp.z * ideology.z + US_0 + age.z + education.z + White_.5

	vaxxIntentions
Predictors	Est	SE	CI	t	p	df
(Intercept)	0.501	0.041	0.420 – 0.582	12.152	<0.001	2980.000
index_ANexp.z	0.419	0.037	0.346 – 0.491	11.298	<0.001	2980.000
ideology.z	-0.386	0.037	-0.459 – -0.312	-10.315	<0.001	2980.000
age.z	0.414	0.039	0.337 – 0.491	10.572	<0.001	2980.000
education.z	0.180	0.036	0.110 – 0.250	5.025	<0.001	2980.000
white_.5	0.357	0.081	0.198 – 0.516	4.404	<0.001	2980.000
index_ANexp.z * ideology.z	0.147	0.033	0.081 – 0.212	4.402	<0.001	2980.000
Observations	2987
R2 / R2 adjusted	0.128 / 0.126

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7. Figures

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## Scale for 'y' is already present. Adding another scale for 'y', which will
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