# hypothesis 2: only false headlines
```r
truth ~ repetitionc * conditiond + (1 + repetitionc | id) + (1 + conditiond | item)
# IN
term result p_sig
1: b_Intercept b = 4.04 [3.89, 4.19] *
2: b_repetitionc b = 0.11 [0.06, 0.17] *
3: b_conditiond b = -0.34 [-0.48, -0.20] *
4: b_repetitionc:conditiond b = -0.14 [-0.22, -0.07] *
# PN
term result p_sig
1: b_Intercept b = 3.68 [3.51, 3.86] *
2: b_repetitionc b = 0.12 [0.08, 0.15] *
3: b_conditiond b = -0.48 [-0.61, -0.35] *
4: b_repetitionc:conditiond b = -0.16 [-0.22, -0.11] *
form <- formula(truth ~ repetitionc * (conditiond + age + gender + education + income + aot + covid_concern + conspiracy)
+ (1 + repetitionc | id) + (1 + conditiond | item))
# IN
> summ_brms(m2covb)
term result p_sig
1: b_Intercept b = 3.84 [3.69, 3.98] *
2: b_repetitionc b = 0.12 [0.07, 0.18] *
3: b_conditiond b = -0.25 [-0.36, -0.13] *
4: b_age b = -0.16 [-0.22, -0.10] *
5: b_gender b = 0.04 [-0.02, 0.10]
6: b_education b = 0.09 [0.03, 0.15] *
7: b_income b = 0.03 [-0.03, 0.08]
8: b_aot b = -0.43 [-0.49, -0.37] *
9: b_covid_concern b = 0.12 [0.07, 0.18] *
10: b_conspiracy b = 0.14 [0.08, 0.20] *
11: b_repetitionc:conditiond b = -0.14 [-0.21, -0.07] *
12: b_repetitionc:age b = 0.03 [-0.01, 0.07]
13: b_repetitionc:gender b = 0.01 [-0.02, 0.05]
14: b_repetitionc:education b = 0.00 [-0.04, 0.04]
15: b_repetitionc:income b = -0.05 [-0.09, -0.01] *
16: b_repetitionc:aot b = -0.03 [-0.07, 0.01]
17: b_repetitionc:covid_concern b = 0.01 [-0.03, 0.05]
18: b_repetitionc:conspiracy b = 0.01 [-0.02, 0.05]
# PN
> summ_brms(m2covb)
term result p_sig
1: b_Intercept b = 3.77 [3.59, 3.94] *
2: b_repetitionc b = 0.13 [0.08, 0.17] *
3: b_conditiond b = -0.47 [-0.58, -0.35] *
4: b_age b = -0.10 [-0.15, -0.05] *
5: b_gender b = 0.08 [0.03, 0.14] *
6: b_education b = -0.07 [-0.12, -0.01] *
7: b_income b = 0.00 [-0.06, 0.06]
8: b_aot b = -0.46 [-0.51, -0.40] *
9: b_covid_concern b = 0.01 [-0.05, 0.06]
10: b_conspiracy b = 0.16 [0.11, 0.22] *
11: b_repetitionc:conditiond b = -0.16 [-0.22, -0.10] *
12: b_repetitionc:age b = 0.00 [-0.03, 0.03]
13: b_repetitionc:gender b = 0.02 [-0.01, 0.05]
14: b_repetitionc:education b = 0.00 [-0.03, 0.03]
15: b_repetitionc:income b = 0.02 [-0.01, 0.05]
16: b_repetitionc:aot b = -0.03 [-0.06, 0.00] *
17: b_repetitionc:covid_concern b = 0.00 [-0.03, 0.03]
18: b_repetitionc:conspiracy b = -0.01 [-0.04, 0.02]
form <- formula(truth ~ repetitionc * conditiond * (age + gender + education + income + aot + covid_concern + conspiracy)
+ (1 + repetitionc | id) + (1 + conditiond | item))
# IN
term result p_sig
1: b_Intercept b = 3.84 [3.69, 3.99] *
2: b_repetitionc b = 0.12 [0.06, 0.18] *
3: b_conditiond b = -0.24 [-0.38, -0.11] *
4: b_age b = -0.16 [-0.25, -0.07] *
5: b_gender b = 0.03 [-0.05, 0.11]
6: b_education b = 0.06 [-0.03, 0.15]
7: b_income b = 0.04 [-0.05, 0.12]
8: b_aot b = -0.47 [-0.56, -0.38] *
9: b_covid_concern b = 0.10 [0.02, 0.18] *
10: b_conspiracy b = 0.11 [0.03, 0.20] *
11: b_repetitionc:conditiond b = -0.13 [-0.21, -0.05] *
12: b_repetitionc:age b = 0.04 [-0.02, 0.09]
13: b_repetitionc:gender b = 0.02 [-0.03, 0.07]
14: b_repetitionc:education b = 0.06 [0.00, 0.11] *
15: b_repetitionc:income b = -0.06 [-0.11, -0.01] *
16: b_repetitionc:aot b = 0.01 [-0.05, 0.07]
17: b_repetitionc:covid_concern b = 0.03 [-0.02, 0.08]
18: b_repetitionc:conspiracy b = 0.02 [-0.03, 0.08]
19: b_conditiond:age b = -0.01 [-0.13, 0.11]
20: b_conditiond:gender b = 0.01 [-0.11, 0.13]
21: b_conditiond:education b = 0.06 [-0.06, 0.18]
22: b_conditiond:income b = -0.01 [-0.12, 0.11]
23: b_conditiond:aot b = 0.07 [-0.06, 0.19]
24: b_conditiond:covid_concern b = 0.04 [-0.08, 0.16]
25: b_conditiond:conspiracy b = 0.05 [-0.07, 0.16]
26: b_repetitionc:conditiond:age b = -0.01 [-0.09, 0.07]
27: b_repetitionc:conditiond:gender b = -0.01 [-0.09, 0.06]
28: b_repetitionc:conditiond:education b = -0.10 [-0.18, -0.02] *
29: b_repetitionc:conditiond:income b = 0.01 [-0.06, 0.09]
30: b_repetitionc:conditiond:aot b = -0.06 [-0.14, 0.02]
31: b_repetitionc:conditiond:covid_concern b = -0.05 [-0.12, 0.03]
32: b_repetitionc:conditiond:conspiracy b = -0.02 [-0.09, 0.06]
term result p_sig
> summ_brms(m2covb_3way)
term result p_sig
1: b_Intercept b = 3.75 [3.58, 3.93] *
2: b_repetitionc b = 0.13 [0.08, 0.17] *
3: b_conditiond b = -0.44 [-0.56, -0.31] *
4: b_age b = -0.10 [-0.17, -0.02] *
5: b_gender b = 0.11 [0.04, 0.19] *
6: b_education b = -0.09 [-0.17, -0.01] *
7: b_income b = 0.01 [-0.07, 0.09]
8: b_aot b = -0.45 [-0.53, -0.38] *
9: b_covid_concern b = 0.05 [-0.02, 0.13]
10: b_conspiracy b = 0.20 [0.13, 0.28] *
11: b_repetitionc:conditiond b = -0.16 [-0.22, -0.09] *
12: b_repetitionc:age b = -0.01 [-0.05, 0.03]
13: b_repetitionc:gender b = 0.01 [-0.03, 0.05]
14: b_repetitionc:education b = 0.00 [-0.04, 0.04]
15: b_repetitionc:income b = 0.02 [-0.03, 0.06]
16: b_repetitionc:aot b = -0.03 [-0.08, 0.01]
17: b_repetitionc:covid_concern b = 0.02 [-0.02, 0.06]
18: b_repetitionc:conspiracy b = 0.00 [-0.05, 0.04]
19: b_conditiond:age b = 0.00 [-0.11, 0.10]
20: b_conditiond:gender b = -0.06 [-0.17, 0.04]
21: b_conditiond:education b = 0.03 [-0.08, 0.15]
22: b_conditiond:income b = -0.02 [-0.14, 0.09]
23: b_conditiond:aot b = -0.01 [-0.12, 0.10]
24: b_conditiond:covid_concern b = -0.12 [-0.23, -0.01] *
25: b_conditiond:conspiracy b = -0.09 [-0.19, 0.02]
26: b_repetitionc:conditiond:age b = 0.02 [-0.04, 0.08]
27: b_repetitionc:conditiond:gender b = 0.03 [-0.03, 0.09]
28: b_repetitionc:conditiond:education b = 0.00 [-0.07, 0.06]
29: b_repetitionc:conditiond:income b = 0.00 [-0.06, 0.07]
30: b_repetitionc:conditiond:aot b = 0.00 [-0.06, 0.06]
31: b_repetitionc:conditiond:covid_concern b = -0.06 [-0.12, 0.00]
32: b_repetitionc:conditiond:conspiracy b = -0.01 [-0.07, 0.05]
```
# hypothesis 1: moderation of repetition effect and headline veracity
```r
m1b <- brm(truth ~ veracityc * repetitionc + (1 + veracityc * repetitionc | id) + (1 + repetitionc | item), d2c,
cores = 8, chains = 8, iter = 2000, warmup = 1000, file = glue("../models-{COUNTRY}/m1b"),
backend = "cmdstanr", stan_model_args = list(stanc_options = list("O1")),
prior = priors)
m1b
# IN
> summ_brms(m1b)
term result p_sig
1: b_Intercept b = 4.18 [4.07, 4.29] *
2: b_veracityc b = 0.28 [0.14, 0.42] *
3: b_repetitionc b = 0.11 [0.07, 0.16] *
4: b_veracityc:repetitionc b = -0.01 [-0.09, 0.07]
# PN
term result p_sig
1: b_Intercept b = 3.89 [3.78, 4.01] *
2: b_veracityc b = 0.42 [0.23, 0.61] *
3: b_repetitionc b = 0.10 [0.06, 0.14] *
4: b_veracityc:repetitionc b = -0.03 [-0.10, 0.04]
form <- formula(truth ~ veracityc * (repetitionc + age + gender + education + income + aot + covid_concern + conspiracy)
+ (1 + veracityc * repetitionc | id) + (1 + repetitionc | item))
> summ_brms(m1b_cov)
term result p_sig
1: b_Intercept b = 4.01 [3.90, 4.11] *
2: b_veracityc b = 0.33 [0.18, 0.47] *
3: b_repetitionc b = 0.11 [0.07, 0.16] *
4: b_age b = -0.13 [-0.21, -0.06] *
5: b_gender b = 0.02 [-0.05, 0.09]
6: b_education b = 0.07 [-0.01, 0.14]
7: b_income b = 0.05 [-0.01, 0.12]
8: b_aot b = -0.37 [-0.45, -0.30] *
9: b_covid_concern b = 0.10 [0.04, 0.17] *
10: b_conspiracy b = 0.14 [0.06, 0.21] *
11: b_veracityc:repetitionc b = -0.01 [-0.09, 0.07]
12: b_veracityc:age b = 0.06 [0.01, 0.10] *
13: b_veracityc:gender b = -0.02 [-0.06, 0.03]
14: b_veracityc:education b = 0.03 [-0.02, 0.08]
15: b_veracityc:income b = 0.03 [-0.01, 0.08]
16: b_veracityc:aot b = 0.17 [0.12, 0.22] *
17: b_veracityc:covid_concern b = 0.01 [-0.03, 0.05]
18: b_veracityc:conspiracy b = 0.04 [-0.01, 0.08]
# PN
> summ_brms(m1b_cov)
term result p_sig
1: b_Intercept b = 3.95 [3.83, 4.07] *
2: b_veracityc b = 0.40 [0.20, 0.60] *
3: b_repetitionc b = 0.10 [0.06, 0.14] *
4: b_age b = -0.09 [-0.15, -0.03] *
5: b_gender b = 0.07 [0.01, 0.13] *
6: b_education b = -0.06 [-0.13, 0.00]
7: b_income b = 0.03 [-0.04, 0.09]
8: b_aot b = -0.33 [-0.40, -0.27] *
9: b_covid_concern b = 0.08 [0.02, 0.14] *
10: b_conspiracy b = 0.16 [0.10, 0.22] *
11: b_veracityc:repetitionc b = -0.03 [-0.10, 0.04]
12: b_veracityc:age b = 0.02 [-0.03, 0.07]
13: b_veracityc:gender b = -0.10 [-0.15, -0.05] *
14: b_veracityc:education b = 0.06 [0.01, 0.11] *
15: b_veracityc:income b = 0.04 [-0.02, 0.09]
16: b_veracityc:aot b = 0.23 [0.18, 0.28] *
17: b_veracityc:covid_concern b = 0.05 [0.00, 0.10] *
18: b_veracityc:conspiracy b = -0.08 [-0.13, -0.03] *
form <- formula(truth ~ veracityc * repetitionc * (age + gender + education + income + aot + covid_concern + conspiracy)
+ (1 + veracityc * repetitionc | id) + (1 + repetitionc | item))
# IN
> summ_brms(m1b_full)
term result p_sig
1: b_Intercept b = 4.01 [3.90, 4.11] *
2: b_veracityc b = 0.33 [0.19, 0.48] *
3: b_repetitionc b = 0.12 [0.08, 0.17] *
4: b_age b = -0.13 [-0.20, -0.06] *
5: b_gender b = 0.02 [-0.05, 0.09]
6: b_education b = 0.07 [0.00, 0.14]
7: b_income b = 0.05 [-0.01, 0.12]
8: b_aot b = -0.38 [-0.45, -0.30] *
9: b_covid_concern b = 0.10 [0.04, 0.17] *
10: b_conspiracy b = 0.13 [0.06, 0.20] *
11: b_veracityc:repetitionc b = 0.01 [-0.08, 0.10]
12: b_veracityc:age b = 0.05 [0.00, 0.10] *
13: b_veracityc:gender b = -0.02 [-0.07, 0.03]
14: b_veracityc:education b = 0.02 [-0.03, 0.07]
15: b_veracityc:income b = 0.04 [-0.01, 0.08]
16: b_veracityc:aot b = 0.18 [0.13, 0.23] *
17: b_veracityc:covid_concern b = 0.01 [-0.04, 0.05]
18: b_veracityc:conspiracy b = 0.04 [0.00, 0.09]
19: b_repetitionc:age b = 0.02 [-0.02, 0.06]
20: b_repetitionc:gender b = 0.01 [-0.03, 0.04]
21: b_repetitionc:education b = 0.02 [-0.02, 0.06]
22: b_repetitionc:income b = -0.04 [-0.07, 0.00] *
23: b_repetitionc:aot b = 0.02 [-0.02, 0.06]
24: b_repetitionc:covid_concern b = 0.02 [-0.02, 0.05]
25: b_repetitionc:conspiracy b = 0.03 [-0.01, 0.06]
26: b_veracityc:repetitionc:age b = -0.03 [-0.10, 0.04]
27: b_veracityc:repetitionc:gender b = -0.02 [-0.09, 0.04]
28: b_veracityc:repetitionc:education b = -0.06 [-0.13, 0.01]
29: b_veracityc:repetitionc:income b = 0.04 [-0.03, 0.10]
30: b_veracityc:repetitionc:aot b = 0.02 [-0.05, 0.09]
31: b_veracityc:repetitionc:covid_concern b = -0.03 [-0.09, 0.03]
32: b_veracityc:repetitionc:conspiracy b = 0.01 [-0.05, 0.08]
term result p_sig
# PN
> summ_brms(m1b_full)
term result p_sig
1: b_Intercept b = 3.95 [3.83, 4.07] *
2: b_veracityc b = 0.40 [0.20, 0.59] *
3: b_repetitionc b = 0.10 [0.06, 0.14] *
4: b_age b = -0.09 [-0.15, -0.03] *
5: b_gender b = 0.07 [0.01, 0.13] *
6: b_education b = -0.06 [-0.13, 0.00]
7: b_income b = 0.03 [-0.04, 0.10]
8: b_aot b = -0.34 [-0.40, -0.27] *
9: b_covid_concern b = 0.08 [0.02, 0.14] *
10: b_conspiracy b = 0.16 [0.10, 0.22] *
11: b_veracityc:repetitionc b = -0.04 [-0.12, 0.03]
12: b_veracityc:age b = 0.02 [-0.03, 0.07]
13: b_veracityc:gender b = -0.09 [-0.14, -0.04] *
14: b_veracityc:education b = 0.06 [0.00, 0.11] *
15: b_veracityc:income b = 0.04 [-0.01, 0.10]
16: b_veracityc:aot b = 0.23 [0.17, 0.28] *
17: b_veracityc:covid_concern b = 0.05 [0.00, 0.10] *
18: b_veracityc:conspiracy b = -0.09 [-0.14, -0.04] *
19: b_repetitionc:age b = -0.01 [-0.04, 0.02]
20: b_repetitionc:gender b = 0.03 [0.00, 0.06]
21: b_repetitionc:education b = -0.02 [-0.05, 0.02]
22: b_repetitionc:income b = 0.02 [-0.01, 0.05]
23: b_repetitionc:aot b = -0.03 [-0.06, 0.00]
24: b_repetitionc:covid_concern b = 0.00 [-0.03, 0.03]
25: b_repetitionc:conspiracy b = -0.01 [-0.05, 0.02]
26: b_veracityc:repetitionc:age b = 0.01 [-0.05, 0.06]
27: b_veracityc:repetitionc:gender b = 0.04 [-0.02, 0.09]
28: b_veracityc:repetitionc:education b = -0.03 [-0.09, 0.02]
29: b_veracityc:repetitionc:income b = 0.01 [-0.05, 0.07]
30: b_veracityc:repetitionc:aot b = 0.01 [-0.05, 0.07]
31: b_veracityc:repetitionc:covid_concern b = -0.04 [-0.09, 0.01]
32: b_veracityc:repetitionc:conspiracy b = -0.02 [-0.07, 0.03]
term result p_sig
```