Degrees of deception: the effects of different types of COVID-19 misinformation and the effectiveness of corrective information in crisis times

Figures & data

Table 1. The different types of misinformation distinguished in this paper.

	Misinformation type
	De-contextualized or partially false information	Fabricated or completely false information
Deviation facticity	Stays close to reality but places facts outside of their context to alter their meaning Facts are reinterpreted or decontextualized	Deviates further from reality by introducing counterfactual narratives and false storylines An event is depicted inaccurately by altering its epistemic foundations
Intentions/causes	Unintentionally (misinformation): Based on lack of evidence and expert knowledge Intentionally (disinformation): Exploiting a veracity effect and staying close to reality to make a lie more believable, affecting perceptions of the truth	Unintentionally (misinformation): Based on lack of evidence and expert knowledge Intentionally (disinformation): To persuade news users of a different counterfactual narrative; to increase doubt, distrust, and cynicism; to mobilize

Table 2. Regression models predicting the credibility of partially and completely false information across both issues.

	Migrants			Runners
	B	SE	p	B	SE	p
(Constant)	4.23	.15	.000	4.64	.16	.000
Partially false	-.53	.21	.013	−1.05	.23	.000
Completely false	−1.55	.22	.000	−1.78	.22	.000
Attitudes	.01	.05	.797	-.12	.04	.003
Attitudes × partly false	.12	.07	.083	.20	.06	.001
Attitudes × completely false	.30	.07	.000	.34	.06	.000
Adjusted R^2			.07			.05
N			1490			1490

Note: OLS = ordinary least squares. Cells include robust standard errors (clustered on respondents) and betas (standardized coefficients).

Table 3. Regression models predicting the agreement with partially and completely false information across both issues.

	Migrants			Runners
	B	SE	p	B	SE	β
(Constant)	4.00	.17	.000	4.52	.18	.000
Partially false	-.36	.25	.148	−1.02	.26	.000
Completely false	−1.59	.25	.000	−1.74	.25	.000
Attitudes	.03	.06	.617	-.11	.05	.020
Attitudes × partly false	.06	.08	.475	.20	.07	.004
Attitudes × completely false	.37	.08	.000	.32	.07	.000
Adjusted R^2			.05			.04
N			1490			1490

Note: OLS = ordinary least squares. Cells include robust standard errors (clustered on respondents) and betas (standardized coefficients).

Table 4. The effects of fact-checkers on the credibility of partially and completely false information across both issues.

	Migrants			Runners
	B	SE	p	B	SE	p
(Constant)	3.50	.15	.000	3.14	.15	.000
Partially false	.13	.13	.327	.42	.15	.004
Completely false	.77	.15	.000	.99	.15	.000
Attitudes	-.06	.04	.141	-.03	.03	.413
Fact-checker (FC)	−1.08	.13	.000	-.81	.14	.000
Partially false × FC	2.08	.28	.000	2.20	.30	.000
Completely false × FC	2.77	.29	.000	2.69	.27	.000
FC × partially false × attitudes	-.01	.08	.892	-.07	.07	.285
FC × completely false × attitudes	-.13	.08	.106	-.16	.06	.014
Adjusted R^2			.30			.31
N			1481			1479

Note: OLS = ordinary least squares, FC = fact-checker. Cells include robust standard errors (clustered on respondents) and betas (standardized coefficients).

Table 5. The effects of fact-checkers on the agreement with partially and completely false information across both issues.

	Migrants			Runners
	B	SE	p	B	SE	p
(Constant)	2.85	.18	.000	1.76	.17	.000
Partially false	-.17	.17	.347	.19	.14	.156
Completely false	.15	.17	.380	.50	.14	.000
Attitudes	.38	.04	.000	.30	.03	.000
Fact-checker (FC)	-.07	.17	.706	.08	.14	.545
Partially false × FC	.01	.37	.984	.07	.28	.797
Completely false × FC	-.79	.35	.026	-.68	.27	.010
FC × partially false × attitudes	.01	.10	.907	-.10	.06	.113
FC × completely false × attitudes	.16	.09	.08	.03	.06	.585
Adjusted R^2			.09			.11
N			1476			1476

Note: OLS = ordinary least squares, FC = fact-checker. Cells include robust standard errors (clustered on respondents) and betas (standardized coefficients).