How Social Media Influences Public Attitudes to COVID-19 Governance Policy: An Analysis Based on Cognitive-Affective Model

Figures & data

Figure 1 The initial “Social Media + “Cognitive-Affective”” model of public attitudes towards Covid-19 policy.

Table 1 A Summary of Hypotheses

	Aim	Hypothesis
1	Tests on the relationship between cognitive factors and public attitude towards COVID-19 governance policy	H1	HP → PATCOVID-19P
		H2	GEV →PATCOVID-19P
		H3	RGA→PATCOVID-19P
2	Tests on the relationship between affective factors and public attitude towards COVID-19 governance policy	H4	PA→PAT COVID-19P
		H5	Patriotism → PATCOVID-19P
		H6	Nationalism → PATCOVID-19P
3	Test on the relationship between social media and public attitude towards COVID-19 governance policy	H7	SMU→PATCOVID-19P
4	Tests on the moderating effects of social media on the relationship between cognitive factors, affective factors and public attitude towards COVID-19 governance policy	H8	SMU×HP → PATCOVID-19P
		H9	SMU×GEV → PATCOVID-19P
		H10	SMU×RGA → PATCOVID-19P
		H11	SMU×PA → PATCOVID-19P
		H12	SMU×Patriotism→ PATCOVID-19P
		H13	SMU×Nationalism → PATCOVID-19P
		H14	SMU×GEV× RGA→ PATCOVID-19P
		H15	SMU×PA×Patriotism → PATCOVID-19P

Abbreviations: PATCOVID-19P, Public Attitude towards COVID-19 Governance Policy; HP, Hazard Perception; GEV, Government Effectiveness Evaluation; RGA, Risk and Governance Anticipations; PA, Personal Anxiety; SMU, Social Media Use.

Table 2 Distribution of the Sample’s Socio-Demographic Information (N=1222)

	Categories	Frequency	Percentage (%)
Gender	Male	585	47.9
	Female	637	52.1
Age	18–24	396	32.4
	25–34	464	38.0
	35–44	196	16.0
	45–54	100	8.2
	55 -	66	5.4
Education	High school	304	24.9
	Vocational school	111	9.1
	College/University	486	39.8
	Master and above	321	26.3
Subjective income class	Upper	23	1.9
	Upper middle	204	16.7
	Middle	466	38.1
	Lower middle	349	28.6
	Lower	180	14.7
Nationality	Brazil	99	8.1
	India	98	8.0
	US	148	12.1
	UK	137	11.2
	South Africa	108	8.8
	Germany	101	8.3
	France	134	11.0
	China	100	8.2
	Australia	98	8.0
	Russia	67	5.5
	South Korea	89	7.3
	Japan	43	3.5

Table 3 Results of Regression Analysis of Individual Attitude Towards COVID-19 Policy (N=1202)

	Unstandardized Coefficient		Standardized Coefficient	t	p	VIF
	B	SE	Beta
Constant	3.013**	0.256	–	11.757	0.000	–
Male	−0.014	0.048	−0.009	−0.304	0.761	1.114
Age	0.030	0.020	0.041	1.470	0.142	1.083
Education	0.012	0.021	0.016	0.588	0.557	1.092
Income	−0.026	0.024	−0.030	−1.080	0.281	1.074
Authoritarianism	−0.049	0.090	−0.016	−0.543	0.587	1.296
Ill_COVID-19	−0.048	0.075	−0.017	−0.637	0.524	1.044
Mental Depression	0.007	0.026	0.008	0.281	0.778	1.141
COVID-19 Severely_Last Year	0.015	0.029	0.017	0.534	0.593	1.405
COVID-19 Severely_Recently	−0.012	0.025	−0.015	−0.470	0.638	1.443
COVID-19 Hanzard Perception	0.038	0.037	0.036	1.046	0.296	1.631
Government Effectiveness Evaluation	0.062**	0.017	0.095	3.526	0.000	1.028
Risk and Governance Anticipations	−0.081*	0.034	−0.067	−2.386	0.017	1.118
Personal Anxiety_Last Year	0.184**	0.025	0.258	7.241	0.000	1.798
Personal Anxiety_Recently	0.056*	0.026	0.079	2.141	0.032	1.915
Patriotism	−0.093**	0.035	−0.073	−2.634	0.009	1.089
Nationalism	0.024	0.030	0.024	0.780	0.436	1.308
Social Media Use	0.078**	0.030	0.077	2.595	0.010	1.238
R^2	0.161
Adjusted R^2	0.149
F	F (17, 1184)=13.366, p=0.000

Notes: D-W Value: 1.969; *p<0.05 **p<0.01.

Table 4 Summary of Process Model 82 Mediation Test Results

Items	Indirect Effect	BootSE	BootLLCI	BootULCI	Direct Effect	Test Results
Total Indirect Effect	0.0401**	0.0127	0.0168	0.0664
SMU≤ Patriotism ≤ PATCOVID-19P	−0.142**	0.0064	−0.0272	−0.0021	0.1209**	Masking effect
SMU≤ PA_Recently ≤ PATCOVID-19P	0.0514**	0.0103	0.0328	0.0727	0.1209**	Partial mediation effect
SMU≤ GEV ≤ PATCOVID-19P	−0.0031	0.0031	−0.100	0.0024	0.1209**	Mediating effect is not significant
SMU≤ RGA ≤ PATCOVID-19P	0.0084**	0.0044	0.0017	0.0183	0.1209**	Partial mediation effect
SMU≤ Patriotism ≤ PA_Recently ≤ PATCOVID-19P	−0.0022**	0.0012	−0.0048	−0.0022		Masking effect and partial mediation effect

Note: **p<0.01.

Abbreviations: PATCOVID-19P, Public Attitude towards COVID-19 Governance Policy; HP, Hazard Perception; GEV, Government Effectiveness Evaluation; RGA, Risk and Governance Anticipations; PA, Personal Anxiety; SMU, Social Media Use.

Figure 2 “Social Media + “Cognitive-Affective”” effect relationship on public attitudes towards COVID-19 policy (**p<0.01).