| Literature DB >> 35783784 |
Chong Zhang1, Tong Cao2, Asad Ali3.
Abstract
During crises and uncertain situations such as the coronavirus disease 2019 (COVID-19) pandemic, social media plays a key function because it allows people to seek and share news, as well as personal views and ideas with each other in real time globally. Past research has highlighted the implications of social media during disease outbreaks; nevertheless, this study refers to the possible negative effects of social media usage by individuals in the developing country during the COVID-19 epidemic lockdown. Specifically, this study investigates the COVID-19 fear using the survey data collected from a developing country. In total, 880 entries were used to analyze the COVID-19 fear using the AMOS software. Findings indicated that information-seeking and sharing behavior of individuals on social media has a significant impact on perceived COVID-19 information overload. Perceived COVID-19 information overload has a positive impact on COVID-19 fear. In addition, fake news related to COVID-19 strengthens the relationship between perceived COVID-19 information overload and COVID-19 fear. The implication and limitations of the study are also discussed in the final section of the study.Entities:
Keywords: COVID-19; COVID-19 fear; fake news; information overload; social media
Year: 2022 PMID: 35783784 PMCID: PMC9247549 DOI: 10.3389/fpsyg.2022.930088
Source DB: PubMed Journal: Front Psychol ISSN: 1664-1078
FIGURE 1Proposed model.
Sample demographics.
| Frequency | Percentage | ||
| Gender | Male | 179 | 52.8 |
| Female | 160 | 47.2 | |
| Age range | Less than 20 years | 15 | 4.4 |
| 21–30 years | 194 | 57.2 | |
| 31–40 years | 98 | 28.9 | |
| 41 years and above | 32 | 9.4 | |
| Education level | High school | 15 | 4.4 |
| Bachelors degree | 118 | 34.8 | |
| Masters degree | 164 | 48.4 | |
| Doctor degree | 42 | 12.4 | |
| Profession | Student | 80 | 23.6 |
| Government employed | 107 | 31.6 | |
| Private employed | 78 | 23.0 | |
| Self-employed | 74 | 21.8 | |
| Social media usage | Hourly | 169 | 49.9 |
| Once per day | 113 | 33.3 | |
| Several times per day | 48 | 14.2 | |
| Several times per week | 9 | 2.7 |
Results of the confirmatory factor analysis.
| Construct | Items | Cronbach’s alpha | Composite reliability | Average variance extracted |
| Intentions to seek Information related to COVID-19 | 7 | 0.94 | 0.94 | 0.69 |
| Intentions to share Information related to COVID-19 | 8 | 0.92 | 0.92 | 0.58 |
| Perceived COVID-19 information overload | 3 | 0.82 | 0.82 | 0.61 |
| COVID-19 fear | 3 | 0.92 | 0.92 | 0.80 |
| Fake news related to COVID-19 | 5 | 0.94 | 0.94 | 0.75 |
Descriptive statistics and correlations matrix.
| Construct | Mean | Std. deviation | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| 1. Gender | 0.47 | 0.50 | – | |||||||||
| 2. Age | 2.43 | 0.72 | −0.06 | – | ||||||||
| 3. Education level | 2.69 | 0.74 | −0.20 | 0.16 | – | |||||||
| 4. Profession | 2.43 | 1.08 | 0.01 | −0.04 | −0.01 | – | ||||||
| 5. Social media usage frequency | 1.70 | 0.81 | 0.03 | −0.10 | −0.03 | −0.05 | – | |||||
| 6. Intentions to seek Information related to COVID-19 | 4.61 | 0.76 | 0.01 | 0.06 | 0.05 | −0.01 | −0.02 |
| ||||
| 7. Intentions to share Information related to COVID-19 | 4.74 | 0.64 | 0.08 | 0.13 | 0.04 | −0.06 | 0.03 | 0.37 |
| |||
| 8. Perceived COVID-19 information overload | 5.43 | 1.15 | 0.00 | 0.11 | 0.04 | −0.08 | −0.03 | 0.22 | 0.19 |
| ||
| 9. COVID-19 fear | 3.80 | 1.12 | 0.11 | 0.13 | 0.05 | −0.10 | −0.10 | 0.17 | 0.13 | 0.30 |
| |
| 10. Fake news related to COVID-19 | 4.07 | 0.65 | 0.03 | 0.07 | 0.02 | −0.10 | 0.09 | 0.41 | 0.45 | 0.13 | 0.05 |
|
*p = 0.05, **p = 0.01, diagonal cells represent square roots of average variance extracted. Bold diagonal cells show Cronbach’s alpha values.
Item loadings and cross-loadings.
| Factor | ||||||
| Construct | Items | 1 | 2 | 3 | 4 | 5 |
| 1. Intentions to seek Information related to COVID-19 | SEE1 |
| 0.00 | 0.13 | 0.01 | −0.03 |
| SEE2 |
| −0.01 | −0.02 | −0.01 | −0.04 | |
| SEE3 |
| 0.08 | 0.06 | 0.03 | −0.01 | |
| SEE4 |
| 0.03 | 0.00 | −0.01 | 0.07 | |
| SEE5 |
| −0.06 | −0.02 | −0.03 | 0.01 | |
| SEE6 |
| 0.01 | −0.06 | 0.04 | 0.01 | |
| SEE7 |
| 0.00 | −0.05 | −0.02 | −0.02 | |
| 2. Intentions to share Information related to COVID-19 | SH1 | 0.08 |
| −0.04 | −0.07 | 0.10 |
| SH2 | −0.03 |
| −0.02 | 0.01 | −0.07 | |
| SH3 | −0.11 |
| 0.00 | −0.02 | 0.01 | |
| SH4 | −0.05 |
| −0.07 | 0.02 | 0.01 | |
| SH5 | 0.01 |
| 0.04 | 0.01 | 0.05 | |
| SH6 | 0.06 |
| 0.11 | −0.03 | −0.02 | |
| SH7 | 0.06 |
| 0.05 | 0.01 | −0.07 | |
| SH8 | 0.07 |
| −0.03 | 0.05 | 0.00 | |
| 3. Perceived COVID-19 information overload | OV1 | −0.06 | 0.03 | −0.07 | 0.05 |
|
| OV2 | 0.01 | 0.07 | 0.06 | 0.03 |
| |
| OV3 | 0.05 | −0.10 | 0.05 | −0.06 |
| |
| 4. COVID-19 fear | CF1 | −0.01 | −0.01 | 0.02 |
| −0.04 |
| CF2 | 0.07 | 0.01 | −0.01 |
| 0.01 | |
| CF3 | −0.05 | −0.01 | 0.00 |
| 0.05 | |
| 5. Fake news related to COVID-19 | FA1 | −0.01 | 0.01 |
| −0.02 | 0.07 |
| FA2 | 0.03 | 0.06 |
| −0.02 | 0.04 | |
| FA3 | 0.00 | −0.02 |
| 0.03 | −0.05 | |
| FA4 | −0.01 | 0.00 |
| 0.02 | −0.04 | |
| FA5 | −0.02 | −0.04 |
| 0.00 | 0.01 | |
Highlighted values represent loadings of items on their corresponding variables.
Path analysis results of the structural model.
| Hypothesized relationships | β | S.E. | Significance level | Conclusion |
| H1a: Intentions to seek information related to COVID-19 to perceived COVID-19 information overload | 0.26 | 0.09 | 0.01 | Supported |
| H1b: Intentions to share information related to COVID-19 to perceived COVID-19 information overload | 0.24 | 0.10 | 0.05 | Supported |
| H2: Perceived COVID-19 information overload to fear of COVID-19 | 0.28 | 0.05 | 0.00 | Supported |
|
| ||||
| Fake news related to COVID-19 | 0.03 | 0.05 | 0.00 | |
| H3: Interaction effect | 0.10 | 0.05 | 0.05 | Supported |
|
| ||||
| Age to fear of COVID-19 | 0.14 | 0.08 | 0.07 | |
| Gender to fear of COVID-19 | 0.28 | 0.11 | 0.01 | |
| Education level to fear of COVID-19 | 0.06 | 0.08 | 0.42 | |
| Occupation to fear of COVID-19 | −0.08 | 0.05 | 0.14 | |
| Social media usage frequency to fear of COVID-19 | −0.12 | 0.07 | 0.09 |
*p = 0.05, **p = 0.01, and ***p = 0.001. Interaction effect = Fake news related to COVID-19 × perceived COVID-19 information overload.
FIGURE 2Interaction effect of fake news related to COVID-19 on social media platforms and perceived information overload on COVID-19 fear.