What Drives People to Share Misinformation on Social Media during the COVID-19 Pandemic: A Stimulus-Organism-Response Perspective
Abstract
:1. Introduction
2. Theoretical Background
2.1. Misinformation Sharing on Social Media
2.2. The Framework of Stimulus-Organism-Response
2.3. Stimuli: Social Media Dependencies
2.4. Organisms: Cognitive and Affective States
3. Research Model and Hypotheses
3.1. The Role of Affect
3.2. The Role of Social Media Dependency
3.3. The Antecedents of Positive Affect
3.4. The Antecedents of Negative Affect
4. Research Method
4.1. Data Collection and Sample
4.2. Instrument Development
4.3. Control Variables
5. Data Analysis and Results
5.1. Measurement Model Test
5.2. Structural Model Test
6. Discussion
6.1. Key Findings
6.2. Theoretical and Practical Implications
6.3. Limitations and Directions for Future Research
7. Conclusions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Measurement Instruments
Construct | No. | Item | References |
---|---|---|---|
Informational dependency | ID1 | I usually get information through social media during COVID-19. | [30] |
ID2 | I usually utilize information gained from social media during COVID-19. | ||
ID3 | I immediately update information received from social media during COVID-19. | ||
Social dependency | SD1 | I consider how to act with friends, relatives, or acquaintances in social media during COVID-19. | [30] |
SD2 | I get ideas about how to approach others from social media during COVID-19. | ||
SD3 | I have something to do with my friends by using social media during COVID-19. | ||
SD4 | I use social media to have fun with family or friends during COVID-19. | ||
SD5 | By using social media, I am a part of social events without having to be there during COVID-19. | ||
Perceived information timeliness | PIT1 | The information about COVID-19 on social media is current. | [47] |
PIT2 | The information about COVID-19 on social media is timely. | ||
PIT3 | The information about COVID-19 on social media is up to date. | ||
Perceived socialization | PS1 | I talk about things with others while using social media during COVID-19. | [51] |
PS2 | I feel like I belong to a community while using social media during COVID-19. | ||
PS3 | I meet interesting people while using social media during COVID-19. | ||
PS4 | I get peer support from others while using social media during COVID-19. | ||
Information overload | IO1 | I am often distracted by the excessive amount of information on social medial about COVID-19. | [2] |
IO2 | I find that I am overwhelmed by the amount of information that I process on a daily basis from social media about COVID-19. | ||
IO3 | I receive too much information regarding the COVID-19 pandemic to form a coherent picture of what’s happening. | ||
Social overload | SO1 | I care too much about my friends’ well-being on social media during COVID-19. | [57] |
SO2 | I deal too much with my friends’ problems on social media during COVID-19. | ||
SO3 | I care for my friends too much on social media during COVID-19. | ||
SO4 | I pay too much attention to my friends’ posts on social media during COVID-19. | ||
Positive affect | Participants were asked to rate the intensity of the emotion they experienced in a particular situation during COVID-19: PA1: desire; PA2: relaxation; PA3: happiness. | [39] | |
Negative affect | Participants were asked to rate the intensity of the emotion they experienced in a particular situation during COVID-19: NA1: sadness; NA2: fear; NA3: anger; NA4: shock. | [40] | |
Misinformation sharing | MIS1 | I have shared content related to the COVID-19 virus that I later found out was a hoax. | [2,36] |
MIS2 | I share content on COVID-19 even if sometimes I feel the content may not be correct. | ||
MIS3 | I share content on social media related to COVID-19 without checking facts through trusted sources. | ||
All items are using seven-point Likert scale, with 1 = strongly disagree; 7 = strongly agree. |
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Demographics | Count (%) | Demographics | Count (%) |
---|---|---|---|
Age | Education | ||
18–25 | 127 (32.3%) | High school or below | 23 (5.9%) |
26–30 | 104 (26.5%) | College | 285 (72.5%) |
31–40 | 128 (32.6%) | Graduate school or above | 85 (21.6%) |
41–50 | 19 (4.8%) | Information sharing frequency | |
More than 50 | 15 (3.8%) | Less than 3 times | 27 (6.9%) |
Gender | 3–10 times | 123 (31.3%) | |
Female | 225 (57.3%) | 11–20 times | 97 (24.7%) |
Male | 168 (42.7%) | More than 20 times | 146 (37.1%) |
Usage Experience | Working status | ||
Less than 6 months | 5 (1.3%) | Employed full time | 262 (66.7%) |
6 months–1 year | 10 (2.5%) | Student | 90 (22.9%) |
1–3 years | 55 (14%) | Self-employed | 31 (7.9%) |
4–6 years | 163 (41.5%) | Unemployed or retired | 6 (1.5%) |
7 years and above | 160 (40.7%) | Others | 4 (1%) |
Construct | Items | Mean | STD | Loading | CR | AVE |
---|---|---|---|---|---|---|
Informational dependency (ID) | ID1 | 6.03 | 0.96 | 0.85 | 0.88 | 0.71 |
ID2 | 5.67 | 1.11 | 0.81 | |||
ID3 | 5.90 | 1.02 | 0.86 | |||
Social dependency (SD) | SD1 | 5.97 | 1.15 | 0.80 | 0.90 | 0.64 |
SD2 | 5.77 | 1.22 | 0.80 | |||
SD3 | 5.66 | 1.17 | 0.76 | |||
SD4 | 5.76 | 1.32 | 0.86 | |||
SD5 | 5.36 | 1.38 | 0.76 | |||
Perceived information timeliness (PIT) | PIT1 | 6.13 | 1.02 | 0.83 | 0.87 | 0.69 |
PIT2 | 5.75 | 1.07 | 0.82 | |||
PIT3 | 5.96 | 1.00 | 0.85 | |||
Perceived socialization (PS) | PS1 | 6.13 | 0.96 | 0.74 | 0.82 | 0.52 |
PS2 | 5.12 | 1.28 | 0.73 | |||
PS3 | 5.64 | 1.04 | 0.70 | |||
PS4 | 5.28 | 1.17 | 0.74 | |||
Information overload (IO) | IO1 | 4.58 | 1.33 | 0.88 | 0.76 | 0.80 |
IO2 | 4.03 | 1.55 | 0.91 | |||
IO3 | 3.96 | 1.54 | 0.82 | |||
Social overload (SO) | SO1 | 5.83 | 1.11 | 0.78 | 0.88 | 0.65 |
SO2 | 5.22 | 1.28 | 0.78 | |||
SO3 | 5.32 | 1.31 | 0.82 | |||
SO4 | 5.52 | 1.17 | 0.84 | |||
Positive affect (PA) | PA1 | 6.12 | 0.95 | 0.83 | 0.88 | 0.70 |
PA2 | 5.77 | 1.03 | 0.84 | |||
PA3 | 5.87 | 1.06 | 0.84 | |||
Negative affect (NA) | NA1 | 5.26 | 1.30 | 0.82 | 0.88 | 0.65 |
NA2 | 4.64 | 1.42 | 0.83 | |||
NA3 | 4.75 | 1.46 | 0.78 | |||
NA4 | 5.09 | 1.31 | 0.81 | |||
Misinformation sharing (MIS) | MIS1 | 5.62 | 1.02 | 0.84 | 0.87 | 0.69 |
MIS2 | 5.70 | 1.00 | 0.81 | |||
MIS3 | 5.89 | 1.00 | 0.84 |
Construct | ID | SD | PIT | PS | IO | SO | PA | NA | MIS | Age | Gender | Education | Experience |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Informational dependency | 0.84 | ||||||||||||
Social dependency | 0.41 | 0.80 | |||||||||||
Perceived information timeliness | 0.53 | 0.28 | 0.83 | ||||||||||
Perceived socialization | 0.47 | 0.64 | 0.52 | 0.72 | |||||||||
Information overload | 0.06 | 0.06 | 0.06 | 0.16 | 0.87 | ||||||||
Social overload | 0.43 | 0.61 | 0.58 | 0.55 | 0.16 | 0.80 | |||||||
Positive affect | 0.46 | 0.44 | 0.48 | 0.55 | 0.07 | 0.55 | 0.84 | ||||||
Negative affect | 0.27 | 0.24 | 0.26 | 0.36 | 0.45 | 0.38 | 0.32 | 0.81 | |||||
Misinformation sharing | 0.50 | 0.48 | 0.42 | 0.52 | 0.12 | 0.58 | 0.55 | 0.37 | 0.83 | ||||
Age | 0.06 | 0.12 | 0.01 | 0.07 | -0.22 | 0.16 | 0.08 | -0.15 | 0.06 | 1.00 | |||
Gender | 0.10 | 0.02 | 0.05 | 0.05 | 0.05 | -0.02 | -0.02 | 0.20 | 0.02 | -0.20 | 1.00 | ||
Education | 0.12 | 0.10 | 0.15 | 0.15 | 0.17 | -0.06 | -0.02 | 0.16 | 0.06 | -0.29 | 0.15 | 1.00 | |
Usage experience | 0.21 | 0.23 | 0.16 | 0.18 | 0.01 | 0.08 | 0.13 | 0.11 | 0.14 | 0.10 | 0.02 | 0.15 | 1.00 |
Relationship | Direct Effect without Mediator | Direct Effect with Mediator | Mediation Effect |
---|---|---|---|
PIT→PA→MIS | 0.147 * | 0.084 n.s. | Full mediation |
PS→PA→MIS | 0.219 ** | 0.140 * | Partial mediation |
IO→NA→MIS | 0.003 n.s. | −0.036 n.s. | No mediation |
SO→NA→MIS | 0.401 *** | 0.304 *** | Partial mediation |
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Wu, M. What Drives People to Share Misinformation on Social Media during the COVID-19 Pandemic: A Stimulus-Organism-Response Perspective. Int. J. Environ. Res. Public Health 2022, 19, 11752. https://doi.org/10.3390/ijerph191811752
Wu M. What Drives People to Share Misinformation on Social Media during the COVID-19 Pandemic: A Stimulus-Organism-Response Perspective. International Journal of Environmental Research and Public Health. 2022; 19(18):11752. https://doi.org/10.3390/ijerph191811752
Chicago/Turabian StyleWu, Manli. 2022. "What Drives People to Share Misinformation on Social Media during the COVID-19 Pandemic: A Stimulus-Organism-Response Perspective" International Journal of Environmental Research and Public Health 19, no. 18: 11752. https://doi.org/10.3390/ijerph191811752