| Literature DB >> 32369026 |
Araz Ramazan Ahmad1,2, Hersh Rasool Murad3.
Abstract
BACKGROUND: In the first few months of 2020, information and news reports about the coronavirus disease (COVID-19) were rapidly published and shared on social media and social networking sites. While the field of infodemiology has studied information patterns on the Web and in social media for at least 18 years, the COVID-19 pandemic has been referred to as the first social media infodemic. However, there is limited evidence about whether and how the social media infodemic has spread panic and affected the mental health of social media users.Entities:
Keywords: COVID-19; Iraq; Kurdistan region; fake news; impact; infodemic; mental health; misinformation; panic; social media
Mesh:
Year: 2020 PMID: 32369026 PMCID: PMC7238863 DOI: 10.2196/19556
Source DB: PubMed Journal: J Med Internet Res ISSN: 1438-8871 Impact factor: 5.428
Sociodemographic variables of study participants (N=516).
| Variables | Participants, n (%) | ||
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| |||
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| Male | 294 (56.9) | |
|
| Female | 222 (43.0) | |
|
| |||
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| 18-35 | 336 (65.1) | |
|
| 36-50 | 149 (28.9) | |
|
| ≥51 | 31 (6.0) | |
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| |||
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| PhD | 43 (8.3) | |
|
| Master of Arts | 85 (16.5) | |
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| Higher diploma | 3 (0.6) | |
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| Bachelor | 261 (50.6) | |
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| Diploma | 65 (12.6) | |
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| High school | 35 (6.8) | |
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| Secondary school | 11 (2.1) | |
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| Primary school | 7 (1.4) | |
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| Just reading and writing | 6 (1.2) | |
The social media platforms used to get news about the coronavirus disease.
| Social media platforms | Participants (N=516), n (%) |
| 426 (82.6) | |
| 33 (6.4) | |
| 17 (3.3) | |
| Snapchat | 2 (0.4) |
| YouTube | 10 (1.9) |
| TikTok | 1 (0.2) |
| 6 (1.2) | |
| 3 (0.6) | |
| Telegram | 4 (0.8) |
| Skype | 1 (0.2) |
| Viber | 9 (1.7) |
| LINE | 2 (0.4) |
| 1 (0.2) | |
| VKontakte (VK) | 0 (0.0) |
| Badoo | 0 (0.0) |
| Myspace | 1 (0.2) |
The news topics classifications.
| News topics | Participants (N=516), n (%) |
| Social news | 14 (2.7) |
| Health news (COVID-19a) | 394 (76.4) |
| Technology news | 3 (0.6) |
| Economic news | 10 (1.9) |
| Sports news | 4 (0.8) |
| Miscellaneous news | 65 (12.6) |
| Political news | 20 (3.9) |
| Cultural news | 6 (1.2) |
aCOVID-19: coronavirus disease.
Descriptive statistics of questions.
| Questions | Value, mean (SD) | Coefficient of variation | Relative importance |
| Question 3: Do you think that publishing more news related to COVID-19a on social media has spread fear and panic among the people? | 2.68 (0.63) | 23.51 | 89.333 |
| Question 5: Do you think the level of Kurdish pages, groups, and accounts on social media covering COVID-19 is good? | 1.96 (0.88) | 44.9 | 65.333 |
| Question 6: Have you published any information and news related to COVID-19 on social media? | 2.18 (0.93) | 42.66 | 72.667 |
| Question 8: Filters need to be set up for social media and a specific policy followed during humanitarian crises such as the spread of the COVID-19. | 2.74 (0.62) | 22.63 | 91.333 |
| Total | 2.39 (0.765) | 33.425 | 79.667 |
aCOVID-19: coronavirus disease.
Impacts of fear on study participants (N=516).
| Impact scale | Participants, n (%) |
| Psychological | 199 (38.6) |
| Physical | 9 (1.7) |
| Physical psyche | 47 (9.1) |
| All of them | 75 (14.6) |
| I was not afraid | 186 (36.0) |
Categories of information shared on social media.
| Information | Participants (N=516), n (%) |
| Dissemination of the number of infections (A) | 90 (17.4) |
| Dissemination of the death toll (B) | 39 (7.6) |
| Dissemination of fear-inducing information about COVID-19a (C) | 56 (10.9) |
| Publication of photos and videos of the cities and countries with a high number of cases (D) | 78 (15.1) |
| Fake news about COVID-19 (E) | 137 (26.6) |
| Dissemination of the number of infections (A) and dissemination of the death toll (B) | 13 (2.5) |
| Dissemination of the number of infections (A) and dissemination of fear-inducing information about COVID-19 (C) | 4 (0.8) |
| Dissemination of the number of infections (A) and publication of photos and videos of the cities and countries with a high number of cases (D) | 9 (1.7) |
| Dissemination of the number of infections (A) and fake news about COVID-19 (E) | 7 (1.4) |
| Dissemination of the death toll (B) and dissemination of fear-inducing information about COVID-19 (C) | 3 (0.6) |
| Other | 80 (15.9) |
aCOVID-19: coronavirus disease.
Some questions according to the gender of participants (N=516).
| Variables | Male, n (%) | Female, n (%) | Total, n (%) | ||||
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| No | 25 (53.2) | 22 (46.8) | 47 (100.0) | |||
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| Neutral | 36 (51.4) | 34 (48.6) | 70 (100.0) | |||
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| Yes | 233 (58.4) | 166 (41.6) | 399 (100.0) | |||
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| No | 144 (68.3) | 67 (31.8) | 211 (100.0) | |||
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| Neutral | 49 (43.4) | 64 (56.3) | 113 (100.0) | |||
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| Yes | 101 (52.6) | 91 (47.4) | 192 (100.0) | |||
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| No | 133 (71.5) | 73 (28.5) | 186 (100.0) | |||
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| Neutral | 30 (60.0) | 20 (40.0) | 50 (100.0) | |||
|
| Yes | 151 (53.9) | 129 (46.1) | 280 (100.0) | |||
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| |||||||
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| No | 37 (75.5) | 12 (24.5) | 49 (100.0) | |||
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| Neutral | 22 (64.7) | 12 (35.3) | 34 (100.0) | |||
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| Yes | 235 (52.3) | 198 (45.7) | 433 (100.0) | |||
aCOVID-19: coronavirus disease.
Accounting some questions according to gender of participants (N=516).
| Variable | Gender | Total | |||||
|
| Male, n (%) | Female, n (%) |
| ||||
|
| |||||||
|
| 251 (58.9) | 175 (41.1) | 426 (100.0) | ||||
|
| 7 (21.2) | 26 (78.8) | 33 (100.0) | ||||
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| 10 (58.8) | 7 (41.2) | 17 (100.0) | ||||
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| Snapchat | 0 (0.0) | 2 (100.0) | 2 (100.0) | |||
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| YouTube | 6 (60.0) | 4 (40.0) | 10 (100.0) | |||
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| TikTok | 0 (0.0) | 1 (100.0) | 1 (100.0) | |||
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| 3 (50.0) | 3 (50.0) | 6 (100.0) | ||||
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| 3 (100.0) | 0 (0.0) | 3 (100.0) | ||||
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| Telegram | 3 (75.0) | 1 (25.0) | 4 (100.0) | |||
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| Skype | 1 (100.0) | 0 (0.0) | 1 (100.0) | |||
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| Viber | 7 (77.8) | 2 (22.2) | 9 (100.0) | |||
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| LINE | 1 (50.0) | 1 (50.0) | 2 (100.0) | |||
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| 1 (100.0) | 0 (0.0) | 1 (100.0) | ||||
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| Myspace | 1 (100.0) | 0 (0.0) | 1 (100.0) | |||
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| Social news | 12 (85.7) | 2 (14.3) | 14 (100.0) | |||
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| Health news (COVID-19) | 216 (54.8) | 178 (45.2) | 394 (100.0) | |||
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| Technology news | 2 (66.7) | 1 (33.3) | 3 (100.0) | |||
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| Economic news | 6 (60.0) | 4 (40.0) | 10 (100.0) | |||
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| Sport news | 3 (75.0) | 1 (25.0) | 4 (100.0) | |||
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| Miscellaneous news | 34 (52.3) | 31 (47.7) | 65 (100.0) | |||
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| Political news | 17 (85.0) | 3 (15.0) | 20 (100.0) | |||
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| Cultural news | 4 (66.7) | 2 (33.3) | 6 (100.0) | |||
aCOVID-19: coronavirus disease.
Accounting some questions according to age of participants (N=516).
| Variables | Age, n (%) | Total, n (%) | |||
|
| 18-35 years | 36-50 years | ≥51 years |
| |
|
| |||||
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| 283 (66.4) | 124 (29.1) | 19 (4.5) | 426 (100.0) | |
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| 28 (84.9) | 5 (15.2) | 0 (0.0) | 33 (100.0) | |
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| 10 (58.8) | 7 (41.2) | 0 (0.0) | 17 (100.0) | |
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| Snapchat | 2 (100.0) | 0 (0.0) | 0 (0.0) | 2 (100.0) |
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| YouTube | 4 (40.0) | 4 (40.0) | 2 (20.0) | 10 (100.0) |
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| TikTok | 1 (100.0) | 0 (0.0) | 0 (0.0) | 1 (100.0) |
|
| 3 (50.0) | 2 (33.3) | 1 (16.7) | 6 (100.0) | |
|
| 1 (33.3) | 0 (0.0) | 2 (66.7) | 3 (100.0) | |
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| Telegram | 1 (25.0) | 1 (25.0) | 2 (50.0) | 4 (100.0) |
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| Skype | 0 (0.0) | 1 (100.0) | 0 (0.0) | 1 (100.0) |
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| Viber | 2 (22.2) | 3 (33.3) | 4 (44.4) | 9 (100.0) |
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| LINE | 0 (0.0) | 1 (50.0) | 1 (50.0) | 2 (100.0) |
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| 0 (0.0) | 1 (100.0) | 0 (0.0) | 1 (100.0) | |
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| Myspace | 1 (100.0) | 0 (0.0) | 0 (0.0) | 1 (100.0) |
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| Social news | 9 (64.3) | 3 (21.4) | 2 (14.3) | 14 (100.0) |
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| Health news (COVID-19) | 266 (67.5) | 112 (28.4) | 16 (4.1) | 394 (100.0) |
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| Technology news | 3 (100.0) | 0 (0.0) | 0 (0.0) | 3 (100.0) |
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| Economic news | 4 (40.0) | 6 (60.0) | 0 (0.0) | 10 (100.0) |
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| Sport news | 2 (50.0) | 2 (50.0) | 0 (0.0) | 4 (100.0) |
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| Miscellaneous news | 41 (63.1) | 17 (26.2) | 7 (10.8) | 65 (100.0) |
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| Political news | 8 (40.0) | 6 (30.0) | 6 (30.0) | 20 (100.0) |
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| Cultural news | 3 (50.0) | 3 (50.0) | 0 (0.0) | 6 (100.0) |
aCOVID-19: coronavirus disease.
Variable description by age and gender.
| Demographics | Variable | Total, n (%) | ||||||
|
| Psychological, n (%) | Physical, n (%) | Psychological and physical, n (%) | All of them, n (%) | I was not afraid, n (%) |
| ||
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| Male | 111 (37.8) | 5 (1.7) | 24 (8.2) | 42 (14.3) | 112 (38.1) | 294 (100.0) | |
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| Female | 88 (39.6) | 4 (1.8) | 23 (10.4) | 33 (14.9) | 74 (33.3) | 222 (100.0) | |
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| Combined | 199 (38.7) | 9 (1.7) | 47 (9.1) | 75 (14.6) | 186 (36) | 516 (100.0) | |
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| 18-35 | 135 (40.2) | 6 (1.8) | 36 (10.7) | 43 (12.8) | 116 (34.5) | 336 (100.0) | |
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| 36-50 | 57 (38.3) | 2 (1.3) | 9 (6.0) | 23 (15.4) | 58 (38.9) | 149 (100.0) | |
|
| ≥51 | 7 (22.6) | 1 (3.2) | 2 (6.5) | 9 (29.03) | 12 (38.7) | 31 (100.0) | |
|
| Combined | 199 (38.7) | 9 (1.7) | 47 (9.1) | 75 (14.6) | 186 (36.0) | 516 (100.0) | |
Simple regression model analysis of a dependent variable (spreading panic about coronavirus disease) on the effects of social media on spreading panic about coronavirus disease and social media’s impact on mental health in the Kurdistan Region of Iraq.
| Model | Unstandardized coefficients |
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| |||||
| B | SE | |||||||
| Constant | 0.4456 | 0.219 | 4.865 | .001 | .8701 | .757 | 95.652 | <.001 |
| Social media | 0.6458 | 0.0588 | 11.532 | <.001 | N/Aa | N/A | N/A | N/A |
aNot applicable.