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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
The aim of this study is to determine how social media affects self-reported mental health and the spread of panic about COVID-19 in the Kurdistan Region of Iraq.
To carry out this study, an online questionnaire was prepared and conducted in Iraqi Kurdistan, and a total of 516 social media users were sampled. This study deployed a content analysis method for data analysis. Correspondingly, data were analyzed using SPSS software.
Participants reported that social media has a significant impact on spreading fear and panic related to the COVID-19 outbreak in Iraqi Kurdistan, with a potential negative influence on people’s mental health and psychological well-being. Facebook was the most used social media network for spreading panic about the COVID-19 outbreak in Iraq. We found a significant positive statistical correlation between self-reported social media use and the spread of panic related to COVID-19 (
During lockdown, people are using social media platforms to gain information about COVID-19. The nature of the impact of social media panic among people varies depending on an individual's gender, age, and level of education. Social media has played a key role in spreading anxiety about the COVID-19 outbreak in Iraqi Kurdistan.
The coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus [
Prior to the outbreak of COVID-19, people already relied on social media to gather information and news, and since the outbreak in December 2019, people in many countries have relied on social media to obtain information about the virus. In addition, people in Iraqi Kurdistan depend on social media. Internet use is strongly associated with behaviors related to health information; users write about their health on various social media platforms [
At the time of writing, the global spread of COVID-19 is still a rapidly evolving situation. The Kurdistan Regional Government (KRG) created a webpage [
According to statements from the KRG’s Ministry of Health, as of April 10, 2020, the total number of confirmed cases is 324, including 3 deaths, 134 recovered patients, and 187 active cases [
The first study on social media during a pandemic dates back to the 2009 H1N1 pandemic, tracking the prevalence of misinformation (determined as 4.5%), terminology use ("H1N1" versus "swine flu"), public sentiments and fear, and relationships between case incidence and public concern [
ABC News reported a poll claiming that in the age of social media, anxiety about the coronavirus spreads faster than the virus itself, resulting in public panic worldwide [
Brewer on BBC News [
After COVID-19 appeared and was transmitted to other countries outside of Mainland China, people turned to social media to know more about the virus. According to Molla [
The mass media has been called on to take responsibility for providing correct information and aiding comprehension among citizens [
Victor [
In a contemporary discussion on the effects of media, one researcher [
Merchant and Lurie [
Mian and Khan [
Little or no evidence is available on the perception and impact of social media during this pandemic, in particular within non-Western communities such as Iraqui Kurdistan.
In this study we used a quantitative survey methodology to obtain data from Kurdish social media users. The questionnaire was prepared in the Kurdish language, and 516 social media users were sampled to collect the data. A descriptive content analysis was used to analyze the data. SPSS Version 25 (IBM Corp) was used to categorize and test the results. The social media users participated in a random online questionnaire, which aimed to determine the impact of social media on the spreading of panic about the COVID-19 outbreak, as well as social media’s impact on people’s mental health and the health crisis facing countries worldwide.
The first question in this study asked participants “Which social media platform do you use to get news and information about COVID-19?” As shown in
The second question was “What news topics have you mostly heard/seen/read on social media during these three months of 2020?” As shown in
Sociodemographic variables of study participants (N=516).
Variables | Participants, n (%) | ||
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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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|||
|
PhD | 43 (8.3) | |
|
Master of Arts | 85 (16.5) | |
|
Higher diploma | 3 (0.6) | |
|
Bachelor | 261 (50.6) | |
|
Diploma | 65 (12.6) | |
|
High school | 35 (6.8) | |
|
Secondary school | 11 (2.1) | |
|
Primary school | 7 (1.4) | |
|
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.
Cronbach alpha was used to determine the reliability of the study; its value was .825 and the validity was 0.753, indicating that the study questionnaire is highly reliable. Reliability refers to the accuracy, dependability, stability, and consistency of the research instrument. The recommended appropriate sample size is “approximately 200 individuals (or more) for a research” [
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) |
Participants in this study were also asked, “Which category of information has had the most impact on creating panic on social media?” As shown in
The responses to Questions 3, 6, and 8 (
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) | |||
|
Yes | 101 (52.6) | 91 (47.4) | 192 (100.0) | |||
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|
No | 133 (71.5) | 73 (28.5) | 186 (100.0) | |||
|
Neutral | 30 (60.0) | 20 (40.0) | 50 (100.0) | |||
|
Yes | 151 (53.9) | 129 (46.1) | 280 (100.0) | |||
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|
No | 37 (75.5) | 12 (24.5) | 49 (100.0) | |||
|
Neutral | 22 (64.7) | 12 (35.3) | 34 (100.0) | |||
|
Yes | 235 (52.3) | 198 (45.7) | 433 (100.0) |
aCOVID-19: coronavirus disease.
According to the results shown in
As shown in
As shown in
Accounting some questions according to gender of participants (N=516).
Variable | Gender | Total | |||||
|
Male, n (%) | Female, n (%) |
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251 (58.9) | 175 (41.1) | 426 (100.0) | ||||
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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) | |||
|
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) | ||||
|
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) | |||
|
LINE | 1 (50.0) | 1 (50.0) | 2 (100.0) | |||
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1 (100.0) | 0 (0.0) | 1 (100.0) | ||||
|
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) | |||
|
Health news (COVID-19) | 216 (54.8) | 178 (45.2) | 394 (100.0) | |||
|
Technology news | 2 (66.7) | 1 (33.3) | 3 (100.0) | |||
|
Economic news | 6 (60.0) | 4 (40.0) | 10 (100.0) | |||
|
Sport news | 3 (75.0) | 1 (25.0) | 4 (100.0) | |||
|
Miscellaneous news | 34 (52.3) | 31 (47.7) | 65 (100.0) | |||
|
Political news | 17 (85.0) | 3 (15.0) | 20 (100.0) | |||
|
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 (%) | |||
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18-35 years | 36-50 years | ≥51 years |
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283 (66.4) | 124 (29.1) | 19 (4.5) | 426 (100.0) | |
|
28 (84.9) | 5 (15.2) | 0 (0.0) | 33 (100.0) | |
|
10 (58.8) | 7 (41.2) | 0 (0.0) | 17 (100.0) | |
|
Snapchat | 2 (100.0) | 0 (0.0) | 0 (0.0) | 2 (100.0) |
|
YouTube | 4 (40.0) | 4 (40.0) | 2 (20.0) | 10 (100.0) |
|
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) | |
|
Telegram | 1 (25.0) | 1 (25.0) | 2 (50.0) | 4 (100.0) |
|
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) |
|
LINE | 0 (0.0) | 1 (50.0) | 1 (50.0) | 2 (100.0) |
|
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) |
|
Health news (COVID-19) | 266 (67.5) | 112 (28.4) | 16 (4.1) | 394 (100.0) |
|
Technology news | 3 (100.0) | 0 (0.0) | 0 (0.0) | 3 (100.0) |
|
Economic news | 4 (40.0) | 6 (60.0) | 0 (0.0) | 10 (100.0) |
|
Sport news | 2 (50.0) | 2 (50.0) | 0 (0.0) | 4 (100.0) |
|
Miscellaneous news | 41 (63.1) | 17 (26.2) | 7 (10.8) | 65 (100.0) |
|
Political news | 8 (40.0) | 6 (30.0) | 6 (30.0) | 20 (100.0) |
|
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 (%) | ||||||
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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) | |
|
Female | 88 (39.6) | 4 (1.8) | 23 (10.4) | 33 (14.9) | 74 (33.3) | 222 (100.0) | |
|
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) | |
|
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) |
It is noted from
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.
As media professionals working at a public university in the Kurdistan Region of Iraq, we conclude from the study results that social media has played a significant role in affecting the public during the COVID-19 crisis. The regression analysis of the study indicates that there is a significant positive statistical correlation (
One could argue that the panic caused by widespread information about COVID-19 in the Kurdistan Region of Iraq is worse than the number of COVID-19 cases and will have a longer-lasting effect. It is important to communicate this to health professionals in the region and for media experts to work with these professionals to ensure that only well-vetted information is disseminated to the public. It is also important to engage the Ministry of Health and the Ministry of Education in this effort to be prepared for future epidemics or health situations. This pandemic has certainly helped the authors identify the need for educating consumers on health topics found through social media.
There were various research limitations, most importantly these are self-reported data from self-selected participants, and the lockdown period was a constraint to gather more representative data. It was difficult to find participants who wished to participate in this study.
As media experts and educators, we have an important role to play both now and in the future of Kurdistan. We must work to educate media consumers on what constitutes good and reliable information and how to critically think through this information. Since younger people are also consuming information from social media and then spreading it to their family and friends, universities are ideal places to design courses and symposiums that can help students and faculty discern how to search for, find, and evaluate health information in the case of an epidemic or pandemic.
coronavirus disease
Kurdistan Regional Government
World Health Organization
With the outbreak of this pandemic, the world has suffered from COVID-19. Our interest lies in the heart of developing knowledge; thus, the idea for this study was born. Here, the authors would like to extend our gratitude to everyone who was part of our research community, as their views have highly enriched our study.
In addition, we extend our acknowledgment to Dr Paiman Ahmad for reviewing this manuscript prior to submission and at the final editing phase. No funding was provided for conducting this study.
None declared.