Abstract

During natural disasters, the dependency of people for information increases by using different media. Due to the current importance of social sites, there’s a growing tendency for people to consume news from social media sites rather than traditional media during the COVID-19 crisis. People were receiving news from across the world and were accepted as real news. The current study aims to know if the news dissemination during COVID-19 had well informed the public or had created panic among people. The findings of the study revealed that respondents were using Facebook and Twitter more than 4 hours daily where Facebook remained the primary source (61%) of information for most of the respondents. However, it also stayed as the main source of panic (85%) among them. Twitter in this regard played more positive i.e. 59% than a negative role i.e. 15% which means that Twitter has informed people rather than creating panic.    

Key Words

COVID-19 Crises, Facebook, Twitter, Information/Panic

Introduction

It is obvious that the 2019 coronavirus (2019-nCoV) or extremely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread rapidly from China i.e. Wuhan to many other Chinese cities and to the rest of the world. This is because the virus is extremely contagious and can replicate within human cells (Singhal, 2020). Around the world, more than 576,274,573  people have contracted this deadly and life-threatening illness, which has resulted in 6,405,377 losses.

Bastani & Bahrami, (2020) investigated that

during a breakout of any virus the general population requires precise and accurate information about the disease's symptoms and how to prevent themselves. During the COVID-19 crisis, people frequently rely on social media as a platform for quick and effective searching, spreading and sharing of health-related information with the public (Y. Zhao & Zhang, 2017).

The COVID-19 epidemic, which originated in China, had a severe impact on the countries that border Pakistan, including China. Iran followed Italy in having the highest coronavirus mortality rate in the northern hemisphere. Italy had the highest COVID-19 mortality rate in the western hemisphere. Iran also had the highest death rate in the eastern region. Pakistani Health Ministry acknowledged the first COVID-19 case on February 26, 2020, in Karachi, Sindh Province Pakistan (Saqlain, Munir, Ahmed, Tahir, & Kamran, 2020). While The Federal Ministry of Health of Pakistan confirmed a second case the same day (Waris, Atta, Ali, Asmat, & Baset, 2020). Gilgit-Baltistan had the second-highest number of confirmed cases. All confirmed cases have recently travelled histories from Iran, Syria, or London. Currently, the situation is extremely precarious and the increasing number of cases is at an alarming rate (Yousaf, Zahir, Riaz, Hussain, & Shah, 2020). Due to Pakistan's location and the increasing number of individuals who have tested positive for COVID-19, a higher level of management, was necessary. And it is, therefore, on February 12, The Pakistan National Action Plan for Coronavirus Disorder was submitted by the Pakistani Ministry of National Health Services, Regulation, and Coordination (Covid-19). In order to provide a rapid, efficient, and efficient reaction to possible Covid-19 incidents, this plan seeks to both prevent the transmission of the virus and increase local and national emergency preparedness. 

On Monday, April 6, 2020, 3,277 confirmed positive cases were reported by the Health Ministry of the Pakistani Government throughout the country. This number included 18 cases deemed to be critical and 50 fatalities. Punjab had 1,493 cases, Sindh had 881, Khyber Pakhtunkhwa had 405, Balochistan had 191, Gilgit Baltistan had 210, the Federal had 82, and Azad Jammu and Kashmir had confirmed 15 cases. The findings are presented in graphical form. Khyber Pakhtunkhwa has recorded the highest number of fatalities to date, with 16. This is followed by Punjab (15), Sindh (15), Balochistan (3), and Gilgit Balochistan (2). (3). (1). Eighty-five infected individuals were discovered in Sindh, thirty in KP, seventeen in Balochistan, and twenty-five in Punjab. Gilgit Baltistan had nine and AJK has each discovered a single case so far. 1.3% is the mortality rate in Pakistan while 4.8% is recorded in its recovery rate. (Waris et al., 2020).

Social Media During Covid-19

Throughout the COVID-19 pandemic, social networking sites became platforms for combating social isolation and ensuring one's own survival (Alzoubi et al., 2020). During the pandemic, Facebook emerged as an important hub for connecting people in need with resources and other individuals who could provide assistance (H. Ouyang, 2020). Skoll, Miller, & Saxon, (2020) shared their personal experience and stated, "During the pandemic, we had been using my laptop to browse Facebook in order to find firsthand stories from medical professionals in China, Iran and Italy”.  Facebook became a source of information regarding the coronavirus outbreak for one hundred thousand healthcare professionals. Facebook, for instance, was utilized as a source of information about the Covid-19 spread. Throughout the crisis, this was analyzed that social networks were also used for preaching and providing spiritual support for Bible and prayer lessons. This was one way in which social networks were utilized (Almahasees & Jaccomard, 2020).

Due to the daily and frequent online interaction of people through social networking sites continues, there is a growing tendency for people to seek and consume news from social networking sites rather than traditional media. This shift in consumption patterns is an inevitable consequence of these social media platforms' characteristics. It is easier to comment on, share, and discuss the news with friends or other readers on social networking sites; and (ii) it is frequently more timely and less expensive to consume news on social networking sites as opposed to mainstream news media such as television or newspapers. 

In 2016, 62% of adults in the United States accessed news via social networking sites, whereas in 2012, only 49% of adults reported doing so (Afroz, Brennan, & Greenstadt, 2012). In addition, social media platforms have surpassed television as the primary source of news (Allcott & Gentzkow, 2017). Despite the numerous advantages of social networking platforms, the 

quality of the news on these sites is inferior to that of traditional news outlets. Due to the low cost of providing news online and the faster and 

easier distribution through social networking platforms, however, a large number of fake news articles, which are defined as news articles that provide false information on purpose, are produced online for a variety of reasons, including political and financial interests. This has led to an increase in online misinformation prevalence. By the time the presidential election concluded, it is estimated that there had been more than one million tweets related to the fake news story known as "pizzagate"(Gerber, Huber, & Washington, 2010). The dissemination of fake news is intended to persuade utilized to accept false or biased beliefs. Fake news is frequently used by propagandists to communicate political messages or exert influence. For instance, there have been reports indicating that Russia has created fake social media accounts and bots to spread false information (Etzioni, Banko, Soderland, & Weld, 2008). Thirdly, the spread of fake news alters how individuals comprehend and respond to real news. Some fake news, for example, is created solely for the purpose of provoking people's mistrust, which causes them to mistrust. They are integrated, making it harder to distinguish between the real and the unreal (Bessi & Ferrara, 2016).

Literature Review

Digital media has developed as a crucial new source of health information and a place for individuals to contribute their individual ideas, experiences, and concerns around health, disease, and treatment (Gold, 2020). There were 19 million references about the coronavirus on new media within twenty-four hours after the virus's debut and subsequent spread to countries outside of mainland China (Molla, 2020). This is due to the fact that everyone desired to understand more about the virus.

People were under a lot of stress and danger to their health as a result of the coronavirus epidemic. As a result, more people are utilizing online media to get information and updates about health-related pandemics (Agius, Grech, & Grech, 2020). Accurate health information and 

maintaining connections with friends, family, and peers are priorities for most people (J. Zhao et al., 2020).

As a result of the coronavirus pandemic outbreak, people's lifestyles have had to change in order to take precautionary measures (Gever, Talabi, Adelabu, Sanusi, & Talabi, 2021). Due to the current global health crisis, there is an increased awareness of the significance of preventing infectious diseases caused by the coronavirus by promoting healthy behaviour modifications such as using disinfectants, maintaining a social distance, wearing masks, and washing one's hands (Albashtawy et al., 2016).

People from the general population who seek health-related information online can receive social support from numerous social media sites. People with health anxiety and other medical disorders have a huge advantage due to the social media-facilitated availability of trustworthy information on the internet. Additionally, it permits individuals to share both their ideas and experiences (A. Ouyang, Inverso, Chow, Kumar, & Zhong, 2016).

The positive and important contribution of social media to the distribution of health information at all levels, from peer assistance to the general public, has aided the spread of health information (Fang, Wang, Wen, & Zhou, 2020). The phrase "peer support" refers to the educational assistance provided by persons who facilitate the exchange of experiences with the goal of disseminating health knowledge about the coronavirus pandemic (also known as the COVID-19 epidemic) (Tan, Rehm, Stevenson, & De Foe, 2021). 

According to González-Padilla & Tortolero-Blanco, (2020), digital media has evolved into a tool that allows people to contact friends and family even when they are alone. This reduces the negative impacts of solitude, such as tension, anxiety, and dread. In addition, it makes it simpler to rapidly transmit vital information, identify symptoms, debate treatments, adopt control techniques from other countries, and adapt them to work with readily available resources. Students who have recently used digital media to continue their studies or who have been isolated while hospitalized or at home are ideally suited to utilize social media platforms, as they are regarded as an important 

and effective means of disseminating trustworthy information to the general public. During the rapid spread of the COVID-19 epidemic, people turned to new media outlets to gain more information on the virus (Bao, Cao, Xiong, & Tang, 2020).

However, the proliferation of fake news and disinformation on social networking sites about pandemics is always causing concern (Sommariva, Vamos, Mantzarlis, ?ào, & Martinez Tyson, 2018). 

Users almost never verify the accuracy of the information they provide. Because each social media post is accompanied by its popularity, social media also makes the trending heuristic more apparent(Sundar, 2008). When a post has a big number of likes, shares, or comments, it has a greater chance of getting discovered by others, and thus, it has a greater probability of receiving further likes, shares, or comments (Thorson, 2008). Therefore, new media popularity is a self-reinforcing cycle that considerably helps the dissemination of content that has not been objectively verified

The bulk of citizens turned to new media during the COVID-19 pandemic for relevant data, sharing and posting rumours, false information, potentially fatal consequences of a purported treatment, aetiology, preventive measures, vaccination, and conspiracies about the virus's origins (Torales, O’Higgins, Castaldelli-Maia, & Ventriglio, 2020). Therefore, the COVID-19 pandemic has produced numerous social concerns (Lwin et al., 2020). False information spreads more rapidly than truthful information, hence compromising the legitimacy and equilibrium of the system(Radwan, Radwan, & Radwan, 2020). For example, the spread of false information on digital media leads to an increase in the number of people purchasing large quantities of masks, soap, toilet paper, medicines, food, etc., behaviours that cause market imbalances and have an effect on other industries in these nations (González-Padilla & Tortolero-Blanco, 2020).

In addition, some individuals published or shared social media messages about empty supermarkets, marketplaces, and pharmacies, which contributed to the spread of panic associated with shortages of essential goods such as food, medicine, and detergents. This terror was brought on by the inaccessibility of these items (Loxton et al., 2020). During the coronavirus crisis, new media has arisen as a significant arena for the sharing of personal experiences, views, and opinions, as well as the debate on issues related to this new disease, such as potential treatments and symptoms. According to the findings of the research conducted by Merchant and Lurie (Loxton et al., 2020), modern technological technologies, such as social networking platforms, are employed to provide either correct or false information to users. People rely on social networking channels to learn more about the COVID-19 pandemic in countries where they did not receive information on the outbreak. This is because several governments failed to inform the public about the outbreak (La et al., 2020). In contrast, in a number of countries, successful utilization of social media platforms was demonstrated. In these nations, the appropriate authorities established official social media profiles to disseminate exact information regarding the COVID-19 epidemic. This is a wonderful illustration of my social media management accomplishment. 




Problem Statement

Social networking sites have a crucial role in spreading news and information among the masses.  Moreover, people show their dependency on media for information and satisfaction during a period of crisis. During the crisis of coronavirus, people were hugely dependent on social media sites, and they were receiving information from everywhere and at the same time were accepting the truth too, like 

releasing 180 lions in the streets of Moscow to Coronavirus dies in the stomach. The current study investigates the role of social media platforms (Facebook & Twitter) in terms of information or panic during the COVID-19 crisis by using the survey research technique.




Significance of the Study

People have already been diverted from traditional media to social media for information and news however amid the covid-19 crisis more people have started utilizing social networking sites for news and updates about the deadly virus. On one hand, while people were receiving information about the pandemic on the other hand they were also facing the issue of misinformation about the virus which ultimately created panic and stress intentionally and unintentionally among the consumer of social networking platforms which may harm the mental and psychological health of the public. This study will evaluate the extent to which social media has informed the users or created stress & panic which may be helpful to regulate the information flow of social media.




Research Objectives

Objectives of this research are:

1. To investigate the frequency of social media, use among private school teachers of Peshawar. 

2. To analyze the role of social media i.e. Facebook & Twitter as a source of information or panic, stress and unrest among users.

3. To evaluate which social media site has been preferred for updates/news/information during the COVID-19 crisis.




Hypothesis

1. There is a significant correlation between age and panic, which the information regarding COVID-19 creates via social media.

2. There is a significant positive correlation between age and social media use

3. Private school teachers are more likely to use Facebook for information/updates than Twitter during Covid-19.

4. Facebook spread more fear, panic and stress among users than Twitter.




Theoretical Framework

Cultivation Theory

For the first time, Gerbner and Larry Gross 1976

expanded the cultivation theory (Bilandzic & Rössler, 2004) 2018. In 1973, Gerbner developed his mass communication paradigms, which have three analysis methods. "Cultivation analysis," often known as "longitudinal surveys of peoples," is indeed the third form of analysis. Views on specific subjects with levels of media receipt, such as tv watching, as the main variable. This study is called the Cultivation Theory.

The cultivation theory was developed as a way to investigate the impact of television on viewers, specifically the impact of seeing violent content on television (Settle, 2018). The primary principle of the idea is that "the more people spend more time "living" in the entertainment industry, the more probable it is that they will assume social reality coincides with realism portrayed by the media." (Riddle,  2009)

The long-term consequences of watching television on viewers are a topic of discussion in cultivation theory. The ability of television to shape specific perspectives on particular issues and general beliefs about the world, according to this line of thinking, is one of the greatest dangers posed by the medium. In the past six years, the field of mass communication has witnessed the proliferation of cable television, satellite television, video games, and most recently, social media. It appears that the theory has thus far been able to withstand ongoing modifications and enhancements. Since the year 2000, over 125 studies have provided support for the theory demonstrating its adaptability to the ever-changing media landscape (Gerbner & Gross,  2017).  

Social media exposure significantly increases the perception of risk in society compared to traditional media coverage. While earlier studies concentrated on traditional media sources, the latest researchers, representing the current trend of rapid information exchange, paid closer attention to the information obtained from social media (Ng, Yang, & Vishwanath,  2018).

According to the cultivation theory, people who spend more time with media have a more significant impact on how others perceive them than those who spend less time with it. Even worse, they would develop mean world syndrome, which uses to ignore reality and think that people and the world are more dangerous than they actually are. It would indeed be fascinating to see if the recent pandemic events caused people to start exhibiting symptoms of Sick World Syndrome.

 It would be interesting to see if people started experiencing Sick World Syndrome as a result of the recent pandemic events and if that could be seen as both a negative and a motivating factor for preventive measures.Social media use was positively correlated with the fear of contracting the H1N1 virus. In terms of COVID-19, similar conclusions were reached by (Garfin,  Silver, & Holman, 2020). Increased stress and anxiety can cause overreaction inaccuratese.lfhealth assessments, andultimately, serious health resource issues on a national scale. The younger generation, who is more likely to disregard warnings and restrictions, is particularly well-versed in using social networking sites.(Setbon & Raude,  2010).

Research Methodology

The current study used a quantitative research design by conducting a cross-sectional approach for the data collection process. The researcher employed a survey, using simple random sampling, among the private school teachers of Hayatabad Peshawar using a structured questionnaire. Three sections of the questionnaire were made i.e. Section 'A' contained demographic details of the respondents,gender, age, & level of education. Part 'B' included questions on how often social media is used for Coronavirus updates, likewise section 'C' discuss the role of social media sites. The questionnaire is designed following the five-point Likert scale, which ranges from 0 (strongly disagree) to 5(strongly disagree) (Likert, 1967). 

According to Private Schools Regulatory Authority (PSRA), the number of Private schools in Hayatabad is 69 with a total strength of 1950 teachers (Population of the study). The sample was randomly taken from 69 schools in Hayatabad Peshawar. The researcher used Dr John Curry's formula for calculating the sample size for this study. The size came out to be 97 however, the researcher conducted a survey of 100 respondents. According to Dr John Curry's formula.

1. If the population size is between 10 and 100, then 100% of the population will be sampled.

2. If the population is between 101 and 1000, then 10% of the population will be sampled.

3. The sample percentage will be 5% if the population size is between 1,001 and 5000.

4. The sample percentage will be 3 per cent if the population is between 5,001 and 10,000.

5. The proportion of the sample will be 1 per cent if the population is more than 10,000.

Data Analysis

In this part of the study, the researcher analyzed the results obtained and briefly discussed the data and statistics in the table.




 

Table 1. Demographic Information of the Respondents

Age	Frequency	Gender	Frequency	Education	Frequency
21-23	6	Male	38	FA/ FSc	1
24-26	28	Female	62	Bachelor	21
27-29	27	-	-	MA/MSc	58
30+	39	-	-	MPhil/MS	18
-	-	-	-	PhD.	2
	Total	100	100		100




Explanation

The table above reports the demographic information (age, gender and education) of the respondents. The researcher discovered that majority of the respondents, using social media, was of the age 30+ i.e. 39% followed by 24-26, 27-29 and 21-23 which is 28%, 27% and 6% respectively. The table shows that out of the total 100 respondents 62 were female and 38 were male. The table also reveals the statistics regarding education which is 58 (58%) of the respondents got an MA/MSc degree followed by Bachelor, MPhil/MS, PhD and FA/FSc being 21%, 18%,2% & 1% respectively.




 

Table 2. Time Spent online during covid-19

How much time normally you spent online during covid-19?	Frequency	Per cent
Less than 1 hour---- 2 Hours	15	15.0
More than 2----4 hours	29	29.0
More than 4 hours	56	56.0
Total	100	100.0

 




Explanation

The table reports the frequency of time spent by a respondent online during Covid-19. According to the table most of the respondents i.e. 56 beings (56%) spent more than 4 hours/per day online during Covid-19 while 29 beings (20%) spent more than 2-4 hours per day while 15 respondents spent less than 1-2 hours.




 

Table 3. Social Media Accounts Information of the Respondents

Which among, Facebook & Twitter, do you have an account on?	Frequency	Per cent
Facebook	45	45.0
Twitter	5	5.0
Both	50	50.0
Total	100	100.0

 




Explanation

The current table gives information on the respondents' accounts on social media. The said table reveals the frequencies of the respondents' who have accounts on social media and participated in the survey. The majority of the respondents i.e. 50 told that they have accounts on both Facebook and Twitter. 45% of the respondents revealed that they have an account on Facebook however 5% have been found to have a Twitter account.




 

Table 4. Preferred Social Media Accounts (Facebook or Twitter) Source of updates/Information

Which among both did you prefer the most for updates/information regarding COVID-19?	Frequency	Per cent
Facebook	61	61.0
Twitter	39	39.0
Total	100	100.0

 




Explanation

This table shows the respondents’ preference among Facebook & Twitter for updates/information during Covid-19. Mostly the respondents i.e., 61% were of the opinion that they used Facebook for updates/information while 39% use Twitter for the same.




 

Table 5. Frequency Table of Social Media Sites between the Last Two Items

	Had spread positive, real and accurate information and had played a positive role during the Covid-19	Had spread panic, fear and stress through spreading fake information and had played a negative role during the Covid-19
	Frequency	Frequency
Facebook	15	85
Twitter	59	41
Total	100	100




Explanation

The above table shows the statistics regarding the role of Facebook and Twitter during COVID-19 i.e. positive or Negative. The data reveals that Facebook has played negative role than a positive one as 85% of the respondents were of the view that Facebook had spread panic, fear and stress through spreading fake information and had played a negative role during the Covid-19, however, on the other hand, Twitter is found to have played a positive role during the same period. 59% of the respondents told that it had spread positive, real and accurate information and had played a positive role during Covid-19.




 

Table 6. Correlations Table of age and Information spread panic

		Your Age	Information Spread Panic
your age	Pearson Correlation	1	-.011
	Sig. (2-tailed)		.911
	N	100	100
Information spread panic.	Pearson Correlation	-.011	1
	Sig. (2-tailed)	.911
	N	100	100

 




Explanation

Table 6 shows the Correlation results between the age and Information spread panic item. it is found that the Correlation between the age and Information spread panic item is -0.011 with a p-value is 0.911, which indicates that age is negatively insignificant correlated with Information spread panic.




 

Table 7. Correlations Table of age and Frequency of social media.

		Your Age	Frequency of Social Media
your age	Pearson Correlation	1	-.072
	Sig. (2-tailed)		.477
	N	100	100
Frequency of social media	Pearson Correlation	-.072	1
	Sig. (2-tailed)	.477
	N	100	100

 




Explanation

Table 7 shows the Correlation results between the age and Frequency of social media items. it is found that the Correlation between the age and Frequency of social media items is -0.072 with a p-value is 0.477, which indicates that age is negatively insignificant correlated with the Frequency of social media.

Discussion & Conclusions

The global environment has been permanently

 changed by the COVID-19 pandemic. All aspects of life have been affected negatively by the lockdown (Bao, Sung, & Kwon,  2021). The current study aimed to investigate the role of social networking sites during COVID-19, to know if the social networking sites spread the news for the sake of information or panic and to analyze the social media sites that have been frequently used for updates/news/information during COVID-19 crisis. The data analysis of this study revealed that the majority of the teachers got help with information/updates regarding Covid-19 through using different social media applications. Female teachers compared to males had been frequently using these applications (Facebook & Twitter etc) throughout the day in order to get themselves updated regarding COVID-19. The researcher found similar results to (Radwan, Radwan, & Radwan,  2020) that the respondents were using more than 4 hours daily on different social media applications in order to satisfy their needs however, the information received via different social sites have been found to play a negative role during this period by creating panic more than creating awareness among its users.

The data also revealed that the respondents (school teachers) used both Facebook and Twitter for information and updates however, Facebook is found to be a frequently used social site among them. This finding is In-line with the results of (Radwan & Radwan, 2020) who found that majority of their respondents were using Facebook for information regarding Covid-19, however, in the case of this study, Facebook has been seen to be a more panic-creating application than Twitter. 

This study aims to investigate the frequency of social media use among private school teachers of Peshawar also to analyze the role of social media i.e. Facebook & Twitter as a source of information or panic, stress and unrest among the users and to evaluate the preferred social media site for updates/news/information during COVID-19 crisis

The researcher thus hypothesized that 

1. There is a significant correlation between age and panic, which the information regarding COVID-19 creates via social media.

2. There is a significant positive correlation between age and social media use

3. Private school teachers are more likely to use Facebook for information/updates than Twitter during Covid-19.

4. Facebook spread more fear, panic and stress among users than Twitter.

The researcher, in light of the above hypothesis, from the data analysis of this study, found that the age of the respondents was negatively correlated with the panic created through the information regarding COVID-19 i.e -0.011 (H1). This means that as long as the age of the respondents increases the lesser information received regarding COVID-19 via social media creates panic among them. The researcher also found a negative correlation between age and social media use i.e. -0.072 (H2) which means that the more the age of the respondent increases the lesser they use social media which also means that young teachers were more likely to use social media more than the aged ones. The researcher found that 61% of the total respondents preferred Facebook for information/updates over Twitter during Covid-19 (H3) however; Facebook is also seen to be a major tool for spreading panic i.e. 85% compared to its informative role i.e. 15% (H4). Twitter has been seen to play more positive i.e. 59% than a negative role i.e. 15%. This means that Twitter has informed people rather than creating panic among them.

The above discussion led the researcher to conclude that private school teachers in Peshawer (KPK, Pakistan) are frequent users of different social media sites including Facebook and Twitter. They spent more than 4 hours daily on these sites during the COVID-19 crisis and seek information and help regarding this problem. Social media during the period of the COVID-19 crisis played both positive and negative roles. On the one hand, it informed 

people regarding every issue that happened across the world while on the other hand, people remained prey to the fake news it spread which eventually created panic and unrest among them. People across the globe were worried about what will happen next and all this was due to the reports and news spread through social media sites. The researcher concluded that Facebook remained the primary source of information for most of the respondents who participated in the survey of this study however, it also persisted as the main source of panic among them too. The current study concludes with similar findings i.e. News and information regarding COVID-19 spread on Facebook was found fake most of the time. (Wilson & Chen, 2020). This creates panic, unrest and fear among the users. Twitter in this regard has been seen to play a positive role which means that the application informed the people rather than creating panic.

References

• Abbott, A. (2021). COVID's mental-health toll: how scientists are tracking a surge in depression. Nature, 590(7845), 194-196.
• Afroz, S., Brennan, M., & Greenstadt, R. (2012). Detecting hoaxes, frauds, and deception in writing style online. Paper presented at the 2012 IEEE Symposium on Security and Privacy.
• Agius, S., Grech, A., & Grech, V. (2020). The way in which COVID-19 changed behaviour on social media in Malta. Early Human Development.
• Ahmad, A. R., & Murad, H. R. (2020). The Impact of Social Media on Panic During the COVID-19 Pandemic in Iraqi Kurdistan: Online Questionnaire Study. Journal of Medical Internet Research, 22(5), e19556.
• Albashtawy, M., Gharaibeh, H., Alhalaiqa, F., Batiha, A.-M., Freij, M., Saifan, A., Khamaiseh, A. (2016). The health belief model’s impacts on the use of complementary and alternative medicine by parents or guardians of children with cancer. Iranian journal of public health, 45(5), 708-709.
• Ali, M. Y., & Gatiti, P. (2020). The COVID‐19 (Coronavirus) pandemic: reflections on the roles of librarians and information professionals. Health Information &Amp; Libraries Journal, 37(2), 158–162.
• llcott, H., & Gentzkow, M. (2017). Social Media and Fake News in the 2016 Election. Journal of Economic Perspectives, 31(2), 211–236.
• Almahasees, Z., & Jaccomard, H. (2020). Facebook Translation Service (FTS) Usage among Jordanians during COVID-19 Lockdown. Advances in Science, Technology and Engineering Systems Journal, 5(6), 514–519.
• Almunawar, M. N., Anshari, M., & Susanto, H. (2018). Adopting Open Source Software in Smartphone Manufacturers' Open Innovation Strategy Encyclopedia of Information Science and Technology, Fourth Edition. 7369-7381. IGI Global.
• Alzoubi, H., Alnawaiseh, N., Al-Mnayyis, A., Abu- Lubad, M., Aqel, A., & Al-Shagahin, H. (2020). COVID-19 - Knowledge, Attitude and Practice among Medical and Non- Medical University Students in Jordan. Journal of Pure and Applied Microbiology, 14(1), 17–24.
• Atlani-Duault, L., Ward, J. K., Roy, M., Morin, C., & Wilson, A. (2020). Tracking online heroisation and blame in epidemics. The Lancet Public Health, 5(3), e137–e138.
• Avery, E. (2010). Contextual and audience moderators of channel selection and message reception of public health information in routine and crisis situations. Journal of Public Relations Research, 22(4), 378-403.
• Baines, D., & Elliott, R. (2020). Defining misinformation, disinformation and malinformation: An urgent need for clarity during the COVID-19 infodemic. Discussion papers, 20(06), 20-06.
• Balmas, M. (2012). When Fake News Becomes Real. Communication Research, 41(3), 430– 454.
• Bao, H., Cao, B., Xiong, Y., & Tang, W. (2020). Digital Media’s Role in the COVID-19 Pandemic. JMIR MHealth and UHealth, 8(9), e20156.
• Bassetti, M., Vena, A., & Giacobbe, D. R. (2020). The novel Chinese coronavirus (2019‐ nCoV) infections: Challenges for fighting the storm. European Journal of Clinical Investigation, 50(3).
• Bastani, P., & Bahrami, M. A. (2020). COVID-19 Related Misinformation on Social Media: A Qualitative Study from Iran (Preprint). Journal of Medical Internet Research.
• Berube, D. M. (2021). A Story About Toilet Paper: Pandemic Panic-Buying and Public Resilience Pandemic Communication and Resilience. 143-165. Springer.
• Bessi, A., & Ferrara, E. (2016). Social bots distort the 2016 US Presidential election online discussion. First Monday, 21(11-7), 1-14.
• Bilandzic, H., & Rössler, P. (2004). Life according to television. Implications of genre-specific cultivation effects: The gratification/cultivation model.
• Brewer, K. (2020). Coronavirus: how to protect your mental health. BBC News.
• Casero-Ripollés, A. (2020). Impact of Covid-19 on the media system. Communicative and democratic consequences of news consumption during the outbreak. Casero- Ripollés,
• Andreu (2020). "Impact of Covid-19 on the media system. Communicative and democratic consequences of news consumption during the outbreak ". El profesional de la información, 29(2), 290- 223.
• Cellan-Jones, R. (2020). Tech Tent: Is social media spreading the virus. BBC News.
• Charles-Smith, L. E., Reynolds, T. L., Cameron, M. A., Conway, M., Lau, E. H. Y., Olsen, J. M., Pavlin, J. A., Shigematsu, M., Streichert, L. C., Suda, K. J., & Corley, C. D. (2015). Using Social Media for Actionable Disease Surveillance and Outbreak Management: A Systematic Literature Review. PLOS ONE, 10(10), e0139701.
• Depoux, A., Martin, S., Karafillakis, E., Preet, R., Wilder-Smith, A., & Larson, H. (2020). The pandemic of social media panic travels faster than the COVID-19 outbreak (Vol. 27, pp. taaa031): Oxford University Press.
• Dhir, A., Chen, G. M., & Chen, S. (2017). Why do we tag photographs on Facebook? Proposing a new gratifications scale. New Media & Society, 19(4), 502-521.
• Ding, H. (2014). Transnational quarantine rhetorics: public mobilization in SARS and in H1N1 flu. Journal of medical humanities, 35(2), 191-210.
• Ejaz, W., & Ittefaq, M. (2020). Data for understanding trust in varied information sources, use of news media, and perception of misinformation regarding COVID-19 in Pakistan. Data in Brief, 32, 106091.
• Emmott, R. (2020). Russia deploying coronavirus disinformation to sow panic in West, EU document says . Reuters World News.
• Erku, D. A., Belachew, S. A., Abrha, S., Sinnollareddy, M., Thomas, J., Steadman, K. J., & Tesfaye, W. H. (2020). When fear and misinformation go viral: Pharmacists' role in deterring medication misinformation during the 'infodemic' surrounding COVID- 19. Research in Social and Administrative Pharmacy, 17(1), 1954-1963.
• Etzioni, O., Banko, M., Soderland, S., & Weld, D. S. (2008). Open information extraction from the web. Communications of the ACM, 51(12), 68-74.
• Eysenbach, G. (2002). Infodemiology: The epidemiology of (mis) information. The American journal of medicine, 113(9), 763- 765.
• Fang, J., Wang, X., Wen, Z., & Zhou, J. (2020). Fear of missing out and problematic social media use as mediators between emotional support from social media and phubbing behavior. Addictive Behaviors, 107, 106430.
• Fernandez-Guerrero, I. M. (2014). “WhatsAppitis.” The Lancet, 383(9922), 1040.
• Freitas, M. P. d., Vieira, C. C., Garimella, K., Melo, P. O., & Benevenuto, F. (2019). Can WhatsApp counter misinformation by limiting message forwarding? Paper presented at the International conference on complex networks and their applications. 1- 13.
• Frenkel, S., Alba, D., & Zhong, R. (2020). Surge of virus misinformation stumps Facebook and Twitter. The New York Times, 8.
• Garrett, O. (2020). A Case of Modern Mass Hysteria. The Coronavirus.
• GERBER, A. S., HUBER, G. A., & WASHINGTON, E. (2010). Party Affiliation, Partisanship, and Political Beliefs: A Field Experiment. American Political Science Review, 104(4), 720–744.
• Gerbner, G., & Gross, L. (2017). Living with television: The violence profile The Fear of Crime. 169-195: Routledge.
• Gesser-Edelsburg, A., Diamant, A., Hijazi, R., & Mesch, G. S. (2018). Correcting misinformation by health organizations during measles outbreaks: A controlled experiment. PLOS ONE, 13(12), e0209505.
• Gever, V. C., Talabi, F. O., Adelabu, O., Sanusi, B. O., & Talabi, J. M. (2021). Modeling predictors of COVID-19 health behaviour adoption, sustenance and discontinuation among social media users in Nigeria. Telematics and Informatics, 60, 101584.
• Glanz, K., Rimer, B. K., & Viswanath, K. (2008). Health behavior and health education: theory, research, and practice: John Wiley & Sons.
• Gold, H. (2020). Inside the WHO's fight to stop false information about coronavirus from spreading.
• González-Padilla, D. A., & Tortolero-Blanco, L. (2020). Social media influence in the COVID-19 Pandemic. International Braz J Urol, 46(suppl 1), 120–124.
• Goshayeshi, L., Pourahmadi, A., Ghayour- Mobarhan, M., Hashtarkhani, S., Karimian, S., Shahhosein Dastjerdi, R., Eghbali, B., Seyfi, E., & Kiani, B. (2019). Colorectal cancer risk factors in north-eastern Iran: A retrospective cross-sectional study based on geographical information systems, spatial autocorrelation and regression analysis. Geospatial Health, 14(2).
• Hosseini, N., Fakhar, F., Kiani, B., & Eslami, S. (2019). Enhancing the security of patients’ portals and websites by detecting malicious web crawlers using machine learning techniques. International Journal of Medical Informatics, 132, 103976.
• La, V. P., Pham, T. H., Ho, M. T., Nguyen, M. H., P. Nguyen, K. L., Vuong, T. T., Nguyen, H. K. T., Tran, T., Khuc, Q., Ho, M. T., & Vuong, Q. H. (2020). Policy Response, Social Media and Science Journalism for the Sustainability of the Public Health System Amid the COVID-19 Outbreak: The Vietnam Lessons. Sustainability, 12(7), 2931.
• Larson, H. J. (2018). The biggest pandemic risk? Viral misinformation. Nature, 562(7726), 309-310.
• Lin, X., & Kishore, R. (2021). Social media- enabled healthcare: A conceptual model of social media affordances, online social support, and health behaviors and outcomes. Technological Forecasting and Social Change, 166, 120574.
• Loxton, M., Truskett, R., Scarf, B., Sindone, L., Baldry, G., & Zhao, Y. (2020). Consumer Behaviour during Crises: Preliminary Research on How Coronavirus Has Manifested Consumer Panic Buying, Herd Mentality, Changing Discretionary Spending and the Role of the Media in Influencing Behaviour. Journal of Risk and Financial Management, 13(8), 166.
• Lwin, M. O., Lu, J., Sheldenkar, A., Schulz, P. J., Shin, W., Gupta, R., & Yang, Y. (2020). Global Sentiments Surrounding the COVID-19 Pandemic on Twitter: Analysis of Twitter Trends. JMIR Public Health and Surveillance, 6(2), e19447.
• Molla, R. (2020). How coronavirus took over social media.
• Naslund, J., Aschbrenner, K., Marsch, L., & Bartels, S. The future of mental health care: peer-to-peer support and social media. Epidemiol Psychiatr Sci. 2016 Apr; 25 (2): 113–22.
• Ng, Y. J., Yang, Z. J., & Vishwanath, A. (2017). To fear or not to fear? Applying the social amplification of risk framework on two environmental health risks in Singapore. Journal of Risk Research, 21(12), 1487– 1501.
• Ohst, B., & Tuschen-Caffier, B. (2018). Catastrophic misinterpretation of bodily sensations and external events in panic disorder, other anxiety disorders, and healthy subjects: A systematic review and meta-analysis. PLOS ONE, 13(3), e0194493.
• Ouyang, A., Inverso, N. A., Chow, S. M., Kumar, A., & Zhong, B. (2016). Mo1639 “Listening” to IBS Patients in the 21st Century: Offerings From an On-Line Self Help and Support Group. Gastroenterology, 150(4), S739.
• Ouyang, H. (2020). At the Front Lines of Coronavirus, Turning to Social Media. The New York Times.
• Radwan, E., Radwan, A., & Radwan, W. (2020). The role of social media in spreading panic among primary and secondary school students during the COVID-19 pandemic: An online questionnaire study from the Gaza Strip, Palestine. Heliyon, 6(12), e05807.
• Rana, W., Mukhtar, S., & Mukhtar, S. (2020). COVID-19 in Pakistan: current status, challenges and recommendations.
• Riddle, K. (2009). Cultivation theory revisited: The impact of childhood television viewing levels on social reality beliefs and construct accessibility in adulthood. International Communication Association, 2(15), 1-29.
• Saqlain, M., Munir, M. M., Ahmed, A., Tahir, A. H., & Kamran, S. (2020). Is Pakistan prepared to tackle the coronavirus epidemic? Drugs & Therapy Perspectives, 36, 213-124.
• Setbon, M., & Raude, J. (2010). Factors in vaccination intention against the pandemic influenza A/H1N1. The European Journal of Public Health, 20(5), 490–494.
• Sommariva, S., Vamos, C., Mantzarlis, A., Đào, L. U. L., & Martinez Tyson, D. (2018). Spreading the (Fake) News: Exploring Health Messages on Social Media and the Implications for Health Professionals Using a Case Study. American Journal of Health Education, 49(4), 246–255.
• Sundar, S. S. (2008). The MAIN model: A heuristic approach to understanding technology effects on credibility: MacArthur Foundation Digital Media and Learning Initiative Cambridge, MA, 73-100.
• Skoll, D., Miller, J. C., & Saxon, L. A. (2020). COVID-19 testing and infection surveillance: Is a combined digital contact- tracing and mass-testing solution feasible in the United States? Cardiovascular Digital Health Journal, 1(3), 149–159.
• Tan, Y. T., Rehm, I. C., Stevenson, J. L., & De Foe, A. (2021b). Social Media Peer Support Groups for Obsessive-Compulsive and Related Disorders: Understanding the Predictors of Negative Experiences. Journal of Affective Disorders, 281, 661–672.
• Thorson, E. (2008). CHANGING PATTERNS OF NEWS CONSUMPTION AND PARTICIPATION. Information, Communication &Amp; Society, 11(4), 473–489.
• orales, J., O’Higgins, M., Castaldelli-Maia, J. M., & Ventriglio, A. (2020). The outbreak of COVID-19 coronavirus and its impact on global mental health. International Journal of Social Psychiatry, 66(4), 317–320.
• Waris, A., Atta, U., Ali, M., Asmat, A., & Baset, A. (2020). COVID-19 outbreak: current scenario of Pakistan. New Microbes and New Infections, 35, 100681.
• Wilson, M. E., & Chen, L. H. (2020). Travellers give wings to novel coronavirus (2019- nCoV) (Vol. 27, pp. taaa015): Oxford University Press.
• Wirnhier, E., Mesch, M. B., Senker, J., & Schnick, W. (2012). Formation and Characterization of Melam, Melam Hydrate, and a Melam- Melem Adduct. Chemistry - a European Journal, 19(6), 2041–2049.
• Yousaf, M., Zahir, S., Riaz, M., Hussain, S. M., & Shah, K. (2020). Statistical analysis of forecasting COVID-19 for upcoming month in Pakistan. Chaos, Solitons & Fractals, 138, 109926.
• Zhao, J., Han, H., Zhong, B., Xie, W., Chen, Y., & Zhi, M. (2021). Health information on social media helps mitigate Crohn’s disease symptoms and improves patients’ clinical course. Computers in Human Behavior, 115, 106588.
• Zhao, Y., & Zhang, J. (2017). Consumer health information seeking in social media: a literature review. Health Information &Amp; Libraries Journal, 34(4), 268–283.