An Assessment of the Use of Social Media in the Campaign Against the Spread of Corona Virus Disease in Portharcourt City Lga
This study set to assess the use of social media in the campaign against the spread of corona virus disease in Portharcourt City LGA. 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. To carry out this study, an online questionnaire was prepared and conducted in Port Harcourt city, 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 Port Harcourt city, 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 Port Harcourt city. We found a significant positive statistical correlation between self-reported social media use and the spread of panic related to COVID-19 (R=.8701). Our results showed that the majority of youths aged 18-35 years are facing psychological anxiety. 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 Port Harcourt city.
2.1 Impact of Fake News on Public Health
Fake news concerning health on social media represents a risk to global health. The WHO warned in February 2020 that the COVID-19 outbreak had been accompanied by a massive ‘infodemic’, or an overabundance of information—some of which was accurate and some of which was not—which made it difficult for people to find reliable sources and trustworthy information when they needed it. The consequences of disinformation overload are the spread of uncertainty, fear, anxiety and racism on a scale not seen in previous epidemics, such as SARS, MERS and Zika. Therefore, the WHO is dedicating tremendous efforts aimed at providing evidence-based information and advice to the population through its social media channels, such as Weibo, Twitter, Facebook, Instagram, LinkedIn and Pinterest, as well as through its website. The MIT Technology Review highlights that social media are not only being used to spread false news and hate messages but are also being used to share important truthful data and solidarity with all those affected by the virus and hate messages.
We are in what some have called the second information revolution. The first information revolution began with the spread of the written word through the press. Now, in this second information revolution, a digital transformation is shaping how citizens around the world interact with each other. We are facing an unprecedented global expansion in the ways we share, access and create information that is presented in many forms—one of which is social media.
From diverse fields of knowledge linked to health issues, it can be stated that social media can have both a positive and a negative impact on public health. On the one hand, the combination of artificial intelligence and big data can help public health providers identify pandemic diseases in real time, improving the coordination of the response of public health systems through information sharing and improving surveillance and diagnostics. Furthermore, research shows that social media information and social media responses are effective strategies to gain feedback on potential public health policy proposals. This positive impact of social media in health has been demonstrated in a recent study about heat protection policy for Australian schools, which, through the analysis of public comments posted on a national Australian Broadcasting Corporation (ABC) website, identifies the themes to support a national heat protection policy for schools.
There is evidence of the negative effects of using social media to spread misinformation, which produces harmful consequences to global health and well-being, becoming one of the greatest challenges for public health systems today. The most extensively studied topics involving misinformation in health are vaccination, Ebola and the Zika virus, as well as nutrition, cancer, the fluoridation of water and smoking. Disinformation spread by the anti-vaccine movement has led to episodes regarding vaccination provoking easily preventable disasters, such as the measles epidemic in Washington state (January 2019). The spread of false information also explains a decrease in immunization behaviors with respect to measles-mumps-rubella (MMR) vaccinations, explaining the drop in the demand for this vaccine in the UK and the USA between 1999 and 2000.
Furthermore, research related to the negative impacts linked to the authenticity of social media and identities has increased in recent years. This includes the analysis of the problems surrounding social media messages/posts regarding privacy, posts ending with unintended users, concerns on how to use social media platforms, who to follow and how people portray themselves in an inauthentic manner.
2.2 Spreading Fake News on Health in Social Media
2.2.1. The Context in Which Fake News Is Spread
Never before in human history has the role of globalization processes had the impact that it currently has in decision-making processes and societies because of the speed of communication. Globalization also plays a crucial role in the spread of health news, including social media, influencing the way users receive such news. In this arena, it is important to highlight that in a globalized world, health content information can be perceived differently depending on the target group or context. Therefore, fake news may reach citizens in different ways, depending on their age, culture, and other factors. Moreover, research shows that social media and related global digital media content influence discourses about professions and how citizens perceive them, including public health professionals. For instance, many public health programs aimed at children and youth have physical education at the core of their initiatives. The teaching profession is often portrayed in digital media in relation to unhelpful physical crisis messages or discourses related to bullying in movie scenes. In a similar vein, social media has also been demonstrated to influence the perceptions of adolescent students with regard to their sexual and reproductive health learning. As a result, health professionals may recognize that social media channels, such as Facebook, offer possibilities to support their activities.
Research on the role of mass media and messages and dominant discourses that are communicated to the public is an emerging topic of interest in scientific works that requires further investigation. The influence of social media discourses may differ depending on age, culture or gender. For example, young people build their identities, construct knowledge and acquire information from digital media, including social media, beyond formal education and classroom learning, which is an approach resonating with “public pedagogies”. Other authors such as Ulmer argue that mass digital media provides the public an ‘entry point’ into the debates. The fact is that digital and social media contribute to the strength or undermine the diversity of points of view, influencing the development of specific health public health policies and interventions. Such influence of the media has been defined by some authors as the “fourth state”. Taking into account these contextual considerations, it is important to face fake news related to health in social media to support public health policies instead of trying to reverse them.
2.2.2. Fake News, Health and Social Media
In a globalized world, the spread of fake news content on health-related topics in social media and the ways in which it spreads have recently been discussed in depth. Misinformation and disinformation—misinformation as inaccuracy and errors and disinformation as a falsehood created on purpose and the spread of it by malicious individuals (human or bots)—gain momentum from the desire to find a solution to a particular disease or illness by patients or their relatives, who inadvertently contribute to spreading misleading information.
Globally, the narratives of misinformation are dominated by personal, negative, and opinionated tones, which often induce fear, anxiety, and distrust of institutions. Once misinformation gains acceptance in such circumstances, it is difficult to correct, and the effectiveness of interventions varies according to the personal involvement of each individual and his or her literacy and sociodemographic characteristics. However, other studies have shown that ignorance rarely leads to strong support for a cause. For example, those who most strongly reject the scientific evidence of climate change are also those who believe that they are best informed about the issue. People’s pre-existing attitudes often determine their level of belief in misinformation.
With respect to globalization processes, evidence suggests that false information spreads globally more pervasively and farther and faster than the truth spreads in social media. In examinations of possible explanations for this global phenomenon, it has been found that novelty is a pervasive component of false rumors, which are significantly more novel than the truth. However, data cannot support the contention that novelty is the only reason, or the main reason, for the spreading of falsehood. However, other studies that have focused on the analysis of fake news in social media have reached different conclusions.
A recent study that analyzed the credibility of sources publishing articles online that may reach global audiences concludes that for the specific case of online health information and content on social media, people are more concerned about the veracity and credibility of the information source and tend to spread less misinformation about health. One possible explanation given by the authors is that people generally do not read health information for entertainment but rather search for information useful to their health or that of people close to them. Furthermore, in these cases, they are less likely to have a pre-existing opinion about health information than are those who share fake news stories about other topics. A similar conclusion came from a fact-checking study of Twitter and Sina Weibo (the most-used social media platform in China), developed 24 hours after the WHO’s declaration of the Ebola outbreak as a Public Health Emergency of International Concern in August 2014.
In a globalized world, this declaration by the WHO had diverse impacts on the definition of private and public strategies to combat the virus. It contended that only 2% of the posts created on Twitter and Weibo were fake news or disinformation, while the rest were outbreak-related news and scientific health information, mostly coming from news agencies reporting information from public health agencies. This study was able to confirm that these two social media sources contributed to spreading the news of the Ebola outbreak, which was the key message of the WHO.
Research on fake news on health in social media covers a variety of channels, including Twitter, Facebook, Reddit, and Weibo. The analysis of Twitter has gained special attention, and research shows the reach of Twitter codes and the manner in which information spreads on Twitter. This occurs in diverse fields related to public health, from physical education to healthy eating habits or healthy lifestyles.
2.3. Identification of Social Media Interactions as Key to Spreading or Combating Health-Related Fake News
Social interaction appears to be the main method of understanding how disinformation or fake news spreads over social media. Different studies have been conducted to identify by who and how health disinformation content is promoted in social media. In the case of Twitter, different types of malicious actors covering both automated accounts (including traditional spambots, social spambots, content polluters, and fake followers) and human users, mainly trolls, have been identified. It is very difficult to detect whether there is a human or a bot behind a profile. However, all of them produce distorting effects that may be critical to messages from public health systems.
One of the studies in the case of vaccines identified three types of profiles that had a special probability of spreading vaccine-related disinformation. The first profile is trolls, or Twitter accounts with real people behind them, identified from lists compiled by U.S. authorities; these trolls use the hashtag #VaccinateUS and spread pro- and anti-vaccination messages, often with the apparent aim of encouraging people to believe that the medical community is divided. The second profile, called ‘sophisticated bots’, is artificial intelligence that automatically spreads content via Twitter with the same objective of making people believe that the medical community is divided. The third group of profiles is comprised of content polluters, who use anti-vaccine messages to pique users’ curiosity and lead them to click on links, such that every click leads to more income for those behind the website. Some studies have suggested the need to increase social media literacy, provide strategies and instruments to check the reputation, consistency, and evidence of any information, and avoid self-confirmation (based on assumptions or previous unchecked experiences).
2.4. Combating Fake News on Social Media
Several approaches have been proposed in recent years to automatically assess credibility in social media. Most of them are based on data-based models, i.e., they use automatic learning techniques to identify misinformation. Based on these techniques, different applications have been developed
with different objectives and in different contexts, such as detecting opinion spam on review sites, detecting false news and spam in microblogging, and assessing the credibility of online health information. These techniques include both human intervention and algorithms to verify the veracity of information across technologies, such as artificial intelligence (AI) and natural language processing (NLP) . Other mechanisms developed as a remedy against fake news on social media are source ratings that can be applied to articles when they are initially published, such as expert ratings (in which expert reviewers fact check articles—the results of which are aggregated to provide a source rating), user article ratings (in which users rate articles—the results of which are aggregated to provide a source rating), and user source ratings (in which users rate the sources themselves).
According to the literature, social media is an interaction context in which misinformation is spread faster, but at the same time, there are interactions focused on health that are evidence based. Furthermore, it is important to highlight that social media users share the social impact of health research.
2.5 Overview of Coronavirus
Coronavirus disease 2019 (abbreviated “COVID-19”) is an emerging respiratory disease that is caused by a novel coronavirus and was first detected in December 2019 in Wuhan, China. The disease is highly infectious, and its main clinical symptoms include fever, dry cough, fatigue, myalgia, and dyspnea. In China, 18.5% of the patients with COVID-19 develop to the severe stage, which is characterized by acute respiratory distress syndrome, septic shock, difficult-to-tackle metabolic acidosis, and bleeding and coagulation dysfunction (Utibe, 2019).
The first infected patient who had clinical manifestations such as fever, cough, and dyspnea was reported on 12 December 2019 in Wuhan, China. Since then, 2019-nCoV has spread rapidly to other countries via different ways such as airline traveling and now, COVID-19 is the world’s pandemic problem (Felix, 2020).
Coronaviruses (CoV) infections are emerging respiratory viruses and known to cause illness ranging from the common cold to severe acute respiratory syndrome (SARS) (Yin et al., 2019). CoV is zoonotic pathogens that can be transmitted via animal-to-human and human-to-human. Multiple epidemic outbreaks occurred during 2002 (SARS) with ~800 deaths and 2012 (Middle East Respiratory Syndrome: MERS-CoV) with 860 deaths (Lee, 2020). Approximately eight years after the MERS-CoV epidemic, the current outbreak of novel coronavirus (COVID-19) in Wuhan City, Hubei Province of China, has emerged as a global outbreak and significant public health issue. On 30 January 2020, the World Health Organization (WHO) declared COVID-19 as a public health emergency of international concern (PHEIC). Astonishingly, in the first week of March, a devastating number of new cases have been reported globally, emerging as a pandemic. As of 9 March 2020, more than 110,000 confirmed cases across 105 countries and more than 3800 deaths have been reported (Philemon et al., 2020).
The COVID-19 is spread by human-to-human through droplets, feco-oral, and direct contact, with an incubation period of 2-14 days. So far, no antiviral treatment or vaccine has been recommended explicitly for COVID-19. Therefore, applying the preventive measure to control COVID-19 infection is the utmost critical intervention. Healthcare workers (HCWs) are the primary section in contact with patients and are an important source of exposure to the infected cases in the healthcare settings, thus, expected to be at a high risk of infections. By the end of January, the WHO and CDC (Centers for Disease Control and Prevention) have published recommendations for the prevention and control of COVID-19 for HCWs. Indeed, the WHO also initiated several online training sessions and materials on COVID-19 in various languages to strengthen the preventive strategies, including raising awareness, and training HCWs preparedness activities (Wan, 2019). In several instances, misunderstandings of HCWs delayed controlling efforts to provide necessary treatment, implicate rapid spread of infection in hospitals, and also may put the patients’ lives at risk. In this regard, the COVID-19 epidemic offers a unique opportunity to investigate the level of knowledge, and perceptions of HCWs during this global health crisis. Besides, we also explored the role of different information sources in shaping HCWs knowledge and perceptions on COVID-19 during this peak period.
It seems that the current widespread outbreak has been partly associated with a delay in diagnosis and poor infection control procedures. As transmission within hospitals and protection of healthcare workers are important steps in the epidemic, the understanding or having enough information regarding sources, clinical manifestations, transmission routes, and prevention ways among healthcare workers can play roles for this gal assessment. Since nurses are in close contact with infected people, they are the main part of the infection transmission chain and their knowledge of 2019-nCoV prevention and protection procedures can help prevent the transmission chain. Iran is one of the most epidemic countries for COVID-19 and there is no information regarding the awareness and attitude of Iranian nurses about this infectious disease.
2.6 The virus, its origins and evolution
Coronavirus is believed to be transmitted through respiratory aerosols, which were released while an SARS patient coughs or sneezes. Viral infection will spread from the droplets of cough or sneeze of an infected patient are propelled in surroundings via air and will infect the nearby people who are nearby through several ways like mouth, nose or eyes. The virus also can spread by touching infected surfaces, and then touching the mouth, nose, or eye (Centers for Disease Control and Prevention, 2020).
Severe acute respiratory syndrome (SARS) probably first emerged in Guangdong around November 2002. Many of the affected individuals in November and December 2002 had contact with the live-game trade. The disease was described as an “infectious atypical pneumonia” because of its propensity to cause clusters of disease in families and healthcare workers. The etiological agent of SARS was identified as a new coronavirus not previously endemic in humans. The lack of serological evidence of previous infection in healthy humans suggested that COVID-19 had recently emerged in the human population and that animal-to-human interspecies transmission seemed the most probable explanation for its emergence. Specimens collected from apparently healthy animals (e.g., Himalayan palm civets (Paguma larvata) and raccoon dogs (Nyctereutes procyonoides)) found in live wild-game animal markets in Guangdong yielded a COVID-19-like virus with more than 99% nucleotide homology to the human COVID-19. But the wild-animal reservoir in nature still has not been identified conclusively. Many workers who handled animals in these wet markets had antibody to the related animal COVID-19-like virus although they had no history of a SARS-like disease. Taken together with the observation that a number of the SARS-affected individuals in November and December 2002 had epidemiological links to the wild-game animal trade, it is likely that these wet markets in Guangdong provided the interface for transmission to humans. The early interspecies transmissions to humans were probably inefficient, causing little human disease or transmission between humans. Eventually, the animal precursor COVID-19-like virus probably adapted to more efficient human-to-human transmission, and Coronavirus emerged. As two authors aptly stated, this was “one small step to man, one giant leap to mankind.
Fifty-three percent of probable cases of Coronavirus reported to the WHO were female, and all age groups were affected (age range 0–100 y). Worldwide, Coronavirus was strikingly a nosocomially acquired infection. Health care workers comprised 22% of reported cases in Hong Kong and Guangdong, China and >40% in Canada and Singapore. A complex mix of agent, host-biologic, and behavioral factors and environmental context determine the magnitude and spread of outbreaks. Not all cities or countries that received even the earliest Coronavirus importations experienced sustained transmission or outbreaks. For example, in Canada, both the Greater Toronto Area, Ontario and the city of Vancouver, British Columbia received critically ill patients from the Hotel M cluster. Whereas the Greater Toronto Area experienced an extensive outbreak, no secondary spread ensued from the case in Vancouver. Similarly, no sustained transmission occurred in the United States despite multiple importations. Why some areas experienced sustained outbreaks and others did not has yet to be fully explained.
SARS remained isolated in China from November 2002 until 21 February 2003, when a physician with SARS traveled from Guangdong province to a hotel in Hong Kong, infecting 10 other guests. The movements of these 11 individuals resulted in the spread of SARS worldwide and sparked all of the major epicenters outside of China.
The rate of spread of an epidemic and whether it is selfsustaining depend on the basic reproduction number (R0). R0 is defined as the average number of secondary cases generated by 1 primary case in a susceptible population. This quantity determines the potential for an infectious agent to start an outbreak, the extent of transmission in the absence of control measures, and the ability of control measures to reduce spread. During the course of an epidemic, Rt, the effective reproduction number, decreases in comparison with R0 as a result of the depletion of susceptible persons in the population, death or recovery with subsequent immunity, and the implementation of specific control measures. To stop an outbreak, Rt must be maintained below 1. Mathematical modeling of the early phase of the Singapore and Hong Kong outbreaks, before the institution of control measures and during which time it was occurring primarily in the hospital setting, estimated that the R0 was 2.2–3.7, indicating that the virus is moderately infective. The attack rate for COVID-19 ranges from 10.3% to 60% or 2.4 to 31.3 cases/1000 exposure-hours, depending on the clinical setting and the unit of measurement. A significant limitation of these calculations is that these data are based on diagnoses made with a clinical case definition. Reanalysis will be required once the results of seroprevalence studies are completed and will provide a more accurate estimate of R0.
2.7.1 Incubation Period
The estimated incubation period for Coronavirus is 2–14 d. An incubation period of as low as 1 d was reported from China (four cases) and Singapore (three cases). Incubation periods of 10–14 d have been reported in a small number of cases from China, but case ascertainment and a well-defined exposure interval for these cases are incomplete. Most countries reported a median incubation period of 4–5 d and a mean of 4–6 d. It remains unclear whether the route of transmission influences the incubation period.
2.7.2 Infectious Period
There has been no evidence to date of COVID-19 transmission prior to symptom onset, and transmission from asymptomatically infected persons has not been observed. There have been no reports of transmission beyond 14 d of fever resolution. Transmission appears to be greatest from severely ill patients and those experiencing rapid clinical deterioration, usually during the second week of illness. Patients with Coronavirus are most infectious at around day 10 of illness. In this regard Coronavirus is unlike most other respiratory-borne diseases, with the notable exception of smallpox.[email protected].[email protected].