Health New Media Res > Volume 5(2); 2021 > Article
Uddin and Islam: YouTube as a source of health information: an analysis of videos on COVID-19


The trend of using social media platforms for health purposes has been increased in recent years. YouTube videos are one of the leading sources of health information for people around the world. The study investigated the portrayal of COVID-19 issues in the videos available on YouTube. The content of the highest viewed 100 videos from January 2020 to May 2020 were analyzed concerning their sources, features, and health messages through the lens of the Extended Parallel Process Model (EPPM). The results show that the video contents made by any kind of organization and health professionals had more balanced health messages (both threat and efficacy) than other types of sources. Moreover, the results indicate that most of the videos were uploaded by news media (44%), but the source displayed the lowest number of health messages in its content compared to other sources. Though health organizations and health professionals as a source had the highest number of health messages, it had only a 14% stake among the highest viewed content. Overall, the videos on COVID-19 displayed more health threat messages (M=15) than efficacy messages (M=7.76), this imbalance of health messages in the health content might be less persuasive for health promotion.


The coronavirus (COVID-19), an infectious disease, has caused the death of over four million people in the past two years and poses a health, social and economic threat to human beings around the globe from the beginning of its detection in Wuhan city of China on December 31, 2019 (Ramphul & Mejias, 2020; World Health Organization [WHO], 2021). Considering its severe impact, the WHO declared a worldwide public health emergency on March 11, 2020 (WHO, 2020). Such a health emergency raised concern among people across the globe about what can happen and how they can get the right information to address the problem. This kind of uncertain situation persuaded people to get health information from multiple sources that can help them, even if they are not affected, to have a basic understanding of the health threat and to take effective measures to protect them from its potential harm (Reynolds & Seeger, 2005).
YouTube is a video-based social media platform, which has immense potentials to reach and influence a large volume of audiences across the world (Luo, Zheng, Zeng, & Leischow, 2014). Around two billion logged-in subscribers contribute more than 500 hours of video content in a minute in the video-sharing platform (Clement, 2019). One of the key features of the platform is that anyone with minimum skills in creating video content can contribute to it. Therefore, the presence of health information on YouTube by individuals who are not experts might be alarming for public health. Researchers (e.g., Basch et al., 2017) found that individuals without expertise in health issues instead of those by professional health organizations are the main contributors to YouTube health content. As a result, it is imperative to understand the content available on YouTube concerning public health. The significance of the social media content examination is inevitable when researchers observed that rumors and misinformation are still rampant in those platforms despite the attempt by the giant tech platforms, including Facebook, Google, Twitter, and YouTube, for stamping out fraud and misinformation from their platforms regarding COVID-19 (Statt, 2020; Tasnim, Hossain, & Mazumder, 2020).
Since COVID-19 was a new phenomenon to the people and YouTube was a potential source of information about the disease, it is important to know who contributes to social media and what kind of messages are available in their content. Therefore, this study aimed at exploring the sources of YouTube videos on COVID-19 and investigating their content’s features and health message appeals. Investigating the videos on health issues requires understanding the key features of the content. This study investigated the features of the content in terms of its popularity and message appeal. The popularity of the content refers to its reach to the public. Meanwhile, message appeals indicated its ability to persuade people to change their health behavior. This study used the Extended Parallel Process Model (EPPM) of Witte (1992) to understand the health messages. The theory emphasizes that people tend to receive health messages when the messages are enriched with both threat and efficacy elements. These elements are measured for this study following the COVID-19 guidelines of the Centers for Disease Control and Prevention (CDC), USA. A previous study on H1N1applied the EPPM theory and used CDC’s guidelines regarding health risk and precaution information to measure threat and efficacy elements (Goodall, Sabo, Cline, & Egbert, 2012). Using the above theoretical framework, this study investigates the following: (a) the major sources of COVID-19 videos on YouTube; (b) message appeals of the video content; and (c) the relationship between sources of the videos and their views, likes, comments, and health messages. The study’s findings focus on the COVID-19 content popularity (e.g., views, likes, and comments) by the source on YouTube. In addition, the findings contributed to knowledge on health messages by showing that effective health messages come mostly from health organizations or health professionals, not from other sources, even the source is a trusted one (e.g., news media). This study suggests that health agencies and health professionals should contribute more health content to YouTube since many people use the site as a source of health information.

Literature Review

Recent experiences indicate that social media platforms often become a leading source of information in an uncertain or an emergency. At the onset of the COVID-19 outbreak, when there was a lack of preventive and protective measures, people searched for health-related information platforms such as YouTube (Basch et al., 2017; Fung et al., 2016). Several studies indicate that the platforms can play an important role in supporting and improving public health, providing a variety of helpful health information, such as virus management, geographical information of affected people, and correct symptomology of the diseases (Chandrasekaran et al., 2017; Charles-Smith et al., 2015). In recent years, social media platforms such as Twitter, Facebook, and YouTube got much attention to scholars from communication, public health, epidemiology for researching different health issues, such as the H1N1 virus, Zika virus, and Ebola (Basch et al., 2017; Basch, Basch, Ruggles, & Hammond, 2015; Pandey, Patni, Singh, Sood, & Singh, 2010).

YouTube Video Sources and Message Appeals

YouTube is the second most web search engine after Google and a primary source for uploading user-generated video content following its billions of subscribers who watch more than a billion hours of videos each day (Amarasekara & Grant, 2019; Clement, 2019). The popularity of YouTube attracts researchers to understand the medium from different perspectives, not only for entertainment but also for social interaction through commenting, seeking information, and providing information (Khan, 2017). As reflected in the research, this platform is used for a variety of purposes, such as promotional activities, factual information, feedback, and awareness creation on different critical perspectives, including health issues (Luo et al., 2014; Smith, Fischer, & Yongjian, 2012). Researchers also found that people use the medium not merely to gain health information but also to investigate, diagnose, and treat different diseases (Madathil, Rivera-Rodriguez, Greenstein, & Gramopadhye, 2015). However, not all health content on YouTube focuses on the health messages that will improve public health. For example, studying e-cigarette content on YouTube, Luo et al. (2014) found that only 3% of videos incorporated warning content related to negative health outcomes of e-cigarette consumption, and most of the videos (84%) were related to e-cigarette promotion, with a focus on an e-cigarette as a socially acceptable and a safer alternative smoking product. Even e-cigarette marketers use fear appeal messages in their content to attract YouTube content viewers for smoking alternatively by showing gruesome images of the cancer patients who were traditional smokers (Paek, Kim, Hove, & Huh, 2014). However, this notion of safer products contradicts the recent evidence of 68 deaths and over 2800 lung injury cases associated with vaping across the United States (CDC, 2020a).
Briones, Nan, Madden, and Waks (2012) investigated Human Papillomavirus (HPV) vaccine videos on YouTube and found 51% of videos express a negative tone, and 32% of videos show a positive tone regarding the vaccine. As per the authors, public controversy on vaccination in the U.S. might influence people to express a negative tone about the vaccine. The HPV vaccine videos of their study mostly come from a news source (36%), following individuals (13%), and medical and hospitals (9%). Recent studies also show a similar trend as health organizations and health professionals have less presence on YouTube. For instance, Basch et al. (2017) examined the most viewed YouTube videos on the Zika virus. They found that 43% of videos were user-generated, 38% were Internet-based news videos, 15% were TV news, and 4% were from health professionals. This trend of YouTube health content source is also almost similar to the study of Pathak et al. (2015) on YouTube content on Ebola. They found that 47% of videos were from individual users, 49% from the news agency, and only 4% content from health organizations or related organizations. However, their findings show that useful health messages come mostly from health organizations and news agencies, which is also echoed similarly in the study of Pandey et al. (2010) on H1N1.
Findings regarding the quality of medical information on Ebola expose those low relevant videos are available more on YouTube than high relevant videos (Nagpal, Karimianpour, Mukhija, Mohan, & Brateanu, 2015). In terms of sources, videos from individual users on Ebola were more misleading than any other source whereas videos of news media were more useful for the information regarding Ebola. However, their study overlooks the video source from health organizations. Another study on marijuana observes that videos on YouTube have less fear appeal (0.9%) (e.g., harmful consequences of marijuana products) and social appeals (18.7%) (e.g., whether marijuana is acceptable or unacceptable) (Yang, Sangalang, Rooney, Maloney, Emery, & Cappella, 2018). It is evident from the previous studies that individuals and news agencies share more videos containing health messages on YouTube compared to professional health bodies or hospitals. In terms of health messages, the videos were less dominant, where there is a need for more attention to promoting public health.

YouTube and Health Research Methods

Most of the previous studies on YouTube content on health issues were descriptive, and focused on view counts, relevance, upload date, clip length, the source of videos, comments, and viewer ratings (Briones et al., 2012; Madathil et al., 2015; Yang et al., 2018). These are all readily available YouTube content features for users and researchers to watch. The YouTube filter option helps researchers to customize their search to find out the targeted content. Madathil et al. (2015) reviewed 18 studies that analyzed YouTube content. Their findings in terms of methodology show that two of the 18 articles focus on framing analysis from the affective and cognitive perspective; seven articles highlight the characterization of video content tone either as positive, negative, or neutral; and the remaining nine studies examined the sources of video content. The most common form of YouTube research is to analyze the content features and tone of the video (Madathil et al., 2015). Content popularity is understood based on its viewership and comments, rating, likes, and dislikes. In this regard, content messages, such as fear and social appeals, lead to higher persuasiveness, significantly affecting viewership and comments (Yang et al., 2018).
Some of the studies also used different theoretical lenses to analyze YouTube health content along with the descriptive analysis of the content. For example, Zhang, Baker, Pember, and Bissell (2017) applied the health belief model (HBM) to investigate Public Service Announcements (PSA) videos on YouTube on healthy eating. Their findings reveal that the major elements of the theory, including severity and self-efficacy, were present in the video content. Most of the PSA videos on YouTube were uploaded by individual users (74%), non-government organizations (10%), and government organizations (7%). Since professionals develop the PSA video content, the presence of relevant health messages is expected, whoever uploads it on YouTube. The theory is also applied by Briones et al. (2012) for understanding HPV content on YouTube. Though their findings highlighted that susceptibility and severity were the core factors to motivate people to take protection from the HPV disease, half of their content neither consider HPV as a dangerous health problem nor vaccination as a good option for getting a cure from the disease.
The above findings are understandable in the U.S. context since here there is a debate on vaccine use as many people do not believe in it. However, coronavirus is a highly contagious disease. People are being infected rapidly across the world. As a result, health organizations like CDC and WHO focus more on prevention approaches to protect people from contracting the virus since there is no specific medication for the disease. Sensing its severity, even the government of different countries lockdown their whole countries or territories to prevent the spread of the disease. So, it is assumed that the magnitude of the COVID-19 health risk is higher than any other health issue. Therefore, the message appeal of user-generated coronavirus content on YouTube might differ from health content in other health issues. Following the consequence, this study uses the concept of EPPM to investigate COVID-19 YouTube videos for understanding health messages.

Extended Parallel Process Model

The Extended Parallel Process Model (EPPM) provides a useful framework to understand the people's perception of threat that determines whether people will accept or reject a message based on their efficacy (Witte, 1992). According to the theory, people are motivated to receive a message when both threat and efficacy are high; the absence of one of the two elements is considered ineffective. Overall, whether a person will accept or reject a health message depends on the magnitude of both threat and efficacy, which also vary from topic to topic and the person’s characteristics, personal experience, and culture (Witte, 1992; Witte & Allen, 2000). As per the EPPM, threat and efficacy might be good components to understand health content on COVID-19 considering the disease’s high negative impact on health and the availability of preventive options.

Threat and Efficacy

McKay, Berkowitz, Blumberg, and Goldberg (2004) explained the threat as the magnitude or severity of harm, and there is a possibility that the harm will occur. While the way out of the harm is considered as efficacy based on how easy the steps are taken to avert the harm. The researchers investigated threat and efficacy messages reflected in a pamphlet and tested among older people who are at risk of cardiovascular (CVD) diseases. The pamphlet provided to the older people highlight heart disease as a high health threat, while consuming B-vitamin and multivitamins is the scope of a way out of the threat. McKay et al. (2004) found that people over 70 years old perceived a low threat of CVD disease compared with those aged 50 to 69. However, people who perceived high efficacy from reading the health messages believe that the recommendations will lower their CVD risks. The finding is consistent with the prediction of EPPM that explains that perceiving threat (danger control vs fear control) depends on the people's situation. People aged over 70 do not worry much about their CVD disease since they are near the terminating age, which is different for those who can live longer.
Hubbell (2006) applied the EPPM to understand Mexican Americans who were susceptible to breast cancer explained the threat in the context of susceptibility and severity. The researcher argued that susceptibility of disease could be understood in the context of health messages that contain information about the risk of experiencing a threat, such as someone who can be affected with breast cancer, while severity refers to the serious consequence of that threat such as someone could die from breast cancer. On the other hand, one's ability to fight the threat is called self-efficacy, such as someone who can fight breast cancer. Investigating cancer health, Rimal and Real (2003) found that high-risk perception makes people concerned about their health status, whereas low efficacy reduces their motivation to respond.
The EPPM theory predicts that health messages enriched with high threat and high efficacy are most likely to motivate people for preventive action. Thus, this study aims to investigate YouTube content on COVID-19 in terms of threat and efficacy messages. To understand the threat and efficacy elements from the COVID-19 perspective, this study uses CDC’s guidelines and the examples of previous studies to formulate threat and efficacy messages.

CDC and Health Messages on COVID-19

Until September 2020, no specific medication or approved vaccine was declared by the WHO or any other organizations to treat the disease. As an alternative, health organizations like CDC advise people to take different preventive measures to protect them from this highly contagious virus. The CDC (2020b) provides COVID-19 health information mainly in two ways—health information regarding risk (or threat) and health information regarding protection measures (or efficacy). As the high risk of the COVID-19, the organization highlights that people aged 65 or over and people in the nursing home are more vulnerable, also people with certain diseases, such as chronic lung disease, severe asthma, serious heart conditions, severe obesity, diabetes, chronic kidney disease, and liver disease. As a health threat, the organization provides specific examples of health risks based on the people’s condition (e.g., mortality rate around 80% among older adults, serious illness, damage to the respiratory system, heart failure, kidney failure, and serious breathing problems). As part of the efficacy, the CDC’s advice is washing hands frequently; maintaining social distance; avoiding touching eyes, nose, and mouth; practicing respiratory hygiene; and staying home. The CDC’s advice for sick people is taking rest and hydrating, also taking home treatment by consulting their care providers (CDC for sick people, 2020c). The organization stated that most of the mild cases with COVID-19 can recover at home.
Previous literature indicated that a large volume of health content on YouTube comes from different sources like individuals and non-health organizations. Therefore, a possibility of having a full of misleading and less appropriate health information on the platform in different health contexts. Also, many people might receive that misleading information being persuaded by their messages and increase the popularity of that content by making comments and giving reactions. Thus, this study investigated YouTube videos on the COVID-19 issues. It analyzed the features and health messages of the videos from the perspective of health promotion. More specifically, this study tried to answer the following questions:
RQ1: What sources are the most viewed YouTube videos about COVID-19 (e.g., individuals or health organizations that produced the videos)?
RQ2: To what extent do YouTube videos about COVID-19 convey information related to threat (i.e., severity and susceptibility to the disease) and the efficacy (individual and collective efficacy) of the people to get rid of the disease?
RQ3: Is there a relation between the type of source and viewer responses in terms of their number of views, likes, comments, and health messages (i.e., threat and efficacy)?

Materials and Methods


This study retrieved COVID-19 YouTube videos in August 2020 using three keywords— COVID-19 virus, coronavirus, and COVID-19 updates, which were the top keywords during the pandemic in April 2020 in the Google Trends, a Google service to “explore what the world is searching for” ( The search results were sorted out by using the view count filter option of YouTube and the upload time of the videos (Paek et al., 2014). YouTube videos from January 2020 to May 2020 were picked for the study considering it as the highest pandemic period. For the next step, the search results excluded the non-English content and irrelevant videos (e.g., “Donald Trump abruptly ends Covid-19 task force briefing”; “How covid-19 could change the financial world order”) based on the content title but retained 50 videos for each search term. At the first stage, 150 most viewed videos were collected manually, and then the number was reduced to 111 after removing the duplication and the videos that had no transcript option in the YouTube and muted comment option. Finally, the top 100 videos in terms of view count (Min=743,865, Max= 26,217,474) were selected as the working sample for the current study.

Coding Scheme

By following the procedures of previous studies (Briones et al., 2012; Madathil et al., 2015; Paek et al., 2014; Yang et al., 2018; Zhang et al., 2017), this study collected the basic features of the YouTube videos, including video links, dates, length, the number of views, the number of comments, the number of likes and dislikes. In addition to these characteristics, the type of source and the message appeal of the videos were coded. The source of the videos is divided into four categories—health organizations or health professionals (e.g., CDC, physicians, etc.), news media (e.g., CNN, BBC, etc.), individuals (e.g., non-health professionals), and other sources (e.g., non-health organizations or groups such as TED-Ed, minute physics, etc.).
The video source is determined by the description or title of each video provided on YouTube. If any content has no clear description or tile, then the content provider’s profile is visited to check the nature of the content uploader, whether it is from an organization or a person. The content type, health organization, and health professionals mean if the content is uploaded by health organization like CDC and any health department, health-promoting organizations or groups who are working under a consortium but not under an organization, while health professionals mean if any individuals identify themselves as physicians, health workers, and health promoters. Along with the title and description of the content, the original source is visited to confirm whether the source is news media or not if the channel was not familiar to the researcher. If any person shares Covid-19 content on YouTube without identifying them as health professionals, they are considered as an individual source. Other sources of YouTube content are from non-health organizations and groups. Although some organizations and groups mainly do not deal with health issues, they uploaded Covid-19 content on YouTube. Not only relying on YouTube description but also visiting their original website, the nature of their organizations or groups are determined.
Finally, this study counted the presence or absence of the EPPM factors (i.e., threat and efficacy elements) to understand health message appeal. Threat and efficacy are measured based on previous studies (Goodall et al., 2012; Hubbell, 2006; McKay et al., 2004) and CDC guidelines on COVID-19. The threat elements of the study were operationalized under severity (message promoting the seriousness of the coronavirus) and susceptibility (messages promoting the likelihood that individuals will be infected with coronavirus). Severity was measured with the magnitude of the pandemic (i.e., high spread/contagious virus, spread from person to person, and spread from any object) and health threats (i.e., death/die/fatal, hospitalization, damage to the respiratory/immune system, no vaccination/no available treatment, severe illness /serious health problem, and breathing and lung problem). Susceptibility was measured with vulnerable groups during the pandemic (i.e., elderly /older people with 65 or above, people in the nursing home, and people with pre-existing/underlying conditions for certain diseases included chronic lung disease/severe asthma/serious heart conditions/ severe obesity/diabetes/ chronic kidney disease/ liver disease/ people under cancer treatment and smoking/immunocompromised) and symptoms of the COVID-19 (i.e., fever/ chills, dry cough/ sore throat, experiencing pain like muscle pain/ fatigue/headache/body aches, loss of taste or smell).
Self-efficacy was measured with individual efficacy (message promoting individual ability to engage in coronavirus disease prevention and treatment) and collective efficacy (message promoting collective efforts to engage in coronavirus disease prevention). Individual efficacy was operationalized following preventive actions (i.e., washing hands frequently; maintaining social distance; avoiding touching eyes, nose, and mouth, etc.; cleaning and disinfecting frequently touched surface; practicing respiratory hygiene/immune system development; staying home/isolated/quarantine) and primary treatment (i.e., treatment/ primary care; recovery at home; mildly affected get recover; taking rest and hydrating; having prescriptive or non-prescriptive medications; avoid triggering that make worse the existing disease). Collective efficacy is operationalized with the keywords included communities, organizations, societies, and we.

Coding Procedures

Each highest viewed COVID-19 video on YouTube was considered as a unit of analysis. After completing the selection process, videos were coded from the transcripts. Among other features, YouTube has an open transcript option. This study saved all the transcripts of the selected videos in a separate word file on a device. Also, data were saved in digital storage for security purposes. Before coding procedures, the first author of the study and a mentor of the research project both checked the readability of a couple of the computer-generated auto transcripts. If the videos’ length is more than 15 minutes, then transcripts were stored for only 15 minutes. The authors of the study and two naive coders who were blind to the research questions and the research objective coded 10 videos independently for reliability purposes. These samples of the video content were not a part of the study. The naive coders were given training so that they could understand the operational definition of the coding items and coding procedures. Intercoder reliability was assessed with Krippendorff's alpha, which was between two trained coders was on average 0.8367. Among the three coders, the reliability coefficient was on average 0.7988, which indicates the relatively strong coding agreement among the coders (Hayes & Krippendorff, 2007). For a more detailed coding reliability agreement, see Table 2.

Data Analysis

Descriptive statistical methods were applied for the basic features of the YouTube videos. Analysis of Variance (ANOVA) was performed to understand the relationship between video sources and the number of views, likes, comments, and health messages (i.e., threat and efficacy). If F-test was found significant in ANOVA, then post hoc analysis was conducted for pairwise comparison using Bonferroni adjustment.


Research question 1 asked about the sources of the most viewed YouTube videos on COVID-19. The highest number of videos (44%) is uploaded by the news media organizations followed by individual users (24%), other sources such as non-health organizations or groups (18%), and health organizations or health professionals (14%). The basic features of the videos by sources are presented in Table 1. Overall distribution of the number of views (M = 567,9507.59, SD = 5333221.109, Min = 743,865, Max = 26,217,474), likes (M = 109,321.00, SD = 152,637.594, Min = 3,400, Max = 902,000), dislikes (M = 5,165.41, SD = 5902.394, Min = 424, Max = 44,000), comments (M = 133,66.41, SD = 13265.034, Min = 0, Max = 59066), video length in minute (M = 11.70, SD = 14.724, Min = 1, Max = 110) were all positively skewed. Main character in the videos was male (67%) followed by female (28%), and both male and female (5%).
The second research question asked about the message appeal of the YouTube videos on COVID-19 about the threat and efficacy messages (see Table 2). In Table 2, the study showed the presence of health messages in the COVID-19 content in terms of EPPM, along with the examples from those content. The coding reliability of sample content is also presented for each major variable in the table. All four categories of sources scored more in the health threat message category (M=15) than the efficacy message category (M=7.76). That means each video has an average of two threat messages, which is double compared to the presence of efficacy messages. However, 87.06% of content has the presence of both threat and efficacy messages, while 8.24% of videos have only threat messages and 4.71% of videos have only efficacy messages (see Figure 1).
The third research question asked about the relationship between the type of source and viewer responses in terms of their number of views, likes, dislikes, comments, and health messages (i.e., threat and efficacy). One way-ANOVA test was conducted to address the relationship of the above variables. Since all the dependent variables are positively skewed, a natural log transformation was performed. No significant relationships were found between the sources and the number of views, comments, and dislikes. Overall F score was significant for likes, F(3, 96) = 7.797, p = .000, health message (threat), F(3, 91) = 11.618, p = .000, and health message (efficacy), F(3, 83) = 4.939, p = .003. Bonferroni test was conducted to evaluate the pairwise difference between sources (health organizations or health professionals, news media, individual users, and other sources) and the number of likes and the presence of health messages (see Table 3). The result showed that news media content gets significantly (p < .05) lower levels of likes (M=61,604.55) than other two categories— other sources (non-health organizations or groups) (M= 211,666.67) and individuals (M=126,944.92). In terms of threat messages, videos uploaded by news media organizations have significantly (p < .05) lower presence of health threat messages (M=6.89) compared to health organizations or health professionals (M=25) and other sources (M=16.67), while videos by individuals also contained significantly (p < .05) lower presence of health threat message (M=11.46) than health organization or health professionals. According to the pairwise comparison, news media also displayed a significantly (p=.004) lower number of efficacy messages (M=3.68) than individual users (M=8.92).

Discussion and Conclusion

YouTube, a dominant user-generated video-sharing platform, is not only a big source of entertainment (Haridakis & Hanson, 2009; Khan, 2017) but also a good source of generating and consuming health information for different purposes (e.g., Briones et al., 2012; Madathil et al., 2015; Nagpal et al., 2015; Pandey et al., 2010). Viewership (567.03 million) of COVID-19 videos of the study as of August 2020 also indicates that YouTube is a popular source of health content. Since anyone can contribute to this site, there is a chance of spreading misinformation or information that might be harmful or less beneficial for public health. Thus, it is imperative to know which source of health information is comparatively better for public health promotion. This study applied EPPM to unearth the sources that are enriched with the threat and efficacy messages by investigating the top viewed 100 COVID-19 videos on YouTube and exploring their features, sources, and message appeals. The findings of the study are discussed based on the four types of sources of the YouTube videos, which are news media; individual users; health organizations or health professionals; and other sources (non-health organizations or groups).
The study explored that though news media as a health information source produced the highest number of contents on YouTube, it had the lowest number of average viewership, likes, and comments as well as health messages. The number of views, likes, comments is a significant indication of content popularity on YouTube (Haridakis & Hanson, 2009; Khan, 2017; Pathak et al., 2015). Previous studies also confirmed that news media were the highest content producer source on YouTube for virus-related content (Basch et al., 2017; Briones et al., 2012; Pathak et al., 2015; Yan, Wang, Li, Guan, & Niu, 2020).
In terms of health message (the presence of both threat and efficacy elements), health organizations or health professionals' content was on the top, followed by non-health organizations or groups, individual users, and news media. According to the EPPM, the content of news media and individual users are less persuasive for public health promotion, while the content of health organizations or health professionals, and non-health organizations or groups is more motivating for health behavior change since people are most likely motivated to accept health messages when the content has both threat and efficacy elements (Witte, 1992). However, the pitfall is the quantity of the highest viewed health content from the source of health organization (14%) is poorer than the other three sources. This finding regarding the content quantity of health organizations is also matched with the previous studies (Basch et al., 2017; Briones et al., 2012; Pathak et al., 2015).
Analyzing the COVID-19 videos, this study found that all four categories of sources scored more in the health threat message category (M=15) than efficacy messages (M=7.76). This difference in displaying health messages in the media content is consistent with the finding of Goodall et al. (2012), who investigated threat and efficacy messages in the print media articles in the context of H1N1. In their study, Carmack and Hocke-Mirzashvili (2013) found the same trend of health messages as threat messages are dominant than efficacy messages. However, the presence of health messages trend in the YouTube content on HPV is observed opposite by Yan et al. (2020) as their study found more efficacy messages (81.7%) than threat messages (susceptibility (60.5%) and severity (70%)). Studies utilizing EPPM theory suggested that health messages are less effective if there is an imbalance between perceived threat and perceived efficacy messages (Goodall et al., 2012; Krajewski, Schumacher, & Dalrymple, 2019; Witte, 1992). So, an imbalance in health messages with high fear appeal might create more health threats than health promotion. Research on AIDS observed that threat in health messages triggers perceived fear among individuals but is not directly related to message acceptance except that both threat and efficacy are high in health messages (Witte, 1994).
Results of the study revealed that there was a significant difference between the sources concerning health messages (threat and efficacy). According to a pairwise comparison, health organizations/health professionals display significantly higher health threat messages than news media and individual users. Other sources (non-health organizations or groups) also contain significantly more health threat messages than news media. However, only individual users’ content displays a significantly higher number of health messages than news media in terms of efficacy messages. These findings contrast with the finding of Briones et al. (2012), who investigated HPV on YouTube and found no significant association between sources and health messages (severity and efficacy). Another study by Paek et al. (2014) did not find any significant health threat messages on the YouTube videos on e-cigarettes. One possible explanation regarding this contrast finding would be the difference between the health impact of COVID-19 and other health issues. Compared to the COVID-19, the health threat of HPV and e-cigarettes might be less impactful. It might be for the impact of the COVID-19 that had the highest mortality rate after the Spanish Flu of 1918 across the globe (Goldstein & Lee, 2020). Therefore, the flow of health information from different sources was abundant to public awareness of the disease’s health impact and take measures to protect them from virus infection, which might promote YouTube users to reflect more threat and efficacy messages in their content.
To the best of our knowledge, this research is the first-of-its-kind that investigated the highest viewed YouTube COVID-19 content. Since COVID-19 had no specific treatment or vaccine, prevention approaches are more desired for promoting public health. Thus, if the sources of COVID-19 health information on YouTube provide more persuasive health messages, public health will benefit more benefitted since message acceptance depends on the presence of threat and efficacy elements in the content according to the EPPM. From that sense, the result of the study will be helpful for public health to understand the useful health information sources providing effective health messages on YouTube. This study will also be helpful to understand the popular COVID-19 content on YouTube, considering their features, such as views and likes. This study suggests that health organizations and health professionals should come forward during health crises to contribute more to the communication channels like YouTube since their content is more effective than any other sources.
This study contributed to knowledge on health messages by showing that effective health messages come mostly from health organizations or health professionals, not from other sources even the health content comes from a trusted source (e.g., news media). In addition, there is no relationship between health content popularity and having effective health messages. For example, the health content of non-health organizations or groups found most popular in terms of their viewership, likes, and comments but not in terms of containing health messages.
There are several limitations to the study. For example, this study focused only on the highest viewed videos, not all videos on YouTube. As a result, the study results in terms of video quantity based on their sources are not generalizable. Future researchers might fill up the gap by retrieving a large number of COVID-19 content uploaded during the high time of the pandemic. Another focus might be on the YouTube COVID-19 content that came from non-health organizations or groups to understand why their content is so popular in terms of their likes, views, and comments compared to the content even coming from health organizations/health professionals. There are some other options for measuring content popularity such as the highest rated videos, which is overlooked in this study. This study did not investigate the comments other than counting them, which is another limitation of the research. Study comments are imperative to understand people's perceptions about a source of COVID-19 videos. This study also did not pay attention to the misleading COVID-19 health content. To understand the effectiveness of the health messages, experimental research might give better reflection. Future studies, especially health communication researchers, might fill up the gaps by researching the above-mentioned issues.
Amid some limitations, the study has some practical implications for health practitioners. According to the study finding, if the health content comes from a trusted source like prominent news media, the health content might not be effective for health promotion. Thus, health practitioners in social media-based health promotion campaigns can refer people to content that comes only from health professionals or health organizations. Also, the study found that some content was so popular in terms of their views, likes, and comments, but the content message was less persuasive for health promotion because of having imbalanced health messages. So, the practitioners can focus more on the content that is more balanced with health messages, not concentrating on the content's popularity. Besides, this study provides insights for the public health experts and regulatory agencies to understand the nature of COVID-19 content on YouTube in terms of their sources, features, and health messages. Content popularity and the content with effective health messages are the study's major findings that might help health practitioners monitor YouTube content on COVID-19 and utilize it for public health promotion. During a health emergency, people try to get health information and health messages from easy access sources. YouTube is one of them to reach a large volume of people within a short time. The EPPM suggests having a threat and efficacy message high in health content that influences individuals to change their health behavior to protect their health and prevent diseases. Thus, health agencies and regulators need to give more attention to the medium for effective health content as well as for filtering the content that is harmful to public health.


The authors wish to thank the University of New Hampshire (UNH) Summer Writing Academy 2020 for mentoring support to develop the manuscript.


Disclosure statement

The authors declare that there is no conflict of interest in the study.

Funding details

This was self-funded research. They received no funds from any funding agency in conducting, writing, and publication of the research.

Data availability statement

All the data associated with the paper are available to the first author.

Figure 1.
Types of Health Messages by Sources
Table 1.
General Characteristics of COVID 19 Videos on YouTube
n Average viewership Average likes Average dislikes Average comments Average length
Types of video sources (n=100)
 Health organizations & health professionals 14 4,986,928.14 95,678.57 4576.79 12,017.07 10.21
 News media 44 4,513,512.48 61,604.55 4248.07 11,812.23 12.24
 Individuals 24 5,705,108.46 126,944.92 6354.79 13,107.08 11.72
 Other sources (e.g. non-health organizations & groups) 18 8,983,177.67 211,666.67 6244.44 18,492.72 11.85
Table 2.
Presence of Health Messages on COVID 19 Videos on YouTube (n=100)
EPPM Variables n Definition Examples Reliability (n=10)
Severity 95 Message promoting the seriousness of the coronavirus 0.7157
The magnitude of pandemic (e.g., high spread/contagious virus, spread from any object, etc.) “So when a contagious person, who may not even know they’re infected, comes into contact with others, it can spread like wildfire.”
Health threats (e.g., death, hospitalization, no vaccine, etc.) “We expect to see the number of cases, deaths, and affected countries climb even higher.”
Susceptibility 55 Messages promoting the likelihood that individuals will be infected with coronavirus 0.7319
Vulnerable groups (e.g., typically older people, people with pre-existing/underlying conditions, etc.) “If you are older or have underlying illnesses you need to contact your health care provider…”
Symptoms (e.g., fever, cough, muscle pain/headache, etc.) “What are the symptoms of this novel coronavirus? The most common, fever, next is cough, is shortness of breath.”
Individual efficacy 82 Message promoting individual ability to engage in coronavirus disease prevention and treatment 0.6803
Preventive action (e.g., washing hands, social distancing, avoiding touching eyes, mouth, nose, etc.) “Because we know the basics-- wash your hands, stay at home, stay away from other people…”
Home treatment (e.g., primary care, taking rest and dehydration, mildly affected get recover at home, etc.) “Plenty of people get COVID-19, but so gently, their symptoms are so mild that they don't even go to a health care provider.”
Collective efficacy (e.g., we, communities, organizations, etc.) 39 Message promoting collective efforts to engage in coronavirus disease prevention “…we're all in the same boat and so if everyone does their part this will help limit the spread…” N/A

N/A: variable item was open coded.

Table 3.
Bonferroni Pairwise Comparison for Dependent Variables by Source Types
Dependent Variables (I) Source Types (J) Source Types Mean Differences(I-J) Std. Error Sig.
Likes Health org. News media .36421 .15714 .135
Individuals -.07144 .17222 1.000
Other sources -.25098 .18249 1.000
News media Health org. -.36421 .15714 .135
Individuals -.43566* .12995 .007
Other sources -.61519* .14328 .000
Individuals Health org .07144 .17222 1.000
News media .43566* .12995 .007
Other sources -.17953 .15967 1.000
Other sources Health org. .25098 .18249 1.000
News media .61519* .14328 .000
Individuals .17953 .15967 1.000
Threat Health org. News media .58600* .11300 .000
Individuals .38440* .12375 .015
Other sources .15151 .13175 1.000
News media Health org. -.58600* .11300 .000
Individuals -.20160 .09510 .220
Other sources -.43449* .10531 .000
Individuals Health org -.38440* .12375 .015
News media .20160 .09510 .220
Other sources -.23289 .11676 .294
Other sources Health org. -.15151 .13175 1.000
News media .43449* .10531 .000
Individuals .23289 .11676 .294
Efficacy Health org. News media .31812 .13425 .121
Individuals -.09697 .14587 1.000
Other sources .03280 .15065 1.000
News media Health org. -.31812 .13425 .121
Individuals -.41508* .11734 .004
Other sources -.28532 .12323 .138
Individuals Health org .09697 .14587 1.000
News media .41508* .11734 .004
Other sources .12976 .13580 1.000
Other sources Health org. -.03280 .15065 1.000
News media .28532 .12323 .138
Individuals -.12976 .13580 1.000


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