detecting fake news assignment vaccines answers

How to Address COVID-19 Vaccine Misinformation

The spread of misinformation on social media and through other channels can affect COVID-19 vaccine confidence.

Misinformation often arises when there are information gaps or unsettled science, as human nature seeks to reason, better understand, and fill in the gaps.

On this page, the Centers for Disease Control and Prevention (CDC) shares strategies for communicating accurate information  about COVID-19 vaccines, responding to gaps in information , and confronting misinformation with evidence-based messaging from credible sources.

• Misinformation is false information shared by people who do not intend to mislead others.
• Disinformation is false information deliberately created and disseminated with malicious intent.

Both types can affect vaccine confidence and vaccination rates. Most misinformation and disinformation that has circulated about COVID-19 vaccines has focused on vaccine development, safety, and effectiveness, as well as COVID-19 denialism.

The first step to addressing misinformation about COVID-19 vaccines in your community is learning more about it, including where it starts and when, why, and how it is spreading and evolving.

Strategies for addressing COVID-19 vaccine misinformation in your community:

Download CDC’s Rapid Community Assessment Guide  for steps and adaptable tools to quickly gather information and better understand your community of focus.

• Listen to and analyze misinformation circulating in your community through social and traditional media monitoring . This can include monitoring social media channels and traditional media outlets for misinformation and creating a log of that misinformation to identify trends in your area. This can help you understand where, when, why, and how misinformation is spreading in your community.
• Engage with and listen to your community to identify and analyze perceptions, content gaps, information voids, and misinformation.
• Share accurate, clear, and easy-to-find information  that addresses common questions. This can be done through your website, social media, and other places your audience looks for health information. Also use methods to reach those with limited or no internet access, such as radio or community events. Share details, including addresses and hours, about local vaccination sites and events with community-based organizations and local media. See How to Tailor COVID-19 Information to Your Specific Audience .
• Use trusted messengers to boost credibility and the likelihood of being seen and believed over misinformation. Some people may not trust public health professionals or visit the health department website, so it’s more effective to reach them through the channels and sources they look to and trust for health information, such as religious leaders or community organizations.

How to effectively address misinformation:

Below is an example of how to address misinformation following the above guidance:

FACT: The COVID-19 vaccine will not make you sick with COVID-19.

WARNING: Misinformation alert!

FALLACY: Some people are saying that the COVID-19 vaccine will give you COVID-19. That is not true . While you may feel sick after getting the COVID-19 vaccine, that is a sign your body is building protection against the virus that causes COVID-19.

FACT: The COVID-19 vaccine cannot make you sick with COVID-19. COVID-19 vaccines teach your immune system to recognize and fight the virus that causes COVID-19. Sometimes this process can cause symptoms, such as fever and chills. These symptoms are normal and are signs that the body is building protection against the virus that causes COVID-19.

Monitoring misinformation through social listening is a key strategy to quickly identify and address misinformation about COVID-19 vaccines. This includes identifying trending inaccurate information, which, if not addressed, can lead to the spread of misinformation.

Catching misinformation early can help you develop and get out accurate information to address concerns and questions ahead of time and close information gaps before they are filled with inaccurate information.

How to conduct effective social listening and media monitoring and help prevent the spread of misinformation:

• Identify your jurisdiction’s existing information sources that will inform listening, such as analysis of social media comments, call center logs, and media inquiry logs. Check tools regularly to gather social listening data.
• Create and maintain a social media influencer list to monitor for perceptions, content gaps, and misinformation.
• Create and maintain a rumor log to track circulating misinformation, its volume, how it is spreading, and how it evolves over time.
• Set up a social and traditional media monitoring system to access key channels, communities, and conversations, including free monitoring and analytical tools.
• What questions are people asking about COVID-19 vaccination?
• What are people’s attitudes and emotions that may be linked to vaccination behavior?
• What rumors or misinformation are circulating?
• What overarching themes and narratives—beyond individual pieces of content—emerge from widely circulated rumors and misinformation?
• How are people responding to and interpreting vaccine-related communication from public health authorities?
• Learn how Tennessee’s COVID-19 Health Disparity Task Force built trust to battle misinformation.
• Social Listening and Monitoring Tools pdf icon : Tools embedded within CDC’s Rapid Community Assessment Guide to support social listening and monitoring implementation.
• UNICEF Field Guide on Addressing Misinformation external icon : A field guide that includes strategies to address misinformation about COVID-19 vaccines in global contexts.
• How to Detect Misinformation external icon : A quick guide by Stronger on detecting misinformation, disinformation, lies, and conspiracy theories about vaccines.
• COVID-19 State of Vaccine Confidence Insights Report : A biweekly report highlighting emerging issues of misinformation, disinformation, places where intervention efforts can positively impact vaccine confidence across the United States, and major themes influencing COVID-19 vaccine hesitancy and uptake. To receive this report, please email [email protected] .
• Community Engagement Playbook (Agency for Toxic Substances and Disease Registry) – Guidance on what programs/partners could consider as they develop a plan for engaging communities, to include four phases and nine key activities of community engagement.

Exit Notification / Disclaimer Policy

• The Centers for Disease Control and Prevention (CDC) cannot attest to the accuracy of a non-federal website.
• Linking to a non-federal website does not constitute an endorsement by CDC or any of its employees of the sponsors or the information and products presented on the website.
• You will be subject to the destination website's privacy policy when you follow the link.
• CDC is not responsible for Section 508 compliance (accessibility) on other federal or private website.

How to spot coronavirus fake news – an expert guide

Postdoctoral Researcher in Social Sciences, University of Oxford

Disclosure statement

Samantha Vanderslott receives funding from the National Institute for Health Research (NIHR) and the New Venture Fund. She is also a steering committee member for the Vaccination Acceptance Research Network (VARN).

University of Oxford provides funding as a member of The Conversation UK.

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The proliferation of fake news about the COVID-19 pandemic has been labelled a dangerous “ infodemic ”. Fake news spreads faster and more easily today through the internet, social media and instant messaging. These messages may contain useless, incorrect or even harmful information and advice, which can hamper the public health response and add to social disorder and division.

Confusingly some fake news also contains a mixture of correct information, which makes it difficult to spot what is true and accurate. Fake news may also be shared by trusted friends and family, including those who are doctors and nurses. They might not have read the full story before sharing or just glanced over it. Before you decide to share, make sure to read stories properly and follow some checks to determine the accuracy.

If the story appears to claim a much higher level of certainty in its advice and arguments than other stories, this is questionable. People will be seeking certainty in a time of high uncertainty, anxiety and panic. So it is only natural to more readily accept information that resolves, reassures and provides easy solutions – unfortunately, often in a false way.

Similarly, if a story is more surprising or upsetting than other stories it is worth double-checking, as fake news will try to grab your attention by being more exaggerated than real stories.

What to look out for

Source. Question the source. References have been made to “Taiwanese experts” or “Japanese doctors” or “Stanford University” during the outbreak. Check on official websites if stories are repeated there. If a source is “a friend of a friend”, this is a rumour unless you also know the person directly.

Logo: Check whether any organisation’s logo used in the message looks the same as on the official website.

Bad English: Credible journalists and organisations are less likely to make repeated spelling and grammar mistakes. Also, anything written entirely in capital letters or containing a lot of exclamation marks should raise your suspicions.

Pretend social media accounts: Some fake accounts mimic the real thing. For example, the unofficial Twitter handle @BBCNewsTonight, which was made to look like the legitimate @BBCNews account, shared a fake story about the actor Daniel Radcliffe testing positive for coronavirus . Media platforms try to remove or flag fake accounts and stories as well as verify real ones. Look out for what their policies are to try to do this.

Over-encouragement to share: Be wary if the message presses you to share – this is how viral messaging works.

Use fact-checking websites: Websites such as APFactCheck and Full Fact highlight common fake news stories. You can also use a search engine to look up the title of the article to see if it has been identified as fake news by the mainstream media.

Who to trust

The best sources to go to for health information about COVID-19 are your government health websites and the World Health Organization website . Primary sources are generally better than news articles.

Even government messaging and the mainstream media can get things wrong, but they are more trustworthy than unverified sources on social media and viral messaging. For instance, The Conversation is a more trusted source because all content is written by academics who are experts in their fields.

Read more: How to spot bogus science stories and read the news like a scientist

Charlatans have been promoting false preventions and cures for people to spend their money on. For example, the New York attorney general has had to send cease and desist notices for claims that toothpaste, dietary supplements and creams will prevent and cure COVID-19 .

The effects can also be more serious than losing some cash. Iran has reported at least 44 people died from alcohol poisoning after drinking bootleg alcohol in a misguided attempt to cure COVID-19.

Unfortunately, the most basic and correct advice given so far does not offer a miracle or special insight. Wash your hands often (use hand sanitisers if you cannot), avoid touching your face, and sneeze or cough into the crook of your elbow or a tissue (and throw it away in a bag-lined bin). Avoid crowds and public places, keep a sensible distance from people, and do not travel unless absolutely necessary. Now many governments are introducing measures including travel bans and quarantines that need to be followed to protect the health of everyone, especially the most vulnerable.

We can all get caught out. Think twice about the messages currently circulating and help guide your family and friends to decide what to trust.

• Social media
• Coronavirus
• Misinformation

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• Open Access
• Published: 11 August 2022

Misinformation of COVID-19 vaccines and vaccine hesitancy

• Sun Kyong Lee 1 ,
• Juhyung Sun 2 ,
• Seulki Jang 3 &
• Shane Connelly 3  

Scientific Reports volume  12 , Article number:  13681 ( 2022 ) Cite this article

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The current study examined various types of misinformation related to the COVID-19 vaccines and their relationships to vaccine hesitancy and refusal. Study 1 asked a sample of full-time working professionals in the US ( n  = 505) about possible misinformation they were exposed to related to the COVID-19 vaccines. Study 2 utilized an online survey to examine U.S. college students’ ( n  = 441) knowledge about COVID-19 vaccines, and its associations with vaccine hesitancy and behavioral intention to get a COVID-19 vaccine. Analysis of open-ended responses in Study 1 revealed that 57.6% reported being exposed to conspiratorial misinformation such as COVID-19 vaccines are harmful and dangerous. The results of a structural equation modeling analysis for Study 2 supported our hypotheses predicting a negative association between the knowledge level and vaccine hesitancy and between vaccine hesitancy and behavioral intention. Vaccine hesitancy mediated the relationship between the vaccine knowledge and behavioral intention. Findings across these studies suggest exposure to misinformation and believing it as true could increase vaccine hesitancy and reduce behavioral intention to get vaccinated.

Introduction

Health misinformation can kill people, both directly and indirectly. During a public health crisis such as the COVID-19 pandemic, exposure to misinformation about the virus’ spread, symptoms of infection, testing opportunities, and prevention methods can lead to erroneous appraisals of the threat, maladaptive coping behaviors, and a range of fatal consequences. More critically, misinformation about the new COVID-19 vaccines and their development process has the potential to induce high levels of vaccine hesitancy in the public 1 , 2 , preventing vaccination rates sufficient for achieving herd immunity. Due to the high level of uncertainty caused by the pandemic and the relatively fast speed of vaccine development compared to other types of traditional vaccines, the public naturally sought out information to address their vaccine concerns and guide critical decision-making such as whether to get vaccinated or not. However, separating relevant and valid information from false and distorted misinformation about COVID-19 vaccines is difficult when a vast amount of material is being conveyed through media outlets and websites of varying reliability and accuracy. One critically important challenge to obtaining reliable, accurate COVID-19 vaccine information includes the pervasive, unsolicited, and dubious pseudo-news items communicated via online and social media platforms by various types of actors 1 .

Because many people acquire and share news via social media 3 , misinformation can spread quickly through their social networks, and the likelihood of exposure to disinformation and misrepresentations about the vaccine from unverified sources is high. The resulting increase in public anxiety and negative emotional and behavioral responses complicates the process of advising the public through health experts and agencies such as the CDC and WHO. The current research focuses on people’s perceptions of the nature and types of misinformation about COVID-19 vaccines 4 . Additionally, this research examines the relationship between knowledge about COVID-19 vaccines, including its relevant misinformation, and vaccine hesitancy and refusal.

By acknowledging the serious negative impact of health misinformation and its spread, research in infodemiology (i.e., epidemiology of (mis)information 5 , 6 ) identifies the knowledge translation gap between evidence produced by experts and the public’s actual practices and beliefs. Research has identified various quality markers (e.g., source, technical, and content criteria) and their relations with outcome variables (e.g., health-related knowledge and behavioral changes) necessary for effective health communication on the internet. Studies have shown both algorithmic-based correction on Facebook and social correction via anonymous commenters can be effective in reducing beliefs in health misinformation. However, people who believe in conspiracy theories tend to discredit algorithm-based corrections 7 , 8 .

More research is needed to (a) observe and define the trends and prevalence of health misinformation on various types of social media; (b) understand how relevant misinformation is shared; (c) evaluate the reach and influence of misinformation; and (d) develop and test effective interventions 9 , 10 . With the aim of increasing scientific knowledge in these four areas—particularly as they pertain to the current global pandemic, this study consisting of two parts examine the nature and content of misinformation about COVID-19 vaccines and their relationships with vaccine hesitancy. Findings can shed light on designing an effective social action campaign for decreasing the public’s vaccine hesitancy, increasing their health information literacy, and buttressing their ability to identify, resist, and refute false claims encountered in-person or online.

Misinformation on vaccines

Scholars have identified some common misinformation about coronavirus and the pandemic, such as the novel coronavirus being created by the Chinese government as a bioweapon 11 , and that the virus was intentionally created by powerful people 12 . While these types of misinformation emerged during the early period of the COVID-19 pandemic and they are still pervasive, additional rumors and conspiracy theories have emerged about COVID-19 vaccines in particular as governments and scientists invested heavily in capitalizing on existing messenger RNA (mRNA) technology to quickly develop new COVID-19 vaccines. Misinformation, also called as fake news, refers to any inaccurate claims or depictions and disinformation is a subset of misinformation “intended to mislead” 13 (para. 3). Traditional anti-vaxxer organizations have used mis- and disinformation (including conspiracy theories) as their strategies to persuade people to not get vaccinated; one common piece of misinformation is that vaccines cause Autism, which has been debunked over and over 4 , 14 .

However, studies show that even after misinformation is corrected, false beliefs can still remain and are difficult to change 11 , 15 , 16 . Given that different people use varying criteria to determine the truthfulness of information (e.g., compatibility with existing information, source credibility, others’ beliefs, internal consistency, and supporting evidence), some people are more susceptible to misinformation than others 16 . Also, when misinformation is easy to read or hear, aligns with individuals’ political beliefs 17 , and is not carefully deliberated 18 , people are more likely to believe it as truth.

The anti-vaccine movement uses conspiratorial claims to spread mis/disinformation about COVID-19 and its vaccines 4 . For example, some anti-vaxxers say Bill Gates or other powerful people created the virus as a means to mandate vaccines which would be used to inject people with a microchip so that a global surveillance network can be established. Other anti-vax lobbyists made a false claim about Dr. Anthony Fauci, the head of the National Institute of Allergy and Infectious Diseases of the US National Institutes of Health, indicating that he funded a Wuhan lab so they will “transform an innocuous coronavirus into the lethal and transmissible SARS CoV2 virus.” While these claims are definitely false, they promulgate conspiratorial beliefs, increasing uncertainty and concerns about COVID-19 vaccines and their effectiveness. This potentially affects hesitancy and willingness to get vaccinated in people who may already be hesitant and in those who normally do not question vaccines. Therefore, the first study examined the nature and prevalence of COVID-19 vaccine misinformation circulated among the U.S. public:

RQ1. What kinds of misinformation about COVID-19 vaccines do people in the US report being exposed to?

Misinformation and vaccine hesitancy

Individuals’ decision-making about vaccination is a continuum from active demand for vaccines to complete refusal of all kinds of vaccines 2 . While there are a few external factors influencing vaccine hesitancy such as public health and vaccine policies, health professionals’ recommendations, or communication and media portrayals, Dubé et al.’s model lists six internal factors involved in individuals’ decision-making process: (a) knowledge/information, (b) past experiences, (c) perceived importance of vaccination, (d) risk perception and trust, (e) subjective norms, and (f) religious and moral conviction. As such, vaccine hesitancy is a multifaceted phenomenon influenced by various social, cultural, and political contexts; vaccine hesitant people are a heterogenous group in that they may refuse some vaccines, but agree to others; for this reason, vaccine uptake is not directly related to vaccine hesitancy and it can vary by specific vaccines involved. Newer vaccines, such as those developed for COVID-19, generally bring higher levels of hesitancy 19 , 20 .

Hornsey et al. 21 examined the psychological factors influencing rejection of scientific consensus around vaccination. Based on a large sample collected from 24 countries, they found anti-vaccination attitudes were highest among those who (a) were high in conspiratorial thinking, (b) were high in reactance, (c) reported high levels of disgust toward blood and needles, and (d) had strong individualistic/hierarchical worldviews. However, demographic variables (including education) were insignificant or explained minimal amounts of variance. According to this finding, we expect COVID-19 vaccine hesitancy will be higher for those who are misinformed about the virus and its vaccines by the conspiracies related to the pandemic. Certain personality traits such as reactance (or agreeableness) seem also related to vaccine hesitancy (Authors, under review).

Several well-known conspiracy theories are circulated on anti-vaccination websites such as the idea that large pharmaceutical companies and other interested groups exaggerate the benefits of vaccination and hide risks or dangers of vaccines 22 . Regarding COVID-19 vaccines in particular, some new forms of conspiracy theories have emerged. Individual conspiracy beliefs may come from a unitary conspiratorial worldview that believes “it is common for shadowy networks of people with malevolent intentions to execute mass hoaxes on the public in near-perfect secrecy” (p. 308) 21 . Those who feel this way might be motivated to believe conspiracies about science, with negative impacts on vaccination intentions. Previous studies also identified that people’s willingness to endorse conspiracies (e.g., about the assassination of John F. Kennedy or the death of Princess Diana) were generally correlated with a range of “anti-science” attitudes, including anti-vaccination attitudes 23 , 24 .

From a randomized controlled trial, Loomba et al. 25 found that exposure to online misinformation about COVID-19 vaccines declined vaccination intent significantly in both the U.S. (6.4% points) and U.K (6.2% points) samples. They also identified some socio-demographic groups distinctly influenced by exposure to misinformation. Females in the US were more impacted by misinformation than males in terms of vaccination intent to protect others, and lower-income groups were more robust to misinformation regarding vaccination intent to protect themselves or others than the highest income group. In the UK, unemployed participants were less likely to lower their vaccine intent upon exposure to misinformation than the employed, and ‘other’ religious affiliations were more robust to misinformation than Christians.

Based on the above research findings, those who are misinformed about the COVID-19 vaccines development and effectiveness, whether via their own conspiratorial beliefs and worldviews 21 , or from exposure to various mis/disinformation about vaccines 23 , 24 , 25 , will be more likely to be hesitant about getting vaccinated against the virus. Thus, we propose the following:

H1. Knowledge levels about COVID-19 vaccines are negatively associated with COVID-19 vaccine hesitancy.

Consequently, it is expected that people who are more hesitant to get vaccinated will also be less inclined to actually get vaccinated against the coronavirus. Therefore:

H2 . COVID-19 vaccine hesitancy is negatively associated with the behavioral intention to get a COVID-19 vaccine.

Finally, the second study examines the following research question about the mediating relationship between vaccine knowledge and vaccination intent by the level of hesitancy:

RQ2 . Does COVID-19 vaccine hesitancy mediate the influence of knowledge on the behavioral intention to get a COVID-19 vaccine?

Results for study 1

Study 1, examined the types of COVID-19 vaccine misinformation that people in the US reported being exposed to. Results are summarized in Table 1 . Participants indicated the specific nature of misinformation they heard about COVID-19 vaccines. No explicit information was gathered on whether participants believed or agreed with any of the misinformation they reported. Note that out of 505 participants, 17 did not answer the vaccine misinformation questionnaire and responses from a total of 488 participants were coded. Out of 488 participants, 62 participants provided irrelevant responses (e.g., “many”, “false”, “learn about the benefits of COVID-19 vaccination based on what experts currently know”). Out of 426 participants, 207 participants (42.42%) indicated that they did not hear any misinformation related to COVID-19 vaccines. Importantly, it is unclear whether these participants actually did not hear any misinformation about COVID-19, or whether they heard misinformation and believed it to be factual.

Of the 281 participants who reported hearing COVID-19 vaccine misinformation, 70 participants shared more than one piece of vaccine misinformation. Overall, 14 different types of misinformation were reported. According to the results, the most commonly shared vaccine misinformation was that COVID-19 vaccines contain a microchip to track people. The next commonly shared vaccine misinformation and false claims were that COVID-19 vaccines are dangerous and harmful and that COVID-19 vaccines cause death, respectively.

To further understand what demographic variables significantly differentiated participants who reported hearing no COVID-19 vaccine misinformation from those who reported hearing one or more types of misinformation, a series of logistic regression analyses were performed. Results showed that age ( B  = − 0.02, p  < 0.05), the number of dependents ( B  = 0.20, p  < 0.05), and religion specifically Catholic ( B  = 1.18, p  < 0.01) were significantly associated with hearing no vaccine misinformation, while race, gender, education, marital status, political affiliations, organizational size, sexual orientation, and work industry did not. In other words, people who indicated that they did not hear any misinformation related to COVID-19 vaccines were younger, had more dependents, and Catholic.

Results for study 2

Testing of hypotheses 1, 2 and rq2.

As indicated in Fig.  1 , we examined the associations among knowledge levels about COVID-19 vaccines, COVID-19 vaccine hesitancy, and behavioral vaccination intention for COVID-19. In addition, the mediating role of COVID-19 vaccine hesitancy was investigated. The results of structural equation modeling (SEM) demonstrated that the proposed hypothesized model had a good fit: χ 2 (253) = 655.24, p  < 0.001, χ 2 /df = 2.59, CFI = 0.92, RMSEA = 0.06, and SRMR = 0.05. The percentage of explained variance in COVID-19 vaccine hesitancy and behavioral intention to get COVID-19 vaccines were 63.0% and 76.5%, respectively.

Results of the SEM depicting direct and indirect effects (Study 2). Estimates are indicated in standardized values and the dashed lines indicate non-significant paths. *** p  < 0.001.

Figure  1 presents the results for the SEM analysis after controlling for the effects of demographic variables. The results showed that there were no demographic differences in vaccine hesitancy by gender ( p  = 0.15), ethnicity ( p  = 0.65), religion ( p  = 0.37), political party affiliation ( p  = 0.47), and political ideology ( p  = 0.08). On the other hand, demographic differences were seen in behavioral vaccination intention by political ideology ( p  < 0.05) while there were no differences by the other demographic variables: gender ( p  = 0.28), ethnicity ( p  = 0.21), religion ( p  = 0.20), and political party identification ( p  = 0.06). Participants who identified as conservatives were less likely to get vaccinated than those who identified as moderate or liberal.

H1 suggested that knowledge about COVID-19 vaccines would be associated with COVID-19 vaccine hesitancy. We found that individuals’ general knowledge levels about the vaccines had a negative relationship with the levels of vaccine hesitancy for COVID-19 ( b  = − 0.77, p  < 0.001). The more accurate knowledge participants had about COVID-19 vaccines, the lower-level hesitancy they reported for COVID-19 vaccines, supporting H1.

H2 predicted that COVID-19 vaccine hesitancy would be negatively related to behavioral vaccination intention for COVID-19. The result showed that vaccine hesitancy had a strong negative association with behavioral intention ( b  = − 0.75, p  < 0.001), which indicated participants who were more hesitant to get COVID-19 vaccines had lower levels of behavioral intention to receive the COVID-19 vaccine in the future. Therefore, H2 was also supported.

We also examined the mediating role of COVID-19 vaccine hesitancy in the relationship between knowledge about COVID-19 vaccines and behavioral vaccination intention (RQ2). The analysis revealed that COVID-19 vaccine hesitancy fully mediated the association. More specifically, as indicated in Fig.  1 , the level of knowledge on COVID-19 vaccine was significantly associated with vaccine hesitancy for COVID-19, which in turn significantly affected the behavioral intention to get the vaccines. In addition, knowledge about COVID-19 vaccines was not directly associated with the COVID-19 vaccine intention ( b  = 0.11, p  = 0.16). The total standardized effect was significant ( b  = 0.68, p  < 0.001, 95% CI [0.59, 0.77]). Table 2 indicates the results of the mediation analysis of this study.

Using qualitative and quantitative data, the current research examined the types of misinformation circulated among the U.S. public about COVID-19 vaccines and how accuracy of people’s knowledge about COVID-19 vaccines related to their vaccine hesitancy and behavioral intentions to get vaccinated. Based on an open-ended question in an online survey of full-time workers, we asked what specific misinformation participants had heard about the COVID-19 vaccines for the qualitative Study 1. Using another online survey of college students, the quantitative Study 2 measured the level of knowledge about COVID-19 vaccines, multiple aspects of vaccine hesitancy, and behavioral intention of vaccination. While many participants in Study 1 (about 41%) reported not having heard of any misinformation, and many participants in Study 2 correctly answered knowledge questions about COVID-19 vaccines (69 to 98% depending on the question), findings of our study still raised some concerns about the prospect of overcoming vaccine misinformation and hesitancy.

Content analysis of the open-ended responses in Study 1 revealed that 57.6% of the participants reported being exposed to one or more pieces of COVID-19 vaccine misinformation. More than half of the 14 unique types of misinformation contained inaccurate information about health effects (e.g., COVID-19 vaccines are dangerous, harmful, or cause DNA alterations) while a third contained conspiratorial misinformation about the vaccines. While a small number of deaths have been linked to COVID-19 vaccines, these were extremely rare cases out of the total vaccinated population (0.002%; CDC, 2021 26 ), and the causes of these deaths were not always directly linked to vaccination. While this sample was small, it reflected typical working adults in the US and suggested that a sizable number of people were exposed to misinformation about the purpose and effectiveness of COVID-19 vaccines. In addition, the common type of misinformation about the vaccines seemed consistent in nature with broader conspiracy theories about the pandemic being created by the Chinese government or other powerful people to exert control in the world 11 , 12 .

The results of Study 1 also resonated with those of Study 2 as significant numbers of participants in Study 2 inaccurately answered knowledge questions about COVID-19 vaccines. For example, over 30% of participants answered “true” for the following false statement: COVID-19 vaccines can give you COVID-19. More than 25% of participants answered “it can be safer to get a disease than to get its vaccine” or “COVID-19 vaccines can cause infertility.” Slightly over 15% of participants answered true for this false statement: COVID-19 mRNA vaccines can alter human DNA. Although not as frequent as the misinformation related to microchip, these aforementioned statements were all identified in Study 1 as types of misinformation participants had heard.

When participants were misinformed about the vaccines and their effects, we expected their inaccurate knowledge would be associated with more vaccine hesitancy and less behavioral intention of getting vaccinated. The results from Study 2 supported those hypotheses and also showed a full mediation of vaccine hesitancy between the level of knowledge and behavioral intention. This finding supported Dubé et al.’s conceptual model 2 suggesting knowledge as one of the key internal factors of vaccine hesitancy and also confirmed previous findings that conspiratorial thinking was highly associated with anti-vaccination attitudes 4 , 21 , 22 , 23 , 25 . The fact that many participants of Study 2 believed misinformation about COVID-19 vaccines as true and correct information as false was alarming and consistent with the high level of vaccine hesitancy and low vaccination rate in the US compared to other countries. As of March 24th, 2022, 65.4% of the U.S. population are fully vaccinated for COVID-19. However, many European countries have over 75% (e.g., Germany, Italy, Portugal) or 70% (e.g., UK, Sweden) vaccination rates, and countries that started vaccination much later than the US and did not have their own vaccine development such as Japan (79%) or South Korea (86%) are catching up quickly 27 . With the US’s close neighbor Canada achieving an 84% vaccination rate, there appears to be some unique vaccine hesitancy factors operating for the U.S. public compared to other developed countries.

Several factors potentially influencing the U.S. exposure to vaccine misinformation and vaccine hesitancy deserve further exploration and study. First, was the anti-vaccination movement more intense in the US media outlets consumed in the US? Were people in the US influenced to a greater extent than those in non-US developed countries by the conspiracy theories spread by anti-vaxxer organizations? Second, were vaccination-related issues more politicized in the US compared to other countries given the timing of the 2020 national election and socio-political and cultural contexts surrounding the election? Other research has shown that vaccine hesitancy is affected by multiple factors including social culture and politics 2 , 17 . In our Study 2, participants who identified as conservatives were less likely to get vaccinated than those who identified as moderate or liberal. In the midst of the COVID-19 pandemic, the U.S. public experienced a highly contentious presidential election, generating a variety of politically motivated misinformation about COVID-19 and other topics. It seems plausible that the amount of mis/disinformation including those related to the pandemic and vaccines altogether increased immensely due to the competition between then President Donald Trump and Presidential candidate Joe Biden before and during the election period. Use of misinformation as a political weapon might have negatively affected U.S. public health decisions. A recent study showed higher rates of vaccine hesitancy in counties with high support for Donald Trump and this vaccine hesitancy gap widened over the study period (i.e., January 2021 to May 2021) 28 . This research also confirmed the politicized public health recommendations.

Limitations and future directions

A few limitations are worth noting to caveat our findings and to improve future research on this topic. First, many participants in Study 1 indicated they had not been exposed to COVID-19 vaccine misinformation, which might be a positive sign. However, we had no way to verify whether they truly had not been exposed to misinformation, or whether they were exposed to misinformation and believed it to be factual. Considering the wide range of participants (2.5% to 31.3%) who had incorrect answers on the knowledge test in Study 2, we could not completely ignore the possibility that Study 1 participants (who were slightly older and full-time professionals) were exposed to various types of mis/disinformation without recognizing them as inaccurate.

Second, for both Study 1 and 2, we collected cross-sectional data with which we could not test any causal relationships. Although previous evidence has shown that exposure to and belief in vaccine misinformation significantly affects vaccine hesitancy and uptake 4 , 21 , 22 , 23 , 25 , the current study’s data only show correlational relationships between these variables. People involved in the anti-vaccination movement who are highly vaccine hesitant and refusing to get vaccinated might be selectively exposing themselves to more mis- and disinformation due to their beliefs and social interactions within homogeneous networks (i.e., echo-chamber). We think bidirectional relationships would be plausible between knowledge of vaccines and vaccine hesitancy (and refusal) and until longitudinal data are collected, no definite causal relationship can be claimed.

Third, despite the fact that we had a sizable sample in both Study 1 ( n  = 505) and Study 2 ( n  = 441), both samples were somewhat homogeneous in their race/ethnicity and educational composition with over 70% of each sample being white and college-educated. However, the two samples were from different populations as Study 2 sample had close to 70% females and all college students while Study 1 had less than 50% females and consisted mostly of working professionals. Thus, findings from this study cannot be generalized to the U.S. population given the recent Census showing that only 63.7% of the U.S. population are comprised of non-Hispanic Whites 29 . Also, those who were educated in some college or higher level were approximately 62% of the U.S. population; thus, our samples over-represented highly educated people. Research has shown the relationship between education level and vaccine hesitancy in the US was like a U-shape since those with less than high school education and those with PhDs were the most hesitant to get COVID-19 vaccines 28 . King et al. suggested that the actual number of people refusing to get vaccinated would be higher than what their findings showed, given that their sample was more highly educated than the general U.S. population 28 . Similarly, in the current study, the scale of misinformation exposure and its negative relationships with vaccine hesitancy and refusal could be larger than what was reported.

Finally, the current study did not examine other potential reasons for participants believing in mis/disinformation of COVID-19 vaccines other than their inaccurate knowledge. Some of them might have a unitary conspiratorial worldview that made them more vulnerable toward particular mis- and disinformation about vaccines 21 . Others might have had past experiences involving negative health reactions to vaccines, making them hesitant toward other types of vaccines and potentially more susceptible to vaccine mis/disinformation. A future study may adopt an in-depth interview to probe this issue further so we can trace the process of people being exposed to mis/disinformation and believing them as true.

Method for study 1

Participants.

This data collection took place in March, 2021 upon receiving an approval from the Institutional Review Board (IRB) of the University of Oklahoma. All methods were carried out in accordance with relevant guidelines and regulations of the IRB. At that time, COVID-19 vaccines were being administered to individuals at higher risk such as health care workers, first responders, people who were 65 and older, and people with underlying medical conditions nationwide. Participants were recruited via Mechanical Turk (MTurk) using convenience sampling and a total of 505 individuals participated in this study. Informed consent was obtained from every participant. Of those participants, 46.3% were women, 70.4% were White, 78.5% had a Bachelor’s degree or higher degree, and the average age was 37.54 years ( SD  = 10.30). They all lived in the United States and worked at least 40 h a week.

Participants were asked to answer the following open-ended questionnaire: “Have you heard about any misinformation or false claims related to the COVID-19 vaccines? If so, please write down any false information you have come across.” Once the qualitative responses were collected from participants, they were coded by three researchers who have a Ph.D. in Communication or Psychology. In terms of coding procedures, first, the three coders read the same 40 responses and created their own coding schemes independently. After that, they compared and contrasted their coding schemes with the other two coders’ and generated a common coding scheme along with specific coding rules after an intensive discussion. For example, one coding theme was “COVID-19 vaccines have a microchip.” For that theme, the coders agreed to have a rule that specific responses mentioning a microchip should be coded as category number one. Next, to confirm whether the three coders understood the coding scheme and coding rules in a consistent way, they coded 50 additional responses and again compared their coding. Most were identically coded, though a small number of differences were found. For instance, two coders coded one response as category number seven while the third coded the same response as applicable to category number seven and eight. Those minor differences were resolved after another round of discussion and all coders agreed on the final coding scheme. Based on the agreement, the three coders divided up a total of 488 responses and coded 163 responses independently.

Method for study 2

Participants and procedure.

To examine the influence of knowledge about the COVID-19 vaccines on vaccine hesitancy, a cross-sectional survey was designed. This online survey was opened for data collection for three months from February 2021 to May 2021 upon receiving an approval from the IRB at the University of Oklahoma. All methods were carried out in accordance with relevant guidelines and regulations of the IRB. During that period of data collection, the COVID-19 vaccines had become available to the public in the United States. Respondents were recruited via convenience sampling from a departmental research pool (i.e., SONA) at the university. Students at the university who were taking courses from the department voluntarily signed up to join the research pool and they could select studies they wanted to participate in. A total of 597 responses were collected for the Study 2 and informed consent was obtained from all participants. Of them, 156 cases were excluded because of incomplete/missing information, leaving 441 responses available for the remaining analyses. Demographics of the participants ( n  = 441) are reported in Table 3 .

To be eligible for this study, participants were required to be aged 18 years or older and to have not received a COVID-19 vaccine yet. Participants were also informed about the study’s purpose, procedures, risks and benefits, compensation, voluntary nature of the survey, and confidentiality. Those who consented to participate in this study signed the online informed consent and then were asked to complete the following parts. First, they completed demographic information including age, gender, ethnicity, religion, political party affiliation, and political ideology. Next, they answered questions assessing their knowledge levels about COVID-19 vaccines. Third, they answered questions asking about their hesitancy toward COVID-19 vaccination, and finally, they estimated their behavioral intention to get a COVID-19 vaccine. At the end of the survey, we provided a comprehensive fact sheet about the COVID-19 vaccines. Participants received extra credit for their participation.

Knowledge about COVID-19 vaccines

To evaluate levels of knowledge (and possible misconceptions) about the COVID-19 vaccines, participants were presented with 10 statements (five true and five false) about the COVID-19 vaccines. An example of a true statement is “With most COVID-19 vaccines, you will need 2 shots to get the most protection” and for a false statement, “COVID-19 vaccines can cause autism”.

Participants were asked whether to the best of their knowledge these statements were true or false. The statements were provided based on the COVID-19 Vaccine Communication Handbook 11 including widespread myths and anti-vaccination misinformation. The number of correctly answered statements was summed to assess knowledge levels of the COVID-19 vaccines ( M  = 8.35, SD  = 1.87).

COVID-19 vaccine hesitancy (VH)

The Vaccine Hesitancy Scale (VHS) 28 was used to measure participants’ hesitancy levels to get vaccines in general. To reflect the focus of this study, we specified the items by replacing the word, “vaccines” with “COVID-19 vaccines”, and the word, “childhood” to “me”. For instance, we modified the statement “Childhood vaccines are important for my child’s health” to “COVID-19 vaccines are important for my health”. The VHS includes nine items including seven reversed items in the scoring of the scale (e.g., “COVID-19 vaccines are effective”, and “Getting a COVID-19 vaccine is a good way to protect me from disease”). They were measured on a 5-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree). As a result of confirmatory factor analysis (CFA), two items (i.e., item 5 and item 9) were excluded due to lower factor loadings (less than 0.4) 30 . The modified scale was valid to conduct further analyses: χ 2 (14) = 44.93, p  < 0.001, χ 2 /df = 3.21, CFI = 0.99, RMSEA = 0.07, and SRMR = 0.02. The items were averaged with higher scores indicating more hesitancy to get a COVID-19 vaccine ( M  = 2.71, SD  = 0.93).

Behavioral intention (BI) for COVID-19 vaccination

Based on Rothman et al.’s measurement 31 , the behavioral intention to get a COVID-19 vaccine was assessed with two items such as (a) if you were faced with the decision of whether to get the COVID-19 vaccine today, how likely is it that you would choose to get vaccinated? and (b) how likely would you be able to get the COVID-19 vaccine in the future? These items were measured with a 5-point Likert-type scale from 1 (very unlikely) to 5 (very likely). Higher scores on this variable indicate greater intention to receive the COVID-19 vaccine in the future ( M  = 3.30, SD  = 1.36) (see supplementary information).

Statistical analysis

Descriptive statistics including mean, standard deviation, skewness, and kurtosis, and intercorrelations of knowledge levels about COVID-19 vaccines, COVID-19 vaccine hesitancy, and behavioral intention to get COVID-19 vaccines are indicated in Table 4 . The percentage of true and false responses is indicated in Table 5 . The total of COVID-19 vaccine knowledge scores ranged from 2 (6 respondents) to 10 (154 respondents), with a mean of 8.35 ( SD  = 1.87).

Descriptive and correlation analyses were conducted to confirm the normality of the data and to explore the participants’ demographic characteristics and the relationships among all the variables of this study using SPSS 26.0. Before conducting SEM, reliabilities and validities of the measurements were tested. We found that the measurements of this study had robust internal reliabilities as well as convergent and discriminant validities: the Cronbach’s α values were all above 0.80, and the factor loadings of the items of each construct, composite reliabilities (CR) and average variance extracted (AVE) were over 0.60, 0.80, and 0.60, respectively. The outcomes fulfilled the criterion of reliabilities and validities for all the constructs (see Table 6 ).

Structural equation modeling (SEM) was also performed to examine the measurement model and hypotheses of this study using AMOS 25.0. Maximum likelihood estimation was used to estimate the hypothesized model. To measure the overall fit of the suggested model, the following indices were used 30 : χ 2 /df < 3, the comparative fit index (CFI) > 0.90, the root mean square error of approximation (RMSEA) < 0.07, and the standardized root mean square residual (SRMR) < 0.08. To test the mediating effect of COVID-19 vaccine hesitancy, bootstrapping was applied with 2000 bootstrapped replications and 95% confidence intervals (CIs). All the estimates were indicated in standardized scores. We controlled gender, ethnicity, religion, political party affiliations, and political ideology as they have been significantly related to COVID-19 vaccine hesitancy and behavioral intentions to get the vaccines 32 , 33 , 34 , 35 .

Based on a mixed-method approach, the current study examined various types of misinformation related to COVID-19 vaccines, circulated among the U.S. public, and how accuracy in knowledge of COVID-19 vaccines related to vaccine hesitancy and behavioral intention. The study found from the analysis of open-ended answers that people were exposed to conspiratorial misinformation about COVID-19 vaccines such as the vaccines including a microchip or them being dangerous and harmful, causing death, or altering DNA. The knowledge test utilized in the quantitative study also confirmed many people believed such misinformation related to COVID-19 vaccines as true and inaccurate knowledge seemed to increase their vaccine hesitancy and decrease behavioral intention to get vaccinated. Consistent with previous studies, the findings of this study confirmed the importance of accurate knowledge and influence of misinformation related to vaccine hesitancy and refusal. Public health campaigns and strategies need to be strengthened to combat the conspiracy spread by the anti-vaccine movement and effectively intervene in the circulation of mis/disinformation related to COVID-19 vaccines.

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School of Media and Communication, Korea University, 145 Anam-Ro, Seongbuk-Gu, Seoul, 02841, South Korea

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All authors were involved in relevant data analysis and writing the main manuscript. S.K.L. was involved in both conceptualization and design of Study 1 and 2, while J.S. was involved in mainly Study 2 data analysis and writing of the results and preparing Fig. 1 and Tables 2 , 3 , 4 , 5 , 6 ; S.J. and S.C. were involved mainly in Study 1 data analysis and writing of the results. S.J. prepared Table 1 .

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Lee, S.K., Sun, J., Jang, S. et al. Misinformation of COVID-19 vaccines and vaccine hesitancy. Sci Rep 12 , 13681 (2022). https://doi.org/10.1038/s41598-022-17430-6

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Acceptance of a Covid-19 vaccine is associated with ability to detect fake news and health literacy

Univ.Bordeaux, Inserm, Bordeaux Population Health Research Center, U1219, F-33000, Bordeaux, France

K Ouazzani-Touhami

Polyclinique Inkermann, groupe Elsan, F-79000 Niort, France

Kap Code, F-75010 Paris, France

Kappa Santé, F-75002 Paris, France

S Schück

Associated data.

All data generated or analysed during this study are included in this published article. The full dataset is available upon request from the CONFINS cohortteam.

During the Covid-19 pandemic fake news has been circulating impacting on the general population’s opinion about a vaccine against the SARS-CoV-2. Health literacy measures the capacity of navigating health information.

We used data from a prospective national online cohort of 1647 participants. Descriptive statistics, Chi2 and ANOVA independence tests and two multivariable multinomial regression models were performed. Interactions between each variable were tested.

Detection of fake news and health literacy scores were associated with intention to get vaccinated against SARS-CoV-2 (p < 0.01). The risk of being “anti-vaccination” or “hesitant”, rather than “pro-vaccination”, was higher among individuals reporting bad detection of fake news, respectively OR = 1.93 (95%CI = [1.30;2.87]) and OR = 1.80 (95%CI = [1.29;2.52]). The risk of being in “hesitant”, rather than “pro-vaccination” was higher among individuals having a bad health literacy score (OR = 1.44; 95%CI = [1.04;2.00]). No interaction was found between detection of fake news and health literacy.

Conclusions

To promote acceptance of a vaccine against SARS-CoV-2, it is recommended to increase individuals’ ability to detect fake news and health literacy through education and communication programs.

Introduction

Misinformation about health-related subjects represents a public health threat. Around Covid-19, fake news has been circulating extensively since the beginning of the pandemic thus making the general audience doubt the veracity of health and political authorities concerning information around the SARS-CoV-2. 1 In February, the World Health Organisation’s Director-General declared the global ‘over-abundance’ of Covid-19 information an ‘infodemic’.

One of the challenges for health communication introduced by Covid-19 has been to tackle this increasing amount of false content on several communication channels, including social media platforms. 2 Common fake news about Covid-19 include the fact that only older adults can be infected, that swallowing bleach can kill the virus or that the virus is the product of a laboratory. 3 For instance, in Iran, hundreds of people died after ingesting alcohol in a bid to treat Covid-19 as a result of misinformation circulating on social media. 4

The large spread of misinformation about Covid-19 might be explained by the initial scarce knowledge about the virus among the scientific community and politicians. Confusion generated by the plethora of news across media could have nourished misinformation and lack of trust in scientific evidence, especially in a situation where people have been looking for immediate and reassuring answers regarding the SARS-CoV-2. 5 Spread of fake news has been fueled also by public figures and politicians who have been giving, in some cases, contradictory information, as the controversy over the wearing of masks in France. 6

Of particular concern is the misinformation concerning Covid-19 vaccines, fueled by conspiracies (e.g., economic interests) and rumors of safety. 7 Misinformation and misbeliefs can influence willingness to follow the recommendations by health and political authorities on vaccination. A number of studies found that fake news are a main cause of vaccine hesitancy, 8–10 defined as the delay in acceptance or refusal of vaccination despite availability of vaccination services. 11 A global survey in 19 world countries has showed that rates of hesitancy concerning a vaccine against the SARS-CoV-2 range from 11.4% (China) to 45.1% (Russia), 12 which are not negligible numbers. In the Covid-19 pandemic context, it is important to mitigate the impact of misinformation on the decision of not getting vaccinated.

As a means to counteract misinformation, health literacy (HL) is the extent to which people can access, understand, appraise and apply health-related information through all communication channels. 13 Measured through ad hoc scales, 14 health literacy can assess the motivation and capacity of people to make informed decisions based on trustworthy information. According to the health literacy theory, it can be hypothesized that engaging in a positive health information seeking can help better navigate news and, consequently promote recognition of misinformation. 15 Therefore, HL appears to be in close correlation with detection of fake news. Recent studies have explored separately the spread of fake news, 16 , 17 health literacy 18 , 19 and vaccine hesitancy 20 , 21 in the Covid-19 era, without the exploration of the interrelation of these three factors.

Detection of fake news might represent a proxy for other proximal and intermediate causal factors and mechanisms that could explain the acceptance of a vaccine. The mediating role of HL has been assessed in several associations like between socio-economic status and health, 22 population characteristics and use of health services 23 or between education and health outcomes. 24 The role of HL in the relation between detection of fake news and vaccine hesitancy has not been explored,yet.

This study aimed to investigate the relations between intention to get vaccinated against the SARS-CoV-2 and the ability to detect fake news about Covid-19 as well as health literacy among a population-based sample of French adults. We hypothesized that higher acceptance of a vaccine against the SARS-CoV-2 was associated with higher ability to detect fake news related to Covid-19 and that this association was mediated by health literacy levels.

Design, participants and ethics approval

The data used in this study are from a prospective online cohort exploring the impact of Covid-19 and lockdown on the health and wellbeing of French adults (CONFINS cohort, www.confins.org ). The baseline questionnaire was administered in the period between April 8 th and May 11 th 2020. Participants were recruited via advertisement on traditional (press, radio and TV spots) and social media (e.g., Facebook, LinkedIn). Eligibility criteria were being able to read and understand French, being aged 18+, being confined at the moment of the inclusion and not having declared “other” for the variablesex.

Ethics approval was obtained from the French Committee for the Protection of Individuals (Comité de Protection des Personnes—CPP, nr. 46–2020) and the French National Agency for Data Protection (Commission Nationale Informatique et Libertés—CNIL, nr. MLD/MFI/ {"type":"entrez-nucleotide","attrs":{"text":"AR205600","term_id":"21503224"}} AR205600 ). Participants had to electronically sign a consent form before starting the completion of the questionnaire.

The outcome variable was the intention to get vaccinated against the SARS-CoV-2. We used the item “ Would you be willing to get vaccinated against coronavirus even if the vaccine has not yet been fully proven effective? ”. Response modalities were: “No”, “Yes” or “I do not know”. We established a priori that the answer “No” corresponded to the “anti-vaccination group”, the answer “Yes” to the “pro-vaccination” group and the answer “I do not know” to the “hesitant group”.

The primary exposure variable was detection of fake news. Participants had to establish whether the following 8 items (i.e., potential fake news) were true (1 point), false (0 point) or I do not know (0 point): “The Covid-19 virus has been developed in a laboratory”, “The virus can be transmitted through the air”, “Only people over 70 years of age can die from Covid-19”, “The virus can only be transmitted by people who have symptoms such as fever and cough”, “All people infected with the virus develop symptoms”, “Drinking very hot drinks prevents contamination from the virus”, “The virus survives on inert surfaces such as doorknobs or bars in public transportation” and “Everybody should wear a mask to effectively protect themselves from the virus”. A score was calculated from 0 to 8, with higher scores corresponding to better detection of fake news. The categories were: bad detection of fake news (0–5) and good detection of fake news. 6–8

The secondary exposure variable was HL measured through a 5-item scale scored from 0 to 15. Answers to each item were based on a 4-point Likert scale (from 0 “completely disagree” to 3 “completely agree”). Items were: “I compare health information from different sources”, “When I discover new health information I verify if it is true or not”, “I decide what health information is best for me”, “I can state if health information is adapted to my situation or not” , and “I enquiry health professionals on the quality of information I find” . Higher scores corresponded to better HL. Obtained categories were: bad HL (0–9) and good HL. 10–15 The HL questionnaire was developed and used by the French Public Health Agency ( Santé publique France ). 25

Other variables used for the description of the sample and for adjustment in the models included sociodemographic characteristics and vaccine-related items: sex (male, female); age (18–20 years vs 20 years), marital status (single, in a couple since at least three years without being married, married, divorced/widower); having children (no, yes); being a student (no, yes); studying or working in the health domain (no, yes); self-perceived health before the lockdown (poor, good); lifelong medical history including at least one disease among cardiovascular disease, high blood pressure, diabetes, chronic digestive disease, cancer, asthma or other respiratory problems, mental disorders or other illnesses (no/I do not know, yes); being up-to date with vaccination (no, yes, I do not know); and being regularly vaccinated against flu (no,yes).

A descriptive analysis was performed, presenting all variables and measures in the form of numbers and percentages for qualitative variables and means and standard deviations (SD) for quantitative variables, stratified by the degree of acceptance of the vaccine against the SARS-CoV-2 (anti-vaccination, pro-vaccination, hesitant). Chi2 and ANOVA independence tests were performed in order to compare the three groups. A first multivariable multinomial regression model investigated the association between intention to get vaccinated against the SARS-CoV-2 and the capacity to detect Covid-19-related fake news as well as levels of HL, adjusted on the sociodemographic variables which were significantly different among the three groups. Interactions between each variable in the model were also tested. A second model, identical to the first, but with additional stratification between the ability to detect fake news and the level of HL was performed to study the association between the intention to get vaccinated against the SARS-CoV-2 and the capacity to detect Covid-19-related fake news according to HL levels. Statistical significance was defined with a p-value<0.05. Data were analyzed with SAS® version 9.4.

Sample characteristics

A total of 2344 individuals completed the CONFINS questionnaire. We performed a complete-case analysis, thus excluding participants not meeting the inclusion criteria and with missing values in the variables under study. The final sample was composed of 1647 participants: 18.6% (306/1647) anti-vaccination, 10.9% (180/1647) hesitant and 70.5% (1161/1647) pro-vaccination ( Figure 1 ).

Study flow-chart.

Table 1 presents the sociodemographic characteristics of the study population, plus the variables HL and detection of fake news stratified by the three groups of vaccine acceptance. The total sample was composed of 78.5% (1293/1647) female participants. The mean age was 28.2 years (standard deviation ±11.5).

Description of the study population stratified by vaccination groups (n = 1647)

Concerning the detection of fake news, the mean score for the total population was 6.5 (SD ±1.0) out of 8. Those reporting a bad score (0–5) were 15.2% (250/1647) vs 84.8% (1397/1647) reporting a good score. 6–8 The total study population reported a mean of 10.2 (SD ±2.9) out of 15 for HL. Those reporting a bad HL score (0–9) were 38.2% (629/1647) vs. 61.8% (1018/1647) reporting a good HL score. 10–15 We observed significant associations between intention to get vaccinated against the SARS-CoV-2 and sex (p < 0.01), studying or working in the health domain (p < 0.01), having their vaccination up-to-date (p < 0.01), being regularly vaccinated against flu (p < 0.01) and the two exposure variables detection of fake news (p < 0.01) and HL (p < 0.01).

We also assessed which were the most challenging fake news for participants. The item which rose more problems to participants was the one on the transmission of the virus through the air, with similar percentages between “true” and “false” answers, 43.3% and 36.9% respectively. Participants were more doubtful about the item on the development of the Covid-19 virus in a laboratory (29.1% answered “I do not know”). See Supplementary Table S1 in the Supplementary Material for more details.

Intention to get vaccinated and associations with detection of fake news and HL

Table 2 presents the results of the first multivariable multinomial regression model. The reference category was the “pro-vaccination” group. Based on their significance in the descriptive analysis (p < 0.05), we inserted in the model the following covariates: sex, being regularly vaccinated against flu, having up-to-date vaccination and studying or working in the health domain. We also inserted in the model the only interaction we found, i.e., between being regularly vaccinated against flu and studying or working in the health domain (p = 0.04). No interaction was found between detection of Covid-19-related fake news and HL (p = 0.55).

Effect of capacity to detect fake news and health literacy on intention to get vaccination against Covid-19, estimated with a multivariate multinomial regression model (N = 1647)

Adjusted on sex, being regularly vaccinated against flu, having their vaccination up-to-date and studying or working in the health domain. OR: odd ratio; 95%CI: confidence interval at 95%.

The risk of being “anti-vaccination” or “hesitant”, rather than “pro-vaccination”, was higher among individuals reporting a bad detection of fake news, compared to individuals with a good detection of fake news, and the risk of being “hesitant” was more important than the risk of being “anti-vaccination”, respectively OR = 1.93 (95%CI = [1.30;2.87]) and OR = 1.80 (95%CI = [1.29;2.52]), adjusted on covariates. The risk of being “hesitant”, rather than “pro-vaccination” was higher among individuals having a bad HL score, compared to individuals with a good HL score (OR = 1.44; 95%CI = [1.04;2.00]), adjusted on covariates.

Among participants studying or working in the health domain, the risk of being “anti-vaccination” or “hesitant” was higher among those who were not regularly vaccinated against flu compared to those who were vaccinated, and the risk of being “anti-vaccination” was greater than that of being “hesitant”, respectively OR = 2.48 (95%CI = [1.44;4.26]) vs. OR = 1.94 (95%CI = [1.12;3.35]), adjusted on covariates. Among those studying or working in a field other than the health one, the odds of being “anti-vaccination” were 4.5 times higher among those who were not regularly vaccinated against flu compared with those who were vaccinated (OR = 4.5; 95%CI = [2.28;8.86]), adjusted on covariates.

The same model was performed with a supplementary forced-interaction between detection of fake news and HL in order to obtain stratified results according to HL ( Table 3 ). Among individuals having a bad HL score, the risk of being “anti-vaccination” or “hesitant”, rather than “pro-vaccination”, was higher among those having a bad detection of fake news, compared to those with a good detection of fake news, and the risk of being “anti-vaccination” was higher than the risk of being “hesitant”, respectively OR = 2.18 (95%CI = [1.36;3.49]) and OR = 2.08 (95%CI = [1.17;3.71]), adjusted on covariates. Among individuals having a good HL score, the risk of being “hesitant”, rather than “pro-vaccination”, was higher among those reporting a bad detection of fake news, compared to those with a good detection of fake news (OR = 1.83; 95%CI = [1.05;3.18]), adjusted on covariates.

Effect of detection of fake news on intention to get vaccination against SARS-CoV-2, stratified on HL, estimated through a multivariate multinomial regression model (N = 1647)

Adjusted on sex, being regularly vaccinated against flu, having their vaccination up-to-date and studying or work in health domain with an interaction between being regularly vaccinated against flu and study or work in health domain and an interaction between detection of fake news and health literacy; OR: odd ratio; 95% CIs: confidence interval at 95%.

Main findings of this study

The present study assessed the associations and their combinations between intention to get vaccinated against the SARS-CoV-2 and detection of fake news about Covid-19 as well as HL. We intended to verify whether the detection of fake news changed or not the intention to get vaccinated based on the levels of HL. We observed that there was no interaction between the ability of detecting fake news and HL, but that both these variables were associated with the acceptance of a vaccine against the SARS-CoV-2. HL did not have a moderating role, thus confuting our hypothesis. A possible explanation might be that our fake news scale was not too discriminating (i.e., questions were not too difficult) and that individuals with a low HL score could provide a good answer. Only near one participant out of six reported bad detection of fake news about the pandemic. However, the stratification of the results of the second model confirmed that bad HL and bad detection of fake news were both related to being hesitant or anti-vaccination.

What is already known on this topic

In a previous observational study conducted in Italy, 16 among more than 2000 online articles related to Covid-19, articles containing fake news were shared more than 2 million times accounting for 78% of the total shares of all reviewed articles. This high percentage throws light on the fake news phenomenon and calls for an improvement of HL to better navigate information on the net. Previous research also reported that HL in general helps identification of fake news 26 : this is especially true in crisis contexts like that generated by Covid-19. 18

A group of researchers from Germany developed a SARS-CoV-2-related HL measure and administered it among 1153 adult Internet users. 27 A total of 15.2% of participants were found to have “inadequate HL”, 34.9% had “problematic HL”, and 49.9% had “sufficient HL” with reference to Covid-19. The item where participants scored the lowest was that on the capacity to “judge if information on SARS-CoV-2 and the SARS-CoV-2 epidemic in the media is reliable”.

Concerning fake news and vaccination, the association between misinformation and vaccine hesitancy is well documented. 8 A study conducted in England noted a link between belief in Covid-19 conspiracies and an increase in vaccine hesitancy. 28 On the other hand, previous research has also found that HL is associated with preferences for vaccination in general. 29 Following the inoculation theory, preemptive refutation of misinformation weakens the misinformation, just as a medical vaccine is often comprised of weakened virus. 30

What this study adds

This was one of the first studies assessing through a large cohort the association between intention to get vaccinated, misinformation and HL in relation to Covid-19. Our findings allow for a better understanding of the relation between our variables of interest providing a solid ground for policymakers and health professionals to design and implement programs to tackle Covid-19-related ‘infodemic’.

We found that intention to get vaccinated was associated with agreement with fake news. Even if this result has been already assessed in studies describing the relation between spread of misinformation and decrease in immunization, 8 , 31 our study was specifically addressing Covid-19, which has not been explored before. The fight against SARS-CoV-2-related fake news is pivotal, especially considering that even an effective vaccine against Covid-19 runs the risk of falling victim of fake news by increasing vaccine hesitancy. Correcting misinformation should be considered as a vitally important science and health policy activity.

Our study also found a significant association between vaccine hesitancy and low levels of HL. Thus, evidence is provided for the need to propose a series of strategies to help the general public find the correct information and thereby better engage them to adhere to correct guidelines. 5

Vaccine communication strategies should support the identification of fake news and promote HL in target audiences: consistent communication by institutions is crucial to building public confidence in vaccine programs.

Limitations

This study was large and diverse but not representative of the national population. As any online survey, participants who have a special interest in the topic might have been recruited (i.e. self-selection bias). Caution is needed in generalising from the prevalence findings. Another limitation is the labelling of the misinformation items as these particular beliefs are likely contextual and subjective. However, we proposed the most diffused fake news in the French context at the moment of the survey. Finally, we excluded some individuals because of missing values in the variables of interest. The final sample was not extensively affected counting more than 1600 participants.

In conclusion, findings suggest that the spread of fake news can influence vaccine uptake, but also that better HL can help evaluate information about Covid-19. For this, interventions are needed at the education and political levels to contain misinformation and promoteHL.

Role of the funding source

The i-Share and CONFINS team are currently supported by an unrestricted grant of the Nouvelle-Aquitaine Regional Council (Conseil Régional Nouvelle-Aquitaine) (grant N° 4370420) and by the Bordeaux ‘Initiatives d’excellence’ (IdEx) program of the University of Bordeaux (ANR-10-IDEX-03-02). The team has also received grants from Public Health France (Santé Publique France, contract N° 19DPPP023–0) and the Nouvelle-Aquitaine Regional Health Agency (Agence Régionale de Santé Nouvelle-Aquitaine). The funding bodies were neither involved in the study design, or in the collection, analysis, or interpretation of thedata.

Data availability statement

Ethical standards.

The study follows the principles of the Declaration of Helsinki and the collection, storage and analysis of the data comply with the General Data Protection Regulation (EU GDPR).

The study was approved by the French Committee for the Protection of Individuals (Comité de Protection des Personnes—CPP IDF X, nr. 46–2020) and by the National Commission on Informatics and Liberty (Commission Nationale de l’Informatique et des Libertés -CNIL, nr. MLD/MFI/ {"type":"entrez-nucleotide","attrs":{"text":"AR205600","term_id":"21503224"}} AR205600 ).

Supplementary Material

Supplementary_material-fn_fdab028, acknowledgements.

We wish to thank all members of the CONFINS group including the i-Share, Kappa Santé and Kap Code team members: Stéphane Schück, Nathalie Texier and Christophe Tzourio conceived and designed the study cohort; Edwige Pereira, Garance Perret, Aude Pouymayou and Mathilde Pouriel analysed the data; Julie Arsandaux, Shérazade Kinouani, Mélissa Macalli and Ilaria Montagni wrote and revised manuscripts; Raphaël Germain and Clothilde Pollet were in charge of the regulatory affairs; Vanessa Marie-Joseph, Adel Mebarki, Elena Milesi and Marie Mougin were in charge of the study communication. The authors are also grateful to all the participants who volunteered to take part in the study.

I Montagni, Researcher and Lecturer

K Ouazzani-Touhami, Public Health Medical Doctor

A Mebarki, General Co-Director

N Texier, Director

S Schuck, Chief Executive Officer

C Tzourio, Professor and Head

Contributor Information

I Montagni, Univ.Bordeaux, Inserm, Bordeaux Population Health Research Center, U1219, F-33000, Bordeaux, France.

K Ouazzani-Touhami, Univ.Bordeaux, Inserm, Bordeaux Population Health Research Center, U1219, F-33000, Bordeaux, France. Polyclinique Inkermann, groupe Elsan, F-79000 Niort, France.

A Mebarki, Kap Code, F-75010 Paris, France.

N Texier, Kap Code, F-75010 Paris, France. Kappa Santé, F-75002 Paris, France.

S Schück, Kap Code, F-75010 Paris, France. Kappa Santé, F-75002 Paris, France.

C Tzourio, Univ.Bordeaux, Inserm, Bordeaux Population Health Research Center, U1219, F-33000, Bordeaux, France.

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Detecting fake news assignment vaccines answers

Biology Labs 1 And 2

Lab 1: Follow the instructions and complete the assignment below. Submit your answers through the Lab 1 Assignment on Blackboard.

Lab 1: Detecting and Debunking Fake News

Fake news sites are designed to look like real news, but do not follow the same journalistic standards that you would expect from a real news source. The motive for each of these sites is to attract as many visitors as possible to maximize revenue from selling ad space. This is especially dangerous because fake news sites are flooding the world with information that is misleading at best, but is often completely false, and presenting it as true. In this activity, you will learn how to identify fake news while you investigate the authenticity of two sources. As you walk through the steps outlined below, you will see question/note- taking/recording prompts (indicated by letters). Be sure to record your thoughts and answer these questions, as your notes will help you to answer the questions at the end of this exercise. 1. Access and read the news articles referenced below (2 links provided for each, in case one does not cooperate with your computer ). Article 1: “Vaccines Kill More People Than Sharks, Alligators, Bears, Snakes And Spiders Combined” by D. Samuelson. Accessed from: http://vaccines.news. Sept 7 2016.

 http://bit.ly/2fOcL0h

 http://web.archive.org/web/20161031095438/http:/vaccines.news/2016-08-23-vaccines- kill-more-people-than-sharks-alligators-bears-snakes-and-spiders-combined-2.html

Article 2: “CDC officials worry that new flu vaccine recommendations could reduce use” by Lena H. Sun. Accessed from: http://washingtonpost.com. Sept 29 2016.

 http://wapo.st/2dh25ol

 https://www.washingtonpost.com/news/to-your-health/wp/2016/09/29/cdc-officials- worry-that-new-flu-vaccine-recommendations-could-reduce- use/?noredirect=on&utm_term=.22cbcce5b56c

2. Now that you’ve read both articles, we are going to a few aspects of the websites for each article. Clickbait Headlines: Fake news sites often rely on clickbait headlines that are sensational, provocative, and grab your attention. This XKCD.com graphic gives good examples of what headlines may have looked like through the 20th century using clickbait terminology. A. Evaluate both articles 1 and 2 for evidence of

clickbait. Take notes on your findings Author Credibility: Fake news sites often have anonymous authors, authors with false names, or

http://bit.ly/2fOcL0h http://web.archive.org/web/20161031095438/http:/vaccines.news/2016-08-23-vaccines-kill-more-people-than-sharks-alligators-bears-snakes-and-spiders-combined-2.html http://web.archive.org/web/20161031095438/http:/vaccines.news/2016-08-23-vaccines-kill-more-people-than-sharks-alligators-bears-snakes-and-spiders-combined-2.html http://wapo.st/2dh25ol https://www.washingtonpost.com/news/to-your-health/wp/2016/09/29/cdc-officials-worry-that-new-flu-vaccine-recommendations-could-reduce-use/?noredirect=on&utm_term=.22cbcce5b56c https://www.washingtonpost.com/news/to-your-health/wp/2016/09/29/cdc-officials-worry-that-new-flu-vaccine-recommendations-could-reduce-use/?noredirect=on&utm_term=.22cbcce5b56c https://www.washingtonpost.com/news/to-your-health/wp/2016/09/29/cdc-officials-worry-that-new-flu-vaccine-recommendations-could-reduce-use/?noredirect=on&utm_term=.22cbcce5b56c Lab 1: Follow the instructions and complete the assignment below. Submit your answers through the Lab 1 Assignment on Blackboard.

authors who have no journalistic background at all. Fortunately, author credentials, background, and other information can be quickly located through a simple internet search. B. Look up the author of each article (try Googling the author’s name and their news and see if

you can find a biography for each) and evaluate their credibility. Fact-Checking Claims: Real scientific news provide appropriate citations for any facts or figures. Fake news sites often provide falsified or misleading data/statistics that often do not have any citations at all. C. Review the articles 1 and 2, and for each article, see if you can identify information that

should have a citation but doesn’t. Save these for later reference. 3. Let’s consider a couple of claims from Article 1 (the “Vaccines…” article): Claim 1: “poison-filled injection are associated with at least 5,000 deaths annually, according to data from the Vaccine Adverse Events Reporting System (VAERS)” This statement has a citation. Let’s check it. Go to the VAERS database here: https://wonder.cdc.gov/vaers.html (About tab).

 Click on the “VAERS Data Search” button.

 Scroll down to “2. Select symptoms” and choose 10011906 (Death).

 Scroll down to “7. Select report completed dates” and set the date range from Jan 2015 to Dec 2015.

 Click on the “send” button on the right side of the page. D. How many VAERS reported deaths were there in 2015? Repeat for 2017 (you can press the

back button in your browser, and only have to change the year), or any other year of your

Claim 2: “Recognizing that VAERS data constitutes as little as 10% of actual injury and death cases, the annual death rate from vaccines swells to about 39,000.” This claim is referring to a phenomenon called underreporting, where fewer instances of an event or behavior than what has actually occurred are recorded (or reported). Specifically, this article appears to be arguing that a number of deaths and injuries from vaccines are not being reported at all, and the current data are consequently flawed. Take another look at the VAERS website (About tab) https://wonder.cdc.gov/vaers.html:

 View the “Data Limitations and Cautions” section

 Click the link in that section http://vaers.hhs.gov/data/index to view information about VAERS Data (https://vaers.hhs.gov/data.html)

 Click the Guide to Interpreting VAERS Data link (https://vaers.hhs.gov/data/dataguide.html)

E. Summarize the VAERS database disclaimer on underreporting, in your own words.

Claim 3: “You are more than 200 times more likely to suffer mortality following a government- recommended shot, in other words, than you are dying from a bear, shark, alligator or any of the other aforementioned animals.”

https://wonder.cdc.gov/vaers.html https://wonder.cdc.gov/vaers.html http://vaers.hhs.gov/data/index https://vaers.hhs.gov/data.html https://vaers.hhs.gov/data/dataguide.html Lab 1: Follow the instructions and complete the assignment below. Submit your answers through the Lab 1 Assignment on Blackboard.

Let’s use our resources to figure out if this is true.

 Visit the CDC Wonder Underlying Cause of Death database at: https://wonder.cdc.gov/ucd-icd10.html. As you may notice, these data are based on causes of death recorded on formal death certificates.

 Click “I agree” at the bottom to search the database.

 Scroll down to “4. Select year and month” and select 2015.

 Scroll down to “6. Select cause of death” and select ICD-10 Codes (should be already pre-selected).

 Within the ICD-10 Codes browse window, o select code set V01-Y89 (External causes of morbidity and mortality), and click

“Open Fully” o select codes W53–W59 and X20 –X29 (covers all animal related mortalities) [you

can use “Control” and “Click” to select multiple fields] o Click on the “send” button on the right side of the page.

F. How many animal related deaths were recorded in 2015?

Let’s take this a bit further and look at what the CDC Wonder database has on vaccine- attributed deaths, compared to the VAERS Reports. Repeat this Underlying Cause of Death search, but this time, select ICD-10 codes indicating immunization and vaccine associated causes of death. Be sure to choose codes that directly attribute death to the vaccine with intentional administration of the vaccine, especially groups Y58 and Y59 (accidental poisoning or contamination with biological agents do not qualify). G. How many vaccine-attributed deaths are on record within the CDC database from 2015 (you

might need to click for “More Information” to learn more)?

Before we move on, take a moment and consider the CDC Wonder and VAERS websites, in terms of data verifiability and credibility. H. Follow the links below to read more about the types of data and data sources for each:

a. https://wonder.cdc.gov/wonder/help/about-cdc-wonder-508.pdf b. https://wonder.cdc.gov/vaers.html (first paragraph under the About tab)

Now, let’s compare mortality rates. According to the CDC, 2,712,630 people died in 2015 (Underlying Cause of Death data). We can use this information to determine the mortality contribution for vaccine and animal associated deaths in 2015. I. To do this, take the number of deaths attributed to that cause (found earlier) divided by the

total number of deaths, and multiply by 100 to convert the value to a percentage.

Disease Mortality Rate without

vaccination Mortality Rate

with vaccination

Measles 1-3% 0.1%

Mumps 1% 0.01%

https://wonder.cdc.gov/ucd-icd10.html https://wonder.cdc.gov/wonder/help/about-cdc-wonder-508.pdf https://wonder.cdc.gov/vaers.html Lab 1: Follow the instructions and complete the assignment below. Submit your answers through the Lab 1 Assignment on Blackboard.

Tetanus 50% 0.132%

Whooping Cough 3.7% 0.04%

Here you are provided with the mortality rates of some vaccine preventable illnesses (CDC.gov). J. Carefully consider these data. In your interpretation, ask yourself which is the greater

statistical risk to you: the vaccination or contracting the disease Echo Chamber: Another danger sign of fake news websites is that their primary content tends

to focus on a very specific group of people. This reflects an “echo chamber” or “preaching to

the choir” situation, where they only write articles that will affirm the beliefs of their core

audience. When evaluating a resource for the echo chamber phenomenon, pay attention to the

advertisements and other articles published by the site to see if they appear to within a narrow

mindset or worldview.

K. Evaluate both of the original article websites (Articles 1 and 2) for the echo chamber effect. Remember to pay attention to the advertisements, and take a look at a collection of other articles published on the site as well.

Consequences: Publishing and sharing fake news has real consequences. The CDC’s goal for vaccination rates for children is at least 90%. Take a look at the CDC’s data on vaccination rates here to evaluate current vaccination rates: http://www.cdc.gov/nchs/fastats/immunize.htm L. Notice which vaccines are and aren’t meeting this goal. 4. When you are finished, answer the following questions. 1. Discuss your overall evaluation of both sources (Article 1 and Article 2), focusing on the

general websites not the articles. Were you able to identify any echo chamber evidence or clickbait headlines with either? Describe what you found for each, including specific evidence.

2. Describe the credentials (degrees, awards, experience) for the authors of both articles that

we evaluated. Which author do you believe has more credibility? Explain your answer, using their credentials as evidence.

3. Describe each claim that you found within each article (Article 1 and Article 2) that didn’t

have a citation that you thought should have (quote the articles). Explain why you think each claim doesn’t have a citation, and why you think it needs one. If you weren’t able to find a claim that wasn’t cited, state this.

4. Based on your first Vaccine Adverse Events Reporting System search, how many VAERS reported deaths were there in 2015? Compare this to Claim 1, the Article 1 statement that

http://www.cdc.gov/nchs/fastats/immunize.htm Lab 1: Follow the instructions and complete the assignment below. Submit your answers through the Lab 1 Assignment on Blackboard.

the VAERS source attributes 5,000 deaths to vaccines annually; is this accurate? Explain your answer.

5. As you may have noticed during your investigation, the VAERS website contains a lot of

information, including background, disclaimers, and caveats to the database. One such example reads “A report to VAERS does not mean that the vaccine caused the adverse event, only that the adverse event occurred sometime after vaccination.” What does this mean? How does this contribute to or change your approach to interpreting VAERS data?

6. Based on your research on VAERS database underreporting, is underreporting a problem

with VAERS? Given the VAERS underreporting disclaimer that you summarized during your

investigation, how do you respond to the Article 1 (vaccines.news article) Claim 2, that

~90% of vaccine associated deaths aren’t even reported?

7. Check the math. Consider Article 1 (vaccines.news) Claims 1 and 2 in terms of numbers. For

the sake of analysis, let’s assume that 5,000 vaccine associated deaths stated in Claim 1

really does represent only 10% of the actual (reported and unreported) vaccine associated

deaths, as stated in Claim 2. If this true, then does the second part of Claim 2, that the

actual death vaccine associated death rate “swells to about 39,000”, make sense,

mathematically? Is it mathematically possible for the numbers stated in Claims 1 and 2 to

be true? Explain your answer, including your logic and calculations.

8. How many animal related deaths were recorded in 2015, as presented by the CDC Wonder

Cause of Death database? Compare this number to the VAERS reported vaccine deaths (that

you found in your first VAERS search); what is your response? How does this information

impact your interpretation regarding the validity of vaccine.news article Claim 3? Explain

your answer, including your logic and calculations.

9. How many vaccine-attributed deaths are on record within the CDC Wonder database from 2015 (recall, you might have needed to click for “More Information” to learn more after searching for this result)? What does this tell you about the number of data recorded vaccine-attributed deaths in the US in 2015?

10. Consider each search data site that we have used in this investigation; review your

comparison in of the CDC Wonder and VAERS data verifiability. Discuss your interpretation

of the credibility of the data presented on these sites. Which data are more credible?

Provide at least 2 reasons to support your conclusion.

11. Calculate and compare mortality rates. Based on the total number of deaths in 2015 and the VAERS reported number of vaccine attributed deaths for 2015 (for calculations sake) what is the mortality rate % associated with Vaccines from 2015? Based on the total number of deaths in 2015 and the CDC Wonder database number of animal associated

deaths for 2015, what is the mortality rate % associated with animals from 2015? Do these number make sense; are you confident in the validity of these figures, why/why not? Additionally, does this calculation alter your previous conclusions regarding the validity of the Article 1 (vaccine.news article) Claim 3? Explain your answer, including your logic and calculations.

12. Carefully consider your calculated vaccine induced mortality rate (determined based on the

VAERS reported VAERS reported number of vaccine attributed deaths for 2015 and the total number of deaths in 2015). Compare this number to the mortality rate presented in the data table for those without vaccinations for measles, mumps, tetanus, and/or whooping cough. Now, compare these numbers with the mortality rate for those that have been vaccinated for these diseases. Based on these data, which of the following poses the greatest statistical risk (a higher mortality rate): dying a vaccine-caused death in 2015 (assuming the VAERS report data is accurate), dying from measles, mumps, tetanus, and/or whooping cough without being vaccinated, or dying from measles, mumps, tetanus, and/or whooping cough after being vaccinated? Which of these poses the least statistical risk? Explain your answer, including your logic and calculations.

13. In the background information provided with this lab, we learned that the CDC’s goal for

vaccination rates for children is at least 90%. Based on their current vaccine rates, what vaccines are not meeting this goal? Given what you’ve read and researched in this activity, what are some possible reasons why the vaccination rates are below this threshold?

14. If one of your friends shared the Article 1 (vaccines.news news article) on social media, how

would you respond? Write a respectful reply below, pointing out some of the issues in this article that we discovered today.

Now that you completed this lab, return to Blackboard and select Lab 1 Assignment. You will see that Blackboard randomly selected 5 of the 14 questions above for you to answer, from the pool of all 14 questions. Copy-paste your answers for the 5 questions that Blackboard selected into Blackboard. Note that as this is a random selection, sometimes the questions will all be next to each other, sometimes they will be the hard ones, sometimes the easy ones, sometimes a true mix, and each student gets a slightly different selection. Each question is worth 2 points, for a total of 10 points for the lab.

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6 tips to help you detect fake science news

I’m a professor of chemistry, have a PhD and conduct my own scientific research , yet when consuming media, even I frequently need to ask myself: “Is this science or is it fiction?”

There are plenty of reasons a science story might not be sound. Quacks and charlatans take advantage of the complexity of science, some content providers cannot tell bad science from good and some politicians peddle fake science to support their positions.

If the science sounds too good to be true or too wacky to be real, or conveniently supports a contentious cause, then you might want to check its veracity.

Here are six tips to help you detect fake science.

Tip 1: Seek the peer review seal of approval. Scientists rely on journal papers to share their scientific results. They let the world see what research has been done, and how.

Once researchers are confident of their results, they write up a manuscript and send it to a journal. Editors forward the submitted manuscripts to at least two external referees who have expertise in the topic. These reviewers can suggest the manuscript be rejected, published as is or sent back to the scientists for more experiments. That process is called “peer review.”

Research published in peer-reviewed journals has undergone rigorous quality control by experts. Each year, about 2,800 peer-reviewed journals publish roughly 1.8 million scientific papers. The body of scientific knowledge is constantly evolving and updating, but you can trust that the science these journals describe is sound. Retraction policies help correct the record if mistakes are discovered post-publication.

Peer review takes months. To get the word out faster, scientists sometimes post research papers on what’s called a preprint server. These often have “RXiv” — pronounced “archive” — in their name: MedRXiv, BioRXiv and so on. These articles have not been peer-reviewed and so are not validated by other scientists . Preprints provide an opportunity for other scientists to evaluate and use the research as building blocks in their own work sooner.

How long has this work been on the preprint server? If it has been months and it hasn’t yet been published in the peer-reviewed literature, be skeptical. Are the scientists who submitted the preprint from a reputable institution? During the coronavirus crisis, with researchers scrambling to understand a dangerous new virus and rushing to develop lifesaving treatments, preprint servers have been littered with immature and unproven science. Fastidious research standards have been sacrificed for speed .

A last warning: Be on the alert for research published in what are called predatory journals . They don’t peer-review manuscripts, and they charge authors a fee to publish. Papers from any of the thousands of known predatory journals should be treated with strong skepticism.

Tip 2: Look for your own blind spots . Beware of biases in your own thinking that might predispose you to fall for a particular piece of fake science news.

People give their own memories and experiences more credence than they deserve, making it hard to accept new ideas and theories. Psychologists call this quirk the availability bias. It’s a useful built-in shortcut when you need to make quick decisions and don’t have time to critically analyze lots of data, but it messes with your fact-checking skills.

In the fight for attention, sensational statements beat out unexciting, but more probable, facts. The tendency to overestimate the likelihood of vivid occurrences is called the salience bias. It leads people to mistakenly believe overhyped findings and trust confident politicians in place of cautious scientists.

A confirmation bias can be at work, as well. People tend to give credence to news that fits their existing beliefs. This tendency helps climate change denialists and anti-vaccine advocates believe in their causes despite the scientific consensus against them.

Purveyors of fake news know the weaknesses of human minds and try to take advantage of these natural biases. Training can help you recognize and overcome your own cognitive biases .

Tip 3: Correlation is not causation. Just because you can see a relationship between two things doesn’t necessarily mean that one causes the other.

Even if surveys find that people who live longer drink more red wine, it doesn’t mean a daily glug will extend your life span. It could just be that red-wine drinkers are wealthier and have better health care, for instance. Look out for this error in nutrition news.

Tip 4: Who were the study’s subjects? If a study used human subjects, check to see whether it was placebo-controlled. That means some participants are randomly assigned to get the treatment — like a new vaccine — and others get a fake version that they believe is real, the placebo. That way, researchers can tell whether any effect they see is from the drug being tested.

The best trials are also double blind: To remove any bias or preconceived ideas, neither the researchers nor the volunteers know who is getting the active medication or the placebo.

The size of the trial is important, too. When more patients are enrolled, researchers can identify safety issues and beneficial effects sooner, and any differences between subgroups are more obvious. Clinical trials can have thousands of subjects, but some scientific studies involving people are much smaller; they should address how they’ve achieved the statistical confidence they claim to have.

Check that any health research was actually done on people. Just because a certain drug works in rats or mice does not mean it will work for you.

Tip 5: Science doesn’t need “sides.” Although a political debate requires two opposing sides, a scientific consensus does not. When the media interpret objectivity to mean equal time, it undermines science.

Tip 6: Clear, honest reporting might not be the goal. To get their audience’s attention, morning shows and talk shows need something exciting and new; accuracy may be less of a priority. Many science journalists are doing their best to accurately cover new research and discoveries, but plenty of science media are better classified as entertaining rather than educational. Dr. Oz, Dr. Phil and Dr. Drew should not be your go-to medical sources.

Beware of medical products and procedures that sound too good to be true. Be skeptical of testimonials. Think about the key players’ motivations and who stands to make a buck.

If you’re still suspicious of something in the media, make sure the news being reported reflects what the research actually found by reading the journal article itself .

Marc Zimmer is a professor of chemistry at Connecticut College. This article was originally published on theconversation.com .

On social media, vaccine misinformation mixes with extreme faith

False claims tying coronavirus vaccines to infertility drive doubts among women of childbearing age

John Oliver explains why so much ‘science’ you read about is bogus

Fight Fake News

Detect Fake News

Lsu manship school's resource guide to fake news.

This website is a resource for students, academics and citizens to defend themselves against fake news and misinformation.

Read the News

Learning the IMVAIN method, distinguishing between factual news vs. opinion and learning about journalistic history are the first steps for critically reading the news.

As technology advances though deepfakes, a human audio-visual manipulation that is based on artificial intelligence, will become more difficult to detect and that they will be used to interfere with elections.

Experts Focus on Vaccine Misinfo

A panel of experts was questioned by student media journalists. Guests included Prof. Rupali Limaye of Johns Hopkins University on how to talk to parents; Dr. Joseph Kanter, M.D., Louisiana's chief medical officer, Michael Spikes, a Northwestern University media literacy expert, Dr. Hollie Hale-Donze on vaccines and Blake Peterson a news reporter from The Advocate. Learn more and watch the recording of this panel.

Vaccine Misinfo

Is the COVID-19 vaccine safe? Will it change my DNA? Who can we trust for information on COVID-19 and the vaccine? Learn the facts behind common vaccination myths .

 Test Your Media Literacy Skills!

Can you go 6 for 6?

  Which social media platform currently is trying to maintain a hardline "free speech" approach regarding political content?

a. Twitter b. Facebook c. YouTube d. Myspace

b. Facebook

"Zuckerberg has defended Facebook’s commitment to free expression , even as competitors take steps to address hate and disinformation on their platforms."

 Which governmental entities have passed (or are considering) media literacy education? Select all that apply.

a. The United Kingdom b. Louisiana c. California d. Washington state

a, The UK, c. California & d. Washington

 In 2019, The United Kingdom’s cabinet secretaries announced that they would provide guidance for  teaching media literacy to their students. California passed a law which requires media literacy resources to be available to students. Washington state is considering a media literacy bill, and Massachusetts successfully passed a bill so students can better “access, analyze and evaluate all types of media,” according to Media Literacy Now .

Which platforms said they would take down coronavirus misinformation that is dangerous to public health?

a. Facebook b. Twitter c. YouTube d. All of the Above

d. All of the Above

The public health risks associated with virus misinformation were so high that Google, Twitter and Facebook all announced that they would remove dangerous content about COVID-19, a move that showed their willingness to moderate at least some political content.

 What percentage of Americans believe that social media does more to spread lies and falsehoods?

a. 18% b. 27% c. 55% d. 93%

  c. 55%

A 2019 NBC News/Wall Street Journal poll found that 55 percent of Americans believed social media does more to spread lies and falsehoods versus only 31 percent who said it does more to spread news and information. 

  There is evidence that bot accounts account for over half of the Twitter conversation on some political topics?

Wired reported that bot accounts can account for over-half of the conversation around specific political topics on Twitter, raising questions about the notion that Twitter dialogue reflects public opinion.  

Are social media companies technology companies or media companies?

a. Social Media companies b. Media Companies c. It’s Confusing!

c. It's Confusing!

Social media companies want to remain immune from legal consequences for user-generated content. They do not want to be treated like a newspaper. Section 230 of The 1996 Communications Decency Act shields third-party platforms (Facebook, Twitter and Instagram) from liability for user-generated content. Platforms, intent to keep the law unchanged, now face pressure from both political parties that want to change Section 230. 

CJR: Political Misinformation, and a Matter of Scale

In an article by the Columbia Journalism Review, Matthew Ingram discusses recent instances of misinformation and the significance of that disinformation. The Disinformation Issue of CJR  titled True Lies , also contains numerous articles that provide in-depth insight into fake news, deepfakes and disinformation.

Are You Getting Faked Out?

Take the News Literacy Project's quiz on misinformation in the time of COVID-19, and see if you can tell fact from fiction.

Mis- and Disinformation

Fake news, made-up stories and just pure fiction masquerading as news has a long history in American politics. 

About Fake News

Follow our Twitter to stay up to date on the latest in fake news!

How to spot a counterfeit COVID-19 vaccine

• Search Search

To the naked eye, there’s no discernible difference between the potentially life-saving liquid inside a COVID-19 vaccine vial and any other clear liquid. 

In fact, not even trained personnel tasked with administering the vaccine will be able to test on the spot if the liquid contains the authentic mRNA vaccine. The technology needed to make these observations in the field simply doesn’t exist, says Nikos Passas , professor of criminology and criminal justice at Northeastern.

As public health officials plan for widespread distribution of the long-awaited vaccines, ensuring that vaccines are authentic could emerge as an important issue. If the first round of doses are scarce—and desperation for a quick COVID-19 solution is strong—counterfeiters could attempt to capitalize on the opportunity, Passas says.

“The problem we have is a clear mismatch in supply and demand,” he says. “Whenever you have this kind of asymmetry, you can expect illicit markets to pop up.” 

To fight the trade of counterfeit vaccines and other medicines and supplies, Passas and a team of researchers are analyzing global trade networks and collaborating with pharmaceutical companies, banks, and law enforcement worldwide on how to ensure quality control and stop the illicit flow of potentially ineffective—or even fatal—fakes.

Left: Mansoor Amiji, university distinguished professor of pharmaceutical sciences and chemical engineering , and Nikos Passas, professor of criminology and criminal justice are members of a team tasked with stopping illicit flows of medicine and medical supplies. Photos by Matthew Modoono/Northeastern University

Tracking transactions will be a main strategy the team employs to disrupt chains of counterfeit products. 

“We want to follow the money and other trails,” says Passas, who is joined by Northeastern professors Mansoor Amiji and Ravi Sundaram , Boston University professor Muhammad Zaman, and University of Houston professor Ioannis Kakadiaris on the National Science Foundation-funded task force. 

Once COVID-19 vaccines are approved in the United States, which is expected to happen later this month, the U.S. military will oversee the vaccine’s supply chain and facilitate shipping across the country. 

“Where the military is involved, there’s an assurance that the vaccines will be authentic,” says Amiji, university distinguished professor of pharmaceutical sciences and chemical engineering. “But once the product gets beyond that scale, once the military becomes less involved, the concern for counterfeits becomes more pronounced.” 

As of right now, the U.S. military is only expected to help distribute vaccines in the early phases. Afterwards, healthcare providers, pharmaceutical companies, and shipping companies will have a greater role in overseeing distribution and administration of the vaccines. 

A less coordinated effort could expose breaches in the supply chain for counterfeit peddlers to capitalize on, Amiji explains. “People are going to be looking for cheaper alternatives.”

The Department of Homeland Security, which is playing a large role in overseeing quality control, published a list of guidelines to help consumers avoid fake vaccines: 

• Always consult a licensed medical professional to obtain a COVID-19 vaccine or treatment.
• Make sure your doctor has been approved to administer the vaccine.
• Do not buy COVID-19 vaccines or treatments over the internet.
• Do not buy COVID-19 vaccines or treatments through an online pharmacy.
• Ignore unsolicited offers for vaccinations and miracle treatments or cures.
• Don’t respond to text messages, emails or calls about vaccines and treatments.
• Be wary of ads for vaccines and treatments on social media.
• Any suspicious activity can be reported to: [email protected] .

Dangers of unauthorized vaccines

In the worst case scenario, a counterfeit vaccine could directly harm a person. For example, the water used in the fake vaccine liquid could be contaminated with something unsafe for humans, Amiji says. 

But even if the counterfeit vaccine doesn’t cause direct complications, it still won’t prevent COVID-19, and that could provide people with a false sense of security, Amiji says. People might engage in unsafe behaviors under the assumption that they’re protected from COVID-19. 

As a COVID-19 vaccine approaches, researchers are taking aim at the counterfeit drug market

In addition to counterfeit products, the task force also investigates stolen and smuggled authentic goods. In these cases, the vaccines are real, but if they travel through illicit or unsupervised channels to reach consumers, the quality of the vaccine could be compromised, Amiji says. 

The mRNA vaccines in development in the U.S. require complicated storage in extremely cold freezers, making it difficult for unauthorized organizations to ship or store the vaccine properly since they might not have access to the necessary technology, Amiji explains. 

The degradation process—changes in the chemical makeup of the vaccine that make it less potent or different entirely—is one of the main concerns for the mRNA vaccines, especially in substandard supply chains that don’t have access to freezers or dry ice, Amiji says. In the worst case scenario, degraded vaccines could be dangerous for humans. 

“This raises some serious concerns about toxicity,” he says. “These injections could contain impurities that we don’t know about.” 

Pfizer has created a distribution container that keeps the vaccine refrigerated at the proper temperature for up to 10 days if unopened. “But what about the 11th day? In that case, it could be the real product, but it’s used past the expiration date,” Amiji says. 

Detection of counterfeit vaccines could also dissuade already skeptical people from choosing to get vaccinated. 

“The public’s confidence is at risk,” Passas says. “Combine that with the shaken confidence we already have about vaccines and that creates another public health problem. We won’t be able to create the community level immunity needed to protect people if they don’t opt in.”

For media inquiries , please contact Jessica Hair at [email protected] or 617-373-5718.

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COVID-19 Fake News Detection Using Ensemble-Based Deep Learning Model

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COVID-19 Vaccine Misinformation and the Anti-Vaccine Movement

By Dorit Rubinstein Reiss

The anti-vaccine movement is aggressively working to promote misinformation about COVID-19 vaccines, up to and including promoting fake claims of deaths from vaccines. We need to be aware of its efforts, and be prepared to respond.

It’s worth emphasizing that this blog post is focused on the anti-vaccine movement, not people with concerns about vaccines (the “vaccine hesitant”).

In relation to COVID-19, anti-vaccine activists have aggressively promoted misinformation from the start of the pandemic.

In March 2020, anti-vaccine activists incorrectly alleged – by misrepresenting a study – that flu vaccines increase COVID-19 risks . In June, anti-vaccine activist Del Bigtree described COVID-19 as a “cold,” blamed those who died for their own deaths, and called on his followers to “catch that cold.”

And from the beginning, anti-vaccine activists were committed to the ideas that COVID-19 vaccines would not work , would be dangerous, and would be promoted by a nefarious global conspiracy . They continue to spread these allegations, for example, using the fact that there are liability protections for COVID-19 vaccines to imply the vaccines are dangerous. Liability protections for COVID-19 vaccine manufacturers are real ; but they are not evidence that the vaccines are unsafe.

This post will focus on one type of misinformation: alleged deaths from COVID-19 vaccines.

In a prescient article from December 4, 2020 – before either Pfizer/BioNTech or Moderna’s vaccine was authorized by the U.S. Food and Drug Administration for emergency use – Dr. Derek Lowe warned us about coming “ false side effects ” from COVID-19 vaccines. He reminded us that:

“…if you take 10 million people and  just wave your hand back and forth over their upper arms , in the next two months you would expect to see about 4,000 heart attacks. About 4,000 strokes. Over 9,000 new diagnoses of cancer. And about 14,000 of that ten million will die, out of usual all-causes mortality. No one would notice. That’s how many people die and get sick anyway. But if you took those ten million people and gave them a new vaccine instead, there’s a real danger that those heart attacks, cancer diagnoses, and deaths will be attributed to the vaccine.”

It is inevitable that, with millions of people vaccinated, some adverse events will happen subsequently, by coincidence alone. And it is inevitable that the anti-vaccine movement will take advantage of that to create fear and doubt. But they have done more.

The Anti-Vaccine Movement Spreads Fake Death Stories

In at least four cases that I know of, the anti-vaccine movement has made baseless claims that people have died from COVID-19 vaccines.

The most extreme example is nurse Tiffany Dover, who fainted for a moment after her vaccine – and immediately revived . Anti-vaccine activists claimed that she died, and the claim is still being spread on Facebook, along with her personal photos, a fake obituary, and attacks on her and her family. There is even a Facebook group seeking “Justice for Tiffany Dover” (who is not dead).

In another case, an unnamed screenshot alleged that the speaker’s aunt, a nurse in Alabama, died. But the Alabama health department checked and found no such death .

Another young woman had to go online and make a video alerting viewers to the fact that she is not, in fact, dead. The fourth claim, too, involved an unnamed screenshot of someone’s mother-in-law. Maybe the mother-in-law died, but there is no evidence to indicate it. Nonetheless, the unnamed screenshot is shared as fact on anti-vaccine sites.

The Anti-Vaccine Movement Claims Deaths Unrelated – or Potentially Unrelated – to Vaccines

In addition to blaming the vaccine for deaths that did not, as far as we can tell, happen, anti-vaccine activists are also attributing deaths caused by COVID-19 to the vaccine.

The first example I saw of this was the tragic tale of nurse Jennifer Lee McClung, who died from COVID-19 complications at the age of 54 . Anti-vaccine activists shared her story, alleging that she died from the vaccine – although her death occurred before she could reasonably get vaccinated.

On a larger scale, anti-vaccine activists tried to blame deaths of nursing home residents, also from COVID-19, on vaccines – although the fatal outbreak began before vaccines were given, and the victims were unvaccinated.

Other stories are less clear, but lack of clarity does not prevent anti-vaccine activists from seizing on them. For example, a report on the Norwegian Medicines Agency’s investigation of 23 deaths after the vaccine – with a cautious recommendation to consider not vaccinating the most frail patients, for whom even mild symptoms can trigger issues – became, on anti-vaccine sites, a claim that these 23 deaths were caused by the vaccines, which is not what the report from Norway claimed .

We can expect anti-vaccine activists to continue to present deaths after vaccines – or even, as the first two examples show, deaths without a vaccine – as vaccine-related on little or no evidence, or against the evidence. These claims will be spread by their believers online.

What Can We Do?

As these examples show, anti-vaccine organizations and activists do not hesitate to promote and spread claims about COVID-19 that are, at best, unproven, and, at worst, flatly untrue. What can we do?

First, it is important to raise awareness of the fact that such claims exist and are spread, and to encourage everyone to be cautious in evaluating claims of vaccine harms.

Some sources are simply unreliable. For example, the anti-vaccine organizations Children’s Health Defense, led by activist Robert F. Kennedy Jr., has a long history of spreading misinformation (most recently through its new publication, The Defender). So does the anti-vaccine organization Informed Consent Action Network, led by activist Del Bigtree. Any content shared from these sources should be approached with caution.

Take caution when encountering anonymous claims, unverified claims, and conspiracy-based claims apply here. Even testimony from named individuals may be false or misleading. In a viral video, a woman who alleged she was nurse Khalilah Mitchell from Tennessee claimed she received the COVID-19 vaccine and it gave her Bell’s Palsy. But there is no such nurse anywhere in the United States, and the only woman in Tennessee named Khalilah Mitchell works at a bakery , and was not, at the time, in any of the priority groups receiving the vaccine.

At the other side of the spectrum are traditionally reliable sources. The World Health Organization has a list of sites on vaccines from around the world that have been evaluated and shown to meet “ good information practices criteria .”

Second, fast debunking or prebunking of anti-vaccine misinformation will continue to be important. Mainstream media increased their efforts there, which helps, but much of this is still done by the people who have been debunking anti-vaccine misinformation for over a decade.

Third, promoting accurate information about vaccine risks – and benefits – as they appear will continue to be important, too. If reliable sources directly address issues that do come up – like allergic reactions – the claim of harms being hidden will be less plausible.

Fourth, the efforts of social media to reduce the spread of misinformation are helpful, albeit imperfect. Social media uses blunt instruments, and is often slow to respond.

Combatting anti-vaccine misinformation is a constant effort, and will need to stay front and center as a priority. Anti-vaccine misinformation is not just spurious and wrong; it also may contribute to vaccine hesitancy among the broader public, which in turn will hinder national and global public health efforts.

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Dorit Reiss

Dorit Rubinstein Reiss is a professor of law at the University of California, Hastings College of the Law. Increasingly, her research and activities are focused on legal issues related to vaccines, including exemption laws and tort liability related to non-vaccination. She published law review and peer reviewed articles and many blog posts on legal issues related to vaccines. She received an undergraduate degree in Law and Political Science (1999, Magna cum Laude) from the Faculty of Law in the Hebrew University of Jerusalem. She received her Ph.D. from the Jurisprudence and Social Policy program in UC Berkeley. She is a member of the Parents Advisory Board of Voices for Vaccines, and active in vaccine advocacy in other ways. She is also a Member of the Vaccine Working Group on Ethics and Policy (http://vaccineworkinggroupethics.org/).

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• Copy URL https://www.pbs.org/newshour/education/fake-covid-19-vaccination-cards-worry-college-officials

Fake COVID-19 vaccination cards worry college officials

SAN LUIS OBISPO, Calif. (AP) — As the delta variant of the coronavirus sweeps across the United States, a growing number of colleges and universities are requiring proof of COVID-19 vaccination for students to attend in-person classes. But the mandatory requirement has opened the door for those opposed to getting the vaccine to cheat the system, according to interviews with students, education and law enforcement officials.

Both faculty and students at dozens of schools interviewed by The Associated Press say they are concerned about how easy it is to get fake vaccine cards.

Across the internet, a cottage industry has sprung up to accommodate people who say they won’t get vaccinated for either personal or religious reasons.

An Instagram account with the username “vaccinationcards” sells laminated COVID-19 vaccination cards for $25 each.

A user on the encrypted messaging app, Telegram, offers “COVID-19 Vaccine Cards Certificates,” for as much as $200 apiece. “This is our own way of saving as many people as we possibly can from the poisonous vaccine,” reads the seller’s message, viewed by at least 11,000 app users.

An increasing number of inquiries to these sites and similar ones appear to be from those who are trying to get fake vaccination cards for college.

A Reddit user commented on a thread about falsifying COVID-19 vaccination cards, saying, in part, “I need one, too, for college. I refuse to be a guinea pig.”

On Twitter, one user with more than 70,000 followers tweeted, “My daughter bought 2 fake ID’s online for $50 while in college. Shipped from China. Anyone have the link for vaccine cards?”

According to a tally by The Chronicle of Higher Education, at least 664 colleges and universities now require proof of COVID-19 inoculations. The process to confirm vaccination at many schools can be as simple as uploading a picture of the vaccine card to the student’s portal.

WATCH: What Americans should know about the delta variant, masks — regardless of vaccine status

In Nashville, Vanderbilt University places a hold on a student’s course registration until their vaccine record has been verified unless they have an approved medical accommodation or religious exemption.

The University of Michigan says it has a system in place to confirm employee and student vaccinations. A spokesman for the college told the AP the school has not encountered any problems so far with students forging their COVID-19 vaccination record cards.

But Benjamin Mason Meier, a global health policy professor at the University of North Carolina at Chapel Hill, questions how institutions can verify those records.

“The United States, unlike most countries which have electronic systems in place, is basing its vaccination on a flimsy paper card,” he said.

Meier tweeted last week that he spoke with several students who were worried about the accessibility of fraudulent vaccine cards and that they knew a fellow student who had submitted one to the university.

“There needs to be policies in place for accountability to make sure that every student is operating in the collective interest of the entire campus,” he said.

In a statement to the AP, UNC said the institution conducts periodic verification of documents and that lying about vaccination status or falsifying documents is a violation of the university’s COVID-19 community standards and may result in disciplinary action.

“It’s important to note that UNC-Chapel Hill has not found any instances of a student uploading a fake vaccine card. Those claims are simply hearsay at this point,” the school said.

But other university staff and faculty have expressed their concern over the alleged forgery of vaccine cards. Rebecca Williams, a research associate at UNC’s Lineberger Comprehensive Cancer Center and Center for Health Promotion and Disease Prevention, said while she is concerned by these claims, she isn’t surprised.

“This is why I think the development of a reliable national digital vaccine passport app is very important for the sake of all the organizations and businesses that want to require proof of vaccination for employees, students, or business patrons,” Williams said.

The AP spoke with several students across the country who did not want to be identified but said they were also aware of attempts to obtain fake cards.

Some school officials do acknowledge that it’s impossible to have a foolproof system.

“As with anything that potentially requires a certification, there is the possibility for an individual to falsify documentation,” said Michael Uhlenkamp, a spokesman for the chancellor’s office at California State University. The school system, which is the largest in the nation, oversees about 486,000 students each year on 23 campuses.

Dr. Sarah Van Orman, the chief health officer at the University of Southern California and COVID-19 task force member for the American College Health Association, said college campuses are especially challenging environments to control the spread of COVID-19 since tens of thousands of students move into campus from all over the world. But if students falsify their vaccination status, she said it may have limited impact.

“I think that the numbers of students who would do that would be so very small that it wouldn’t affect our kind of ability to get good community immunity,” Orman said.

In March, the concern over fake COVID-19 vaccination cards prompted the FBI to issue a joint statement with the U.S. Department of Health and Human Services urging people not to buy, create or sell fabricated vaccine cards.

The unauthorized use of the seal of an official government agency such as HHS or the Centers for Disease Control and Prevention is a federal crime that carries a possible fine and a maximum of five years in prison.

In April, a bipartisan coalition of 47 state attorneys general sent a letter to the CEOs of Twitter, Shopify and eBay to take down ads or links selling the bogus cards.

Many of the sites have blacklisted keywords related to fake cards, but places to buy the documents are still popping up on messaging apps, chat forums and the dark web.

Sellers on websites such as Counterfeit Center, Jimmy Black Market, and Buy Express Documents list COVID-19 vaccine cards, certificates and passports for sale, some costing €400 Euros or about $473.49 US dollars.

An advertisement on the website Buy Real Fake Passport reads vendors can produce fake vaccination cards by the thousands, if not tens of thousands, based on the demand.

“It is hiding under our noses. If you want it, you can find it out,” said Saoud Khalifah, founder and CEO of scam-detecting software Fakespot. “If we are seeing signs where things like Lollapalooza and other festivals are getting fake cards to gain entrance, the trend is just going to continue into these universities.”

In July, the U.S. Department of Justice announced its first federal criminal fraud prosecution involving a fake COVID-19 immunization and vaccination card scheme. Juli A. Mazi, 41, a naturopathic physician in Napa, California, was arrested and charged with one count of wire fraud and one count of false statements related to health care matters.

Court documents allege she sold fake vaccination cards to customers that appeared to show that they had received Moderna vaccines. In some cases, the documents show Mazi herself filled out the cards, writing her own name, and purported Moderna “lot numbers” for a vaccine she had not in fact administered. For other customers, she provided blank CDC COVID-19 vaccination record cards and told each customer to write that she had administered a Moderna vaccine with a specified lot number.

Requiring vaccinations to attend class at colleges and universities has become a contentious political issue in some states. Public colleges in at least 13 states including Ohio, Utah, Tennessee and Florida cannot legally require COVID-19 vaccinations due to state legislation, but private institutions in those same states can.

Among the states introducing and passing bills barring educational institutions from mandating COVID-19 vaccines, infringement on individual rights or liberties is often cited as the main concern.

But according to a statement released by the American College Health Association and other educational organizations, these restrictions impede on universities’ abilities to operate fully and safely.

“The science of good public health has gotten lost in some of the decisions that have been made in some places,” Orman said. “It has not always been held up by our political leaders.”

Some college students have taken to social media platforms like Twitter and TikTok to voice their outrage over other students possessing fraudulent vaccine cards.

Maliha Reza, an electrical engineering student at Pennsylvania State University, said it is mind-boggling that students would pay for fake vaccination cards when they could get the COVID-19 vaccine at no cost.

“I’m angry about that like there is more anger than I could describe right now,” Reza said. “It’s dumb considering the vaccine is free and it is accessible across the country.”

Roselyn Romero is an intern on the Associated Press Global Investigative team. The internship is funded by the Ida B. Wells Society for Investigative Reporting.

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Fake News Assignment Death by Vaccines

With the growth of social media, fake news websites are appearing with greater frequency. This has begun the rapid spread of misinformation on topics regarding vaccines, food safety, global warming, and many other topics. Students need to be able to evaluate these news sites and soruces.

This assignment will present students with three articles about vaccines: one from the Washington Post, one from Natural News, and the other from a website called "Vaccines.news". Students will be guided through an analysis of the articles, including identifying clickbait, researching the background of the authors, and judging the validity of several statements within the articles.

Essential concepts: Pseudoscience, fake news, news analysis, sources.

Answer Key: Available as part of a environmental science instructor resources subscription .

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Original research article, detecting covid-19-related fake news using feature extraction.

• 1 Air University, Islamabad, Pakistan
• 2 Canadian Institute for Cybersecurity, University of New Brunswick Fredericton, Fredericton, NB, Canada
• 3 School of Information Technology and Engineering, VIT University, Vellore, India
• 4 Division for Institutional Planning, Evaluation and Monitoring (DIPEM), University of Johannesburg, Johannesburg, South Africa
• 5 Department of Information Systems, Faculty of Management, Comenius University Bratislava, Bratislava, Slovakia

Since its emergence in December 2019, there have been numerous posts and news regarding the COVID-19 pandemic in social media, traditional print, and electronic media. These sources have information from both trusted and non-trusted medical sources. Furthermore, the news from these media are spread rapidly. Spreading a piece of deceptive information may lead to anxiety, unwanted exposure to medical remedies, tricks for digital marketing, and may lead to deadly factors. Therefore, a model for detecting fake news from the news pool is essential. In this work, the dataset which is a fusion of news related to COVID-19 that has been sourced from data from several social media and news sources is used for classification. In the first step, preprocessing is performed on the dataset to remove unwanted text, then tokenization is carried out to extract the tokens from the raw text data collected from various sources. Later, feature selection is performed to avoid the computational overhead incurred in processing all the features in the dataset. The linguistic and sentiment features are extracted for further processing. Finally, several state-of-the-art machine learning algorithms are trained to classify the COVID-19-related dataset. These algorithms are then evaluated using various metrics. The results show that the random forest classifier outperforms the other classifiers with an accuracy of 88.50%.

1. Introduction

Reports about the novel coronavirus' (COVID-19) origin in Wuhan city in Hubei province, China came into the limelight in December 2019. Since then, the virus has spread in several provinces in China, and gradually to the majority of countries across the globe. Millions of people have been affected globally by this virus leading to the deaths of hundreds of thousands of people across the globe especially in countries like Italy, Spain, the United States, India, Brazil, and Russia as of June 2020 ( 1 – 3 ). This led the World Health Organization to declare COVID-19 as a pandemic in March 2020 ( 4 , 5 ). It is estimated that on average, each person infected by COVID-19 infects around 2.5 persons if everyone goes on with their normal lives. Thus, the initial person infected may lead to 406 further infections per month. Hence, many nations have implemented lockdowns and social distancing to reduce the spread of COVID-19 ( 6 ).

Social media is flooded with millions of posts about COVID-19. Even though some of the information in social media is genuine and informative, most of the information spread in social media about COVID-19 is potentially rumor. Several doctored videos and photos about origin, spread of the virus, vaccines, and deaths caused by COVID-19 are being shared over social media. It is estimated that around 30–35% of the news, videos, and photos spread on social media platforms are fake. This fake news travels faster than the virus itself creating widespread panic ( 7 ).

According to the International Fact Checking Network's (IFCN) study between January and April 2020, the fake news spread on social media can be categorized as follows: content about symptoms, causes, and cures, government documents, spread of the virus, misrepresentation of videos and photos, comments of politicians, and conspiracies that blame particular groups, countries, or communities for the spread of the virus. The fake news spread on social media has led to economic crisis in some countries. For instance, in some countries people stopped consuming non-vegetarian food as fake news was spread that animals and birds could be infected with COVID-19 and consuming non-vegetarian food may spread the virus in people. This had a severe impact on the sales of non-vegetarian food in some countries affecting the livelihood of many people ( 8 ). Figure 1 depicts the application of machine learning (ML) algorithms ( 9 , 10 ) to detect fake news spread on social media.

Figure 1 . Application of machine learning algorithms for COVID fake news detection.

In view of the serious impacts of the spreading of fake news which affects the privacy and security ( 11 , 12 ) of users, the need of the hour is to design a successful mechanism to detect/predict fake news. In this work, an attempt is made to classify the fake news regarding COVID-19. The dataset used in this work is a fusion of fake and real news collected from several social media platforms and websites ( 13 ). This dataset is then subjected to preprocessing to remove some irrelevant text such as URL punctuations and noisy data, etc. Then, the resultant text is split into several small words using tokenization. Then, the most important features are extracted from these tokens. These features are then trained by state-of-the-art ML algorithms to classify whether the COVID-19-related news is fake or real.

The main contributions of the current work are as follows:

• Classification of a novel COVID-19 fake news dataset collected from several social media platforms.

• Generating 39 linguistic and sentiment ( 14 – 16 ) features from text.

• Extracting the most important features from the COVID dataset to improve the accuracy of the ML algorithms.

• An accuracy of 88.50% is achieved by the random forest classifier.

The rest of the paper is organized as follows: In section 2, related works on COVID-19 fake news are discussed. ML algorithms used in this work to classify the fake news dataset are discussed in section 3. The proposed architecture is presented in section 4. The experiment results are presented along with the discussion in section 5. Finally the paper is concluded in section 6.

2. Literature Survey

Artificial intelligence (AI) has made a significant contribution to different aspects of the COVID-19 outbreak. A survey on various applications of AI for fighting COVID was presented in Nguyen ( 17 ). Specifically, deep learning algorithms play a vital role in empowering AI in most of the applications with larger datasets with different types of data. Some of the applications are computer-generated for predicting infected people from a large gathering through a high-resolution image. The most prominent application is in text mining assisted with natural language processing (NLP) for extracting semantically related information from numerous texts in multilingual news data collected from various social networking platforms. The details obtained from these models such as susceptible infected recovered (SIR) models can be injected into deep learning models for the prediction of COVID-19 trends of transmission and can avoid future pandemic or epidemic diseases ( 18 ). Furthermore, a mapping study on various AI models with big data for detecting fake news on social media was carried out in Meneses Silva et al. ( 19 ). They suggested that the long short term memory model provides better results than other algorithms but incurs more processing power.

Although social media is the platform that spreads news faster with low cost than any other media among all age groups, it is progressively more dangerous in spreading fake news. Many research projects were conducted on detecting fake news from social media. A two-step model for detecting fake news from the real story in social media using artificially intelligent algorithms was designed ( 20 ). Three real datasets were considered for the study. The dataset with unstructured data was preprocessed to obtain meaningful data and was represented using vectors. In turn, the results were generated for 23 supervised learning algorithms and compared based on four evaluation metrics. On the other hand, a biostatistical analysis of the COVID-19 pandemic was carried out in Bandyopadhyay and Dutta ( 21 ) using the KNN classifier. They collected data based on certain information from the news topics in social media through multi-document summarization. This summarization method extracted the information based on the lexical information of the topic and social patterns. The KNN classifier predicted fake news with an accuracy of 80%.

Furthermore, the study in Groza ( 22 ) investigated the spread of deceptive information on social media through COVID Ontology. The reasoning in the natural language is converted into description logic for perceiving inconsistencies among different medical sources. The authors argue for an ontology-based approach for detecting fake news related to the coronavirus. The proposed model explores how well description logics (DLs) understand contradictions in figuring out whether they are true or false. The authors pay particular attention to translating the natural language into DL using the FRED translator, after which, justification is performed to test the performance of the prediction using the racer method. The proposed ontology model reveals misconceptions that circulate in digital news and shows accurate facts. However, potential research can be expanded by program evaluation and provides a verbal remedy for any conflict that exists.

Fake news or false information about this COVID-19 pandemic are very prominent and should be detected to avoid unwanted chaos. A FakeCovid dataset from a multilingual news article was generated, and a classifier was designed to detect the fake news from the dataset generated ( 23 ). BERT, the ML-based classifier model, performed better on the dataset generated with an F1 score of 0.76 at the initial stage of COVID 19. The model does not have a polyglot, so they annotated categories manually for three languages.

The study in Daley ( 24 ) used ML algorithms for the automatic classification of fake news on COVID-19. The features considered for evaluation included the count of motion words and relativity words, prepositions in the headlines of the news website, tone expressed, and word count. Also, any news media would concentrate on the increased count of these parameters to convince the customers on the fake news outlets. They attained 79% accuracy on 5-fold cross-validation using a decision tree classifier, which outperformed other algorithms in the scikit-learn API. An automated framework for detecting COVID-19 cases from X-ray images of the raw chest was proposed in Ozturk et al. ( 25 ). They used a deep neural network, namely the DarkCovidNet model (used for real-time object detection) with 17 convolution layers, each with different filters. The average age of the positive COVID patients in the dataset was 55. The model was used for binary (COVID or not) and multiclass classification (COVID or pneumonia or not) with an accuracy of 98 and 87%, respectively. The performance of the system was assessed with radiologists only, and the robustness of the system can be tested with large datasets.

Therefore, it is evident from the survey that very few attempts were carried out for detecting the fake news in COVID. Although various deep learning techniques were employed for detection, the datasets taken for analysis are very small. It is mandatory to have a strong model to detect false information on COVID-19 to avoid unwanted chaos in different aspects of COVID-19.

3. Proposed Architecture

In this section, the proposed work is discussed in detail.

The steps involved in the proposed work are shown in Figure 2 and are summarized as follows:

• The dataset used in this study is collected from various websites and social media cites.

• Data preprocessing is performed on the dataset by removing URLs, punctuation marks, and empty columns.

• After text preprocessing, tokenization is performed to convert the larger text into words or small lines.

• In the next step, extracting the features from text is performed.

• The features extracted are then passed on to the state-of-the-art ML learning algorithms to train the model.

• Various evaluation metrics are used to evaluate the performance of the ML algorithms.

Figure 2 . Proposed architecture.

The rest of this section discusses various steps involved in the proposed work.

3.1. Dataset Description

The dataset used in this work is a fusion of several fake and real news articles about COVID-19 which are collected across several platforms such as Facebook, Twitter, The New York Times, Harvard Health Publishing, WHO, etc. The dataset has 1,164 instances out of which 586 instances are true and the remaining 578 are fake news ( 26 ).

3.2. Preprocessing and Tokenization

The dataset considered for this work is clean. However, some unnecessary symbols which have an impact on the final classification of the news are to be removed from the dataset. To remove the unnecessary symbols, such as punctuation marks, URLs are removed from the dataset as part of preprocessing.

Tokenization is the process of splitting text into a set of tokens. The fake news dataset is tokenized to convert the long sentences into small words/tokens.

3.3. Feature Extraction

The major contribution of this work is the extraction of important features from the COVID-19 fake news dataset. Feature extraction plays a very important role in text processing as it reduces the dimension of feature space by considering only the important features ( 27 – 29 ). To extract the features, the named-entity recognition (NER) approach is used in our work. The NER is a popular approach for feature extraction that can classify unstructured text based on location, person names, quantities, etc. ( 30 ).

In this study, 39 features are created from the COVID-19-related fake news dataset. The extracted features are represented in Table 1 .

Table 1 . Features extracted from text.

3.4. Training of the Dataset Using ML Algorithms

In the next step, the extracted features are trained using several state-of-the-art ML algorithms. The ML algorithms used in this work are linear SVC, logistic regression, SGD classifier, random forest classifier, bagging classifier, AdaBoost classifier, decision tree classifier, and K-nearest neighbors classifier (KNN) ( 31 ). These algorithms are explained briefly in the rest of this section.

3.4.1. Random Forest Algorithm

The random forest classifier is one of the supervised ML algorithms used for classification and regression purposes ( 32 ). The random forest method constructs decision trees from datasets and creates a forest made of trees. Then it collects the prediction value from each decision tree and computes the optimal solution with the help of voting. It is used as an ensemble method and helps to avoid overfitting ( 33 ). The following steps are implemented in the random forest classifier:

1. Random data samples are selected from the available dataset.

2. A decision tree is constructed for each data sample selected, and a prediction value is obtained from each decision tree. The Gini coefficient method is applied for node splitting as follows:

where D is the sample dataset and P i is the probability of decision class that can appear in D

3. A voting method is applied on the prediction values obtained from each decision tee.

4. A final prediction result is chosen from the prediction value with more votes.

3.4.2. AdaBoost Classifier

The AdaBoost classifier ( 34 ) is an ensemble boosting classification method which combines several weak classifiers to build a strong classifier. The weak classifier performs the classification of one dimension of the input vector. The number of weak classifiers are increased during the training process to obtain more accuracy. The classifier applies minimum error to compute the weight of the newly added weak classifier. Then it updates the weights of each training dataset and sends the value to the recently added weak classifier. The selection of weak classifiers is very important for this algorithm. The result of the strong classifier S ( f ) is represented in the equation given below ( 35 ):

where f denotes the input feature vector, T denotes the number of input vectors, h t ( f ) denotes weak classifiers, and β t denotes the weights of weak classifiers.

3.4.3. Decision Tree Classifier

The decision tree classifier is one of the more popular ML algorithms applied for classification and prediction problems on supervised data. It classifies the trained dataset into trees and rules. It defines methods for the classification of categorical data characterized by their factors. It works better on classification problems where extensive data are involved, and is therefore used in many data mining applications. The first step in the decision tree classifier is to find the attribute to split the tree. Entropy measure is used to identify the best attribute as the root of the decision tree. The entropy measure is used to calculate the update in homogeneity that results from the split on every attribute. Entropy D of the dataset is defined as follows:

Where D is the data partition, n defines the number of decision classes, and p i defines the proportion of the records that comes under the decision class i. Information gain is used for this calculation. This is defined as the difference between the entropy of the dataset calculated before the split (D1) and the entropy calculated from the partitioned split (D2). The formula to calculate the information gain is given as follows:

The attribute with maximum information gain is selected to build the tree with a root node. A decision tree is constructed recursively using the obtained data partition until there are no attributes left. And decision rules can be defined from the constructed decision tree.

3.4.4. K-Nearest Neighbor Classifier

The K-nearest neighbor (KNN) ( 36 ) classifier is one of the popularly used supervised learning algorithms for classification and regression. It is a non-parametric and lazy algorithm in which the method is defined from the dataset. Also it does not require a training dataset for the development of the classification model. K is the number of nearest neighbors and it is the deciding factor. The classifier is based on the estimation of the nearest neighbor. It works on the calculation of a similarity value defined by the distance measure. The training step stores all the instances and their corresponding class labels. The test phase for a new instance 's' is calculated using the following steps:

1. Calculate the distance between the instance 's' in the test data and every instance in the training dataset 'd' using the Euclidean distance formula given as follows:

where n is the total number of instances in the dataset, x i represents instances in the training dataset, and y i are instances in the test dataset 'd'.

2. Arrange the distances in ascending order and select the first 'k' instances.

3. Find the most frequent decision class in the calculated 'k' nearest neighbors.

4. Experiment Results

The experiments on the COVID-19 fake news dataset are carried out in Google Colab, an open source cloud-based graphical processing unit (GPU)-based platform offered by Google Inc. The programming language used to implement the ML algorithms is Python 3.7.

A total of 70% of the COVID-19 fake news dataset is used for training the ML algorithms and the other 30% of the dataset is used for validating and testing the ML algorithms. The performance of the ML algorithms is evaluated on several metrics like accuracy, precision, recall, F1-measure, prediction time, and training time.

The rest of this section discusses the results obtained by the ML algorithms on the COVID-19 fake news dataset before and after the feature extraction.

4.1. COVID-19 Fake News Experiment Results Before Feature Extraction

The performance of the ML algorithms on the COVID-19 fake news dataset before the feature extraction is depicted in Figure 3 and Table 2 . From the table, it is evident that the random forest classifier achieves better prediction accuracy, precision, recall, and F1-measure with 83.33, 83.14, 84.90, and 83.61%, respectively. The random forest classifier trains the dataset in the least time when compared to other ML algorithms. The training and testing time of the ML algorithms on the dataset considered before feature extraction is depicted in Figure 4 .

Figure 3 . Classification report before feature extraction.

Table 2 . Performance of the ML algorithms before feature extraction.

Figure 4 . Training and testing time rate before feature extraction.

4.2. COVID-19 Fake News Experiment Results After Feature Extraction

The performance of the ML algorithms on the COVID-19 fake news dataset after the feature extraction is depicted in Figure 5 and Table 3 . From these results, it is evident that the random forest classifier outperforms the other classifiers in terms of accuracy and precision with 88.50 and 87.77%, respectively. Whereas, linear SVC achieved better recall and F1-measure with 89.77 and 88.76%, respectively.

Figure 5 . Classification report after feature extraction.

Table 3 . Performance of the ML algorithms after feature extraction.

The training and prediction time of the ML algorithms on the dataset considered after feature extraction is depicted in Figure 6 . From the figure, it can be observed that the decision tree classifier has the least prediction time and the linear SVC classifier has the least training time.

Figure 6 . Training and Testing time rate after feature extraction.

5. Discussion

From the results section, it is clear that the performance of the machine learning algorithms improves after they are trained with extracted features from the COVID-19 fake news dataset. Since the size of the dataset is approximately 1,100 records, ML algorithms are chosen for classification rather than deep neural network-based algorithms.

The raw fake news dataset has several words that have no affect on the classification results. When the ML algorithms are trained by the raw dataset without feature extraction, there is a very high chance that the performance of the ML algorithms will be affected by some of the frequent words in the text that have no effect on the classification results. Hence, in this work, we employed NER to extract features from the fake news dataset. The extracted features are then fed to the ML algorithms for training. The comparison between the results of the ML algorithms before and after the feature extraction prove that the performance of the ML algorithms increase after feature extraction.

6. Conclusion

In this work, a sophisticated model that extracts important features from a COVID-19 fake news dataset is presented that improves the performance of ML algorithms. Social media applications are the most significant source to spread any kind of information. So in his work, the dataset related to COVID-19 which was the fusion of several diversified social media platforms and news websites is used for evaluating the performance of the proposed model. A data preprocessing step is applied on the dataset to remove several unnecessary symbols like tags and URLs from the dataset. Later on, to enhance the performance of the machine learning algorithms, the relevant subset of features categorized into linguistic and sentiment features are extracted using the NER approach. The extracted features are trained using several state-of-the-art ML algorithms, namely the random forest classifier, AdaBoost classifier, decision tree classifier, and KNN classifier. The performance of the ML algorithms is analyzed before and after the feature extraction. Random forest provides better accuracy at 83.33% without feature extraction, whereas KNN trains the dataset faster than others. Of several ML algorithms, the random forest classifier with feature extraction outperforms the others with an accuracy of 88.50%, precision of 87.77%, recall of 89.77%, and F1 score of 88.76%. The experiment results prove the importance of feature extraction in fake news detection.

Further in future, audio clips, images, and video clips can be considered for generating fake news data as they are the most attractive way to spread fake information. Deep learning algorithms and natural language processing for the semantical understanding of linguistic features can be further analyzed on a dataset containing text, images, audio, and video.

Data Availability Statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material .

Author Contributions

All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2021.788074/full#supplementary-material

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Keywords: COVID-19, fake news, social media, feature extraction, machine learning

Citation: Khan S, Hakak S, Deepa N, Prabadevi B, Dev K and Trelova S (2022) Detecting COVID-19-Related Fake News Using Feature Extraction. Front. Public Health 9:788074. doi: 10.3389/fpubh.2021.788074

Received: 01 October 2021; Accepted: 11 November 2021; Published: 04 January 2022.

Reviewed by:

Copyright © 2022 Khan, Hakak, Deepa, Prabadevi, Dev and Trelova. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Suleman Khan, sulemankhan@IEEE.org ; Saqib Hakak, saqib.hakak@unb.ca

This article is part of the Research Topic

Big Data Analytics for Smart Healthcare applications

COVID-19 vaccination challenges: from fake news to vaccine hesitancy

Affiliations.

• 1 Universidade Potiguar. Av. Senador Salgado Filho 1610, Lagoa Nova. 59056-000 Natal RN Brasil. [email protected].
• 2 Christus Faculdade do Piauí. Piripiri PI Brasil.
• 3 Centro Universitário UNINTA. Sobral CE Brasil.
• 4 Universidade Federal do Piauí. Teresina PI Brasil.
• 5 Universidade Católica de Quixadá. Quixadá CE Brasil.
• 6 Centro Universitário de Patos. Patos PB Brasil.
• 7 Universidade Estadual do Ceará. Fortaleza CE Brasil.
• 8 Universidade Estadual do Maranhão. São Luís MA Brasil.
• PMID: 36888858
• DOI: 10.1590/1413-81232023283.09862022

Abstract in English, Portuguese

This article aims to synthesize articles addressing fake news and COVID-19 vaccine hesitancy in the context of public health. We conducted an integrative review of articles published in any language between 2019 and 2022 in journals indexed in the following databases: Latin American and the Caribbean Literature on Health Sciences, Medical Literature Analysis and Retrieval System Online, Scopus, Web of Science, and Embase. A critical analysis was performed, guided by the research question and objective of the review. Eleven articles were selected, the overwhelming majority of which were cross-sectional studies. The main factors related to vaccine take-up highlighted by the studies were gender, age, education level, political leanings, religion, trust in health authorities, and perceptions of side-effects and vaccine efficacy. The main obstacles to attaining optimal vaccination coverage were vaccine hesitancy and disinformation. All studies addressed the relationship between low vaccination intention and the use of social media as a source of information about SARS-CoV-2. It is necessary to build public trust in vaccine safety and efficacy. Promoting a better understanding of the benefits of COVID-19 vaccination is essential to combat vaccine hesitancy and improve vaccine take-up.

O objetivo deste artigo é sintetizar artigos que abordam fake news e hesitação vacinal contra a COVID-19 no contexto de saúde pública. Revisão integrativa que incluiu estudos originais indexados nas bases de dados Literatura Latino Americana e do Caribe em Ciências da Saúde; Medical Literature Analysis and Retrieval System Online; Scopus; Web of Science e Embase, publicados em qualquer idioma, entre 2019 e 2022. A análise crítica foi realizada na forma descritiva, consoante à pergunta de pesquisa e ao objetivo da revisão. Foram selecionados 11 artigos, com predomínio de estudos transversais. Relacionaram-se ao processo de adesão à vacinação: gênero, idade, estado civil, escolaridade, posicionamento político, religião, confiança em autoridades de saúde, percepção de efeitos colaterais e eficácia das vacinas, entre outros. Hesitação e desinformação são os principais entraves para se alcançar a cobertura vacinal em muitos países. Todos os estudos abordam a relação entre baixa intenção de imunização e uso de mídias sociais como fonte de informação sobre o SARS-CoV-2. É necessário aumentar a confiança na segurança e eficácia das vacinas. A melhor compreensão dos benefícios da vacinação para COVID-19 é imprescindível para combater a hesitação e ampliar a adesão vacinal.

Publication types

• COVID-19 Vaccines
• COVID-19* / prevention & control
• Disinformation*
• Public Health
• Vaccination
• Vaccination Hesitancy

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Science News

How to detect, resist and counter the flood of fake news, although most people are concerned about misinformation, few know how to spot a deceitful post.

As a wave of misinformation threatens to drown us, researchers are coming up with ways for us to get our footing.

Brian Stauffer

Share this:

By Alexandra Witze

May 6, 2021 at 6:00 am

From lies about election fraud to QAnon conspiracy theories and anti-vaccine falsehoods, misinformation is racing through our democracy. And it is dangerous.

Awash in bad information, people have swallowed hydroxychloroquine hoping the drug will protect them against COVID-19 — even with no evidence that it helps ( SN Online: 8/2/20 ). Others refuse to wear masks, contrary to the best public health advice available. In January, protestors disrupted a mass vaccination site in Los Angeles, blocking life-saving shots for hundreds of people. “COVID has opened everyone’s eyes to the dangers of health misinformation,” says cognitive scientist Briony Swire-Thompson of Northeastern University in Boston.

The pandemic has made clear that bad information can kill. And scientists are struggling to stem the tide of misinformation that threatens to drown society. The sheer volume of fake news, flooding across social media with little fact-checking to dam it, is taking an enormous toll on trust in basic institutions. In a December poll of 1,115 U.S. adults, by NPR and the research firm Ipsos, 83 percent said they were concerned about the spread of false information . Yet fewer than half were able to identify as false a QAnon conspiracy theory about pedophilic Satan worshippers trying to control politics and the media.

Scientists have been learning more about why and how people fall for bad information — and what we can do about it. Certain characteristics of social media posts help misinformation spread, new findings show. Other research suggests bad claims can be counteracted by giving accurate information to consumers at just the right time, or by subtly but effectively nudging people to pay attention to the accuracy of what they’re looking at. Such techniques involve small behavior changes that could add up to a significant bulwark against the onslaught of fake news.

Misinformation is tough to fight, in part because it spreads for all sorts of reasons. Sometimes it’s bad actors churning out fake-news content in a quest for internet clicks and advertising revenue, as with “troll farms” in Macedonia that generated hoax political stories during the 2016 U.S. presidential election. Other times, the recipients of misinformation are driving its spread.

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Some people unwittingly share misinformation on social media and elsewhere simply because they find it surprising or interesting. Another factor is the method through which the misinformation is presented — whether through text, audio or video. Of these, video can be seen as the most credible, according to research by S. Shyam Sundar, an expert on the psychology of messaging at Penn State. He and colleagues decided to study this after a series of murders in India started in 2017 as people circulated via WhatsApp a video purported to be of child abduction. (It was, in reality, a distorted clip of a public awareness campaign video from Pakistan.)

Sundar recently showed 180 participants in India audio, text and video versions of three fake-news stories as WhatsApp messages, with research funding from WhatsApp. The video stories were assessed as the most credible and most likely to be shared by respondents with lower levels of knowledge on the topic of the story. “Seeing is believing,” Sundar says.

Video sells

WhatsApp users looked at three versions of a story that falsely claimed that rice was being made out of plastic — in (left to right) text, audio or a video showing a man feeding plastic sheets into a machine.

Participants tended to rate the video version as more credible than the audio or text versions. The effect diminished for users who were highly involved with the topic of the false story, suggesting that video is a particularly compelling medium for those who may not be knowledgeable on the topic at hand.

Perceived credibility of a message based on format and issue involvement

The findings, in press at the Journal of Computer-Mediated Communication , suggest several ways to fight fake news, he says. For instance, social media companies could prioritize responding to user complaints when the misinformation being spread includes video, above those that are text-only. And media-literacy efforts might focus on educating people that videos can be highly deceptive. “People should know they are more gullible to misinformation when they see something in video form,” Sundar says. That’s especially important with the rise of deepfake technologies that feature false but visually convincing videos ( SN: 9/15/18, p. 12 ).

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One of the most insidious problems with fake news is how easily it lodges itself in our brains and how hard it is to dislodge once it’s there. We’re constantly deluged with information, and our minds use cognitive shortcuts to figure out what to retain and what to let go, says Sara Yeo, a science-communication expert at the University of Utah in Salt Lake City. “Sometimes that information is aligned with the values that we hold, which makes us more likely to accept it,” she says. That means people continually accept information that aligns with what they already believe, further insulating them in self-reinforcing bubbles.

Special report: Awash in deception

• A few simple tricks make fake news stories stick in the brain
• Vaccine hesitancy is nothing new. Here’s the damage it’s done over centuries
• Climate change disinformation is evolving. So are efforts to fight back

Compounding the problem is that people can process the facts of a message properly while misunderstanding its gist because of the influence of their emotions and values , psychologist Valerie Reyna of Cornell University wrote in 2020 in Proceedings of the National Academy of Sciences .

Thanks to new insights like these, psychologists and cognitive scientists are developing tools people can use to battle misinformation before it arrives — or that prompts them to think more deeply about the information they are consuming.

One such approach is to “prebunk” beforehand rather than debunk after the fact. In 2017, Sander van der Linden, a social psychologist at the University of Cambridge, and colleagues found that presenting information about a petition that denied the reality of climate science following true information about climate change canceled any benefit of receiving the true information . Simply mentioning the misinformation undermined people’s understanding of what was true.

That got van der Linden thinking: Would giving people other relevant information before giving them the misinformation be helpful? In the climate change example, this meant telling people ahead of time that “Charles Darwin” and “members of the Spice Girls” were among the false signatories to the petition. This advance knowledge helped people resist the bad information they were then exposed to and retain the message of the scientific consensus on climate change.

Here’s a very 2021 metaphor: Think of misinformation as a virus, and prebunking as a weakened dose of that virus. Prebunking becomes a vaccine that allows people to build up antibodies to bad information. To broaden this beyond climate change, and to give people tools to recognize and battle misinformation more broadly, van der Linden and colleagues came up with a game, Bad News , to test the effectiveness of prebunking (see Page 36). The results were so promising that the team developed a COVID-19 version of the game, called GO VIRAL! Early results suggest that playing it helps people better recognize pandemic-related misinformation.

Take a breath

Sometimes it doesn’t take very much of an intervention to make a difference. Sometimes it’s just a matter of getting people to stop and think for a moment about what they’re doing, says Gordon Pennycook, a social psychologist at the University of Regina in Canada.

In one 2019 study, Pennycook and David Rand, a cognitive scientist now at MIT, tested real news headlines and partisan fake headlines, such as “Pennsylvania federal court grants legal authority to REMOVE TRUMP after Russian meddling,” with nearly 3,500 participants. The researchers also tested participants’ analytical reasoning skills. People who scored higher on the analytical tests were less likely to identify fake news headlines as accurate, no matter their political affiliation. In other words, lazy thinking rather than political bias may drive people’s susceptibility to fake news, Pennycook and Rand reported in Cognition .

When it comes to COVID-19, however, political polarization does spill over into people’s behavior. In a working paper first posted online April 14, 2020, at PsyArXiv.org, Pennycook and colleagues describe findings that political polarization, especially in the United States with its contrasting media ecosystems, can overwhelm people’s reasoning skills when it comes to taking protective actions, such as wearing masks.

Inattention plays a major role in the spread of misinformation, Pennycook argues. Fortunately, that suggests some simple ways to intervene, to “nudge” the concept of accuracy into people’s minds, helping them resist misinformation. “It’s basically critical thinking training, but in a very light form,” he says. “We have to stop shutting off our brains so much.”

Push in the right direction

Nudging Twitter users to think about the accuracy of a nonpolitical headline resulted in users temporarily sharing more information from more trustworthy media outlets (blue dots toward the right) and less from less trustworthy outlets (blue dots toward the left). Dot size is proportional to the number of tweets that link to that website prior to the accuracy nudge.

Effect of an accuracy nudge on news sharing

With nearly 5,400 people who previously tweeted links to articles from two sites known for posting misinformation — Breitbart and InfoWars — Pennycook, Rand and colleagues used innocuous-sounding Twitter accounts to send direct messages with a seemingly random question about the accuracy of a nonpolitical news headline. Then the scientists tracked how often the people shared links from sites of high-quality information versus those known for low-quality information, as rated by professional fact-checkers, for the next 24 hours.

On average, people shared higher-quality information after the intervention than before. It’s a simple nudge with simple results, Pennycook acknowledges — but the work, reported online March 17 in Nature , suggests that very basic reminders about accuracy can have a subtle but noticeable effect .

For debunking, timing can be everything. Tagging headlines as “true” or “false” after presenting them helped people remember whether the information was accurate a week later, compared with tagging before or at the moment the information was presented, Nadia Brashier, a cognitive psychologist at Harvard University, reported with Pennycook, Rand and political scientist Adam Berinsky of MIT in February in Proceedings of the National Academy of Sciences .

How to debunk

Debunking bad information is challenging, especially if you’re fighting with a cranky family member on Facebook. Here are some tips from misinformation researchers:

• Arm yourself with media-literacy skills, at sites such as the News Literacy Project (newslit.org), to better understand how to spot hoax videos and stories.
• Don’t stigmatize people for holding inaccurate beliefs. Show empathy and respect, or you’re more likely to alienate your audience than successfully share accurate information.
• Translate complicated but true ideas into simple messages that are easy to grasp. Videos, graphics and other visual aids can help.
• When possible, once you provide a factual alternative to the misinformation, explain the underlying fallacies (such as cherry-picking information, a common tactic of climate change deniers).
• Mobilize when you see misinformation being shared on social media as soon as possible. If you see something, say something.

Prebunking still has value, they note. But providing a quick and simple fact-check after someone reads a headline can be helpful , particularly on social media platforms where people often mindlessly scroll through posts.

Social media companies have taken some steps to fight misinformation spread on their platforms, with mixed results. Twitter’s crowdsourced fact-checking program, Birdwatch, launched as a beta test in January, has already run into trouble with the poor quality of user-flagging . And Facebook has struggled to effectively combat misinformation about COVID-19 vaccines on its platform.

Misinformation researchers have recently called for social media companies to share more of their data so that scientists can better track the spread of online misinformation. Such research can be done without violating users’ privacy, for instance by aggregating information or asking users to actively consent to research studies.

Much of the work to date on misinformation’s spread has used public data from Twitter because it is easily searchable, but platforms such as Facebook have many more users and much more data. Some social media companies do collaborate with outside researchers to study the dynamics of fake news, but much more remains to be done to inoculate the public against false information.

“Ultimately,” van der Linden says, “we’re trying to answer the question: What percentage of the population needs to be vaccinated in order to have herd immunity against misinformation?”

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