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Writer's picturePre-Collegiate Global Health Review

The Rise of Fake News in the Age of COVID-19 and How We Can Combat It

Nysa Dharan, Denmark High School, Alpharetta, Georgia, USA


“COVID is fake! The vaccines are not safe! CBD and hydroxychloroquine can cure COVID!” These are examples of some of the fake news that has been spreading throughout social media. Although fake news is hardly a new topic, it has risen immensely in recent years. According to an annual Ipsos survey in 2019 of over 25,000 users in 25 countries, a staggering eighty-six percent of internet users have been duped by fake news (Ipsos, 2019). According to the survey, 35% of users believe the United States is the one to blame for this problem, followed closely by Russia and China. This is because social media usage is especially prevalent in these high-population countries. Countries that are in closer proximity of these fake news exporters consume more social media content from them, and therefore more fake news. As per the survey, for example, Japan and Hong Kong believed China to be the largest source of fake news because of this comparatively high influx. This article seeks to determine how fake news has affected the COVID-19 pandemic, and how we can take steps to diminish its effects.

The World Health Organization (WHO) dubs this an “infodemic,” or an overabundance of information, both fake and real, which can be dangerous especially in public health emergencies. One particularly dangerous rumor claims that consumption of highly concentrated alcohol can kill the coronavirus. This rumor alone was associated with an estimated 700 deaths in the first 3 months of 2020, and has caused more than 6000 hospitalizations (Aljazeera, 2020).

There are two major types of fake information that is shared on social media (Lazer, 2018). The first type of fake information is shared with malicious intent, called disinformation. Specific types of disinformation might include placing blame for the spread of COVID-19 on specific ethnic groups or on the government. The purpose of disinformation is to create a greater political divide and chaos for the sake of a specific political agenda. The second type is misinformation, which is spread due to a shared misunderstanding. The problem with misinformation is that opinion is spread as fact, and humans who are very susceptible to such opinions will further spread it, perpetuating a cycle of lies. In addition, a study from the London School of Economics states that people are more likely to spread bad news rather than good news (Soroka, 2015). Therefore, fake information about the vaccine or about mask wear that is painted in a negative light is likely to spread faster.

At the beginning of lockdowns, message made absurd claims about the state of the coronavirus and potential cures. These posts used the logical fallacy of the appeal to authority, making media consumers believe that they were from a valid source, and are therefore correct. While these small messages might seem harmless, a study showed that COVID-19 misinformation constituted almost 70% of the total social media engagement in 2020 (Reuters Institute, 2020). Another statistic in Brazil showed that on just WhatsApp and Facebook, over 329 fake news articles about COVID-19 were shared in 2020. The most frequent category of fake news was political (20.1%), followed closely by epidemiology and statistics (19.5%). In a pandemic, where sharing information—and the correct information —is crucial for slowing down cases, this statistic is fatal.

A 2021 study recruited over 4,500 participants and showed them a series of news stories, either fake or real, and gauged their response (Greene, 2021). The study provided an example of a fake news trend showing a 'link' between the MMR vaccine and autism. In this study, there was a 10% decrease in childhood vaccination rates, which caused a spike in measles cases. The fake stories did seem to change people's behavior, "but not dramatically so" (Greene, 2021). However, because the study was conducted at the individual level, it therefore showed a smaller effect than if it was done to a large group. If each individual person is even just slightly affected by seeing a fake news article, the subsequent effect on a group or country could be devastating.

In today’s world, the Internet is a major source of knowledge, from academia and research to social connections and memories. Anyone can share what they know on any subject, regardless of whether they have the authority and/or sufficient knowledge to do so. Despite this fact, social media has become the number one resource to determine the status of anything going on in the world. Tools that will help identify fake news faster and prevent it from spreading are becoming crucial in our society. One tool that can help is machine learning. Fake news is a sequence of words. Since machine learning algorithms need numbers, one needs to transform the textual context of a sequence of words into numbers. To do this, one can use various approaches such as Bag-of-Words and Document embedding, which turn words into numbers that the machine can process. To test the hypothesis of whether machine learning is an appropriate instrument to identify fake news, a machine learning model was created by the author of this article, and past news articles were gathered to use as data. The trained machine learning model was then used to predict fake news. The results showed that with a combined effort of technology and human thinking, we could stand a chance against the effects of the “infodemic”. An example where its use can be imperative is in the fight against COVID-19 spread. If COVID-19 information can be filtered using machine learning to determine whether news is ‘real’ or ‘fake’, then the negative effects of misinformation can be avoided. This allows for a significantly more cooperative public, which is imperative in the face of a global health crisis. If properly addressed using tools such as machine learning, the effects of fake news can be minimized.

Until tools like this can be widely used, there are several things that we can do that can make us less susceptible to fake news about COVID-19 and other infectious diseases. Every time you look at information, make a conscious effort to determine whether the information is coming from a trusted source. Before you share information, make sure that you weren't swayed by opinionated language or logical fallacies. By using these techniques and learning to be more aware, we can stay safer both online and in the real world.

References

Aljazeera. (2020) Iran: Over 700 Dead after Drinking Alcohol to Cure Coronavirus. Aljazeera. Available at: https://www.aljazeera.com/news/2020/04/iran-700-dead-drinking-alcohol-cure-coronavirus-200427163529629.html.

Bajaj, Samir. (2017) "The Pope Has a New Baby!" Fake News Detection Using Deep Learning.https://web.stanford.edu/class/archive/cs/cs224n/cs224n.1174/reports/2710385.pdf

Caldera, Camille. (2021) Fact Check: Story That Pope Francis Was Arrested Is Fabricated. USA TODAY, https://www.usatoday.com/story/news/factcheck/2021/01/12/fact-check-pope-francis-not-arrested-story-fabricated/6625183002/.

Kassam, Natasha. (2020). Disinformation and coronavirus. The Interpreter. Lowy Institute. https://www.lowyinstitute.org/the-interpreter/disinformation-and-coronavirus

Greene, Ciara. (2021). COVID-19: The first study to look at whether fake news actually changes people's behavior. Devdiscourse. https://www.devdiscourse.com/article/health/1632790-covid-19-the-first-study-to-look-at-whether-fake-news-actually-changes-peoples-behaviour

(2020). Fake News! COVID-19 vaccine won't turn you into a zombie. BusinessToday.in https://www.businesstoday.in/coronavirus/fake-news-covid19-vaccine-wont-turn-you-into-a-zombie/story/426130.html

(2021). Fake News Data Set. https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset

Ipsos. (2019). Fake News: A Global Epidemic Vast Majority (86%) of Online Global Citizens Have Been Exposed to It. Ipsos. https://www.ipsos.com/en-us/news-polls/cigi-fake-news-global-epidemic. Jan. 2021.

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Lazer, DM et al. (2018). The science of fake news. Science 359(6380): 1094–1096.

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Montesi M. (2021). Understanding fake news during the Covid-19 health crisis from the perspective of information behavior: The case of Spain. Journal of Librarianship and Information Science. 2021;53(3):454-465. doi:10.1177/0961000620949653

Onyeador, Obi. (2020). Thumbnail from https://unsplash.com/photos/qXfD_nG4j-U

Phillippine Daily Inquirer (PDI). (2021). 86% of net users fall for fake news. INQUIRER.net, https://technology.inquirer.net/87850/86-of-net-users-fall-for-fake-news

Reuters. (2020). Fact Check: The Coronavirus Pandemic Is Not a Hoax or a Conspiracy to Control the General Public. Reuters Institute. https://www.reuters.com/article/uk-factcheck-hoax-idUSKBN25G2KM

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Reuters Institute. (2020). Types, sources, and claims of COVID-19 misinformation. Reuters Institute for the Study of Journalism. https://reutersinstitute.politics.ox.ac.uk/types-sources-and-claims-covid-19-misinformation

Soroka, Stuart. (2015). "Why do we pay more attention to negative news than to positive news?," British Politics and Policy at LSE, https://blogs.lse.ac.uk/politicsandpolicy/why-is-there-no-good-news/

Thota, Aswini et al. (2018) Fake News Detection: A Deep Learning Approach, SMU Data Science Review, https://scholar.smu.edu/cgi/viewcontent.cgi?article=1036&context=datasciencereview


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