Catching Misinformation in a Digital Age
Updated: Apr 18
By Ziao Yin
Image via CNN
Shocking headlines, out of context clips, and skewed data are all part of the overwhelming levels of information that rapidly inundate our newsfeeds. Edited pictures, videos, and audios on the internet aren’t always real, directing people into believing and spreading that misinformation. These deceptions that float around on the web lead to major consequences. When just one person shares a fake claim on Instagram, it can cause up to 1000 people to receive that information. Now, more than ever, the truth is so important. After seeing how lies and fabrication can lead to confusion amongst a national pandemic and even a capital insurrection, it is up to everyone to stand their ground. With how fast information can travel across the world, individuals spotting misinformation can be the tool needed to end this era of falsehood. Below are the four best ways to quickly spot false claims.
Many organizations, mostly affiliated with a political party, often manipulate axes to prove a claim.
Graph A, for example, tries to prove a point on how rainfall in Seattle is drastically increasing, a way to scare and overemphasize how one day the rain can flow higher than the skyscrapers of Seattle. However, when you take a closer look, you see that the years selected are quite odd, following no apparent order or rule in selection. Then take a look at graph B. It shows the annual precipitation from the 1980s and the picture becomes clearer. Rainfall can vary so much and what the first graph did was cherry-pick years when rainfall was low for past years and cherry-pick the high precipitation years for more current years. Many tactics like these often successfully panic the people.
In contrast, graph C was a claim, made by a right-wing organization, downplaying and even outright denying the fact that climate change is not real. Unlike graph A, this graph tries to encourage people to not freak out about climate change. The real culprit lies not in the x-axis, but the y-axis this time. The axis is severely zoomed, spanning from -10 F to 110 F, and with any line, zooming out on one axis can make the line be perceived as flat. When we take the same data from the graph and apply it is a more approximate range, like in graph D, we see that climate change is an issue and is rising at an alarming rate.
Although making unfair comparisons has been happening all throughout time, it has really risen under the COVID-19 pandemic.
When the pandemic first started, many scepticists were claiming that COVID was just a normal flu, pointing to data like graph E to show how humble the virus was. What wasn’t taken into consideration was that the data was collected from the start of a pandemic, one that would grow much worse, and compared to past diseases that are over. This comparison is unfair and misleading. Graph F is an edited depiction of what it would look like now, near the hopeful end of the pandemic. Taking this updated graph to mind completely changes COVID-19’s story and how seriously it should have been taken.
Graph E) and F) via “Information is Beautiful”
News articles and clickbait is another way that outlets get attention from their audience, making claims that are too good/bad to be true.
Graph G is a drastic comparison of how the type of music songwriters consume can lead to a difference in life expectancy. Although this is an interesting topic and some variation could happen, a gap of 50 years is too extreme to be true. Digging deep into the data, there is a clear reason why this gap is there. Music genres like rap, hip hop, and metal are all fairly new compared to blues, jazz, and country. This means that many artists in younger genres are usually younger and haven’t died yet. The ones that do die are often anomalies, something that happens with every genre of music. Knowing this makes sense and proves that listening to rap and hip hop won’t decrease your life expectancy.
Graph G) via “The Conversation”
Many companies use data that have a selection bias in ads to sway many audiences.
Selection bias is when a highly selected group of people are taken to represent a much wider audience. Let’s say a student were to take the average height of Americans. Going to an NBA game and collecting data from the players would not be an accurate depiction, since they are a highly selected group of people that differs from a much wider group. A real-life example of this would be the classic insurance ad that claims to save you $500 upon switching. This is a claim made by most insurance companies and the question that comes to mind is “how?” Well, the data collected by the companies are highly selected, since it is only based on people who do switch. The vast majority of people who won’t save will not switch companies, therefore not be represented in this data.
Another example, relevant to the pandemic, is a claim made by New York dentists, saying that since the pandemic, they have seen an increase in dental issues. They attributed this to wearing masks whereas, in reality, masks probably had nothing to do with the data. The data comes from the fact that citizens will not be as likely to leave their houses for routine cleanings unless pain or dental problems occur. This led to a percentage increase of people coming in with poor dental hygiene.
Knowing these four ways of deception is the key to spotting misinformation. These can be sneaky, sliding past the minds of even the most cautious people. Seeing how data can be skewed and what to look for can help the world call out deception and save many from being fed the wrong information.
Written by writer Ziao Yin