Beware of data shared via social media - get the facts

Like infographics, social media and other forms of user-generated content pose unique challenges regarding data. Many news outlets and journalists have checks and balances in place to ensure that information reported (especially information based on data) is as accurate as possible. But those same checks and balances do not hold true for those creating and sharing content on social media. Social media gives user-generated content a wider reach and greater influence than ever before. Sharing content on social media poses additional challenges. For example, it can be difficult to identify the original source, and consequently, its credibility. In this post we’ll examine common issues that arise with user-generated and shared social content.

Social media posts are often criticized for allowing users to share information without putting it in context. This is particularly problematic with data. While the numbers and charts may represent real information, the reader needs context to interpret information correctly. Readers may inadvertently share questionable charts or statistics because social media platforms make it easy to do so. Images of graphs and charts pulled from research articles can quickly be shared without citing the source. These images might then circulate widely without context.

Intended for sharing

Even thoughtfully designed charts and graphs taken out of context can be problematic, but what happens when content is designed specifically for social media? Authors of social content know that readers often prefer brief messages instead of a large amount of information. Knowing shorter content is more likely to quickly spread to a wider audience, authors may intentionally design their social posts with content that will catch the eye of the audience they wish to reach.

In earlier posts, we’ve discussed features that can make data and data visualizations hard to interpret correctly. Whether it’s logarithmic scales or truncated axes, we’ve seen how these may make data easier to read, but may unintentionally cause readers to draw inaccurate conclusions. While these mistakes are often unintentional, some users may intentionally manipulate visualizations to reinforce a specific point of view.

A fresh look

Let’s take another look at some of the data we examined before. These two charts show life expectancy in different countries. We demonstrated how truncating the axes allow you to see the differences in data, but also might lead readers to view those differences as more significant than they really are.

Figure 2. Global Life Expectancy (full axis) World Bank (male) World Bank (female)

Does Figure 3 seem like something you might see on your social media feed? It's the same data as in the previous two charts, but it’s been heavily manipulated so that you focus on what the author wants you to see. First, it includes a large title with the conclusion the author wants you to make – that moving to France will lead you to live a longer life. It doesn’t mention that this is solely the life expectancy of the country’s residents. A variety of cultural and lifestyle factors come into play and would not apply if you simply move to a place. Also, removing grid lines and shrinking the size of the font showing age makes it harder to read the scale of the data. The choice of colors also further conveys the purpose — implying that the US is worse than France.

Figure 4. Homicide Rate Line Chart CDC

Figure 5. Homicide Rate Social Media Post CDC