Community for discussion about experiments or discoveries made with scientific methods.
Links to articles: please preserve headlines when possible, shortening / replacing as needed. When multiple articles are involved, please consider a text post.
If there is a narrower community available, discussion is encouraged there.
If a topic relates more closely to application of knowledge than obtaining it, discussion is encouraged in c/technology.
Attribution for the banner image: Image by FreePik
Among those who shared any political content on Twitter during the election, fewer than 5% of people on the left or in the center ever shared any fake news content, yet 11 and 21% of people on the right and extreme right did
Grinberg, N., Joseph, K., Friedland, L., Swire-Thompson, B., & Lazer, D. (2019). Fake news on Twitter during the 2016 U.S. presidential election. Science, 363(6425), 374–378. doi:10.1126/science.aau2706
Ironically, the misleadingly biased visualization makes this tantamount to fake news.
It's not even close to fake news. Logarithmic scales are standard in this kind of visualization. The thrust of the result is that right-wing people share more fake news, and if you look at the graph, this is clear. If you mistake the X-axis as a linear scale, the result makes the effect less pronounced, not more.
So if anything, the graph undersells the thesis in the name of creating a more compact and readable visualization. There is no deception here.
Exactly, and that's the problem! When the chart makes it look like the right "only" shares maybe twice as much fake news when it's actually 10x-100x more, it makes the right look way less bad than it actually is.
there's also superconsumer and supersharer on the "political right" side of the chart causing a visual bias
I'm less upset about those, but I agree that it would be nice to have a vertical gap between them and the ideological clusters above to make it clearer that they're orthogonal categories of grouping.