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
There is a lot happening on that graph with not nearly enough metrics to tell you what it’s presenting
Also worth noting that the X axis is growing by orders of magnitude and not linearly.
Did my best with the information I had. Which was basically only the graph itself.
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.
If you mistake the X-axis as a linear scale, the result makes the effect less pronounced, not more.
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.
What are “superconsumers” and “supersharers?” Are those politically neutral terms, or are they further extentions to the right like the graphs seem to imply?
Yes, they are suspected right-wing bots separated from the data-set based on a set of criteria that marks them as outliers.
The “supersharers” and “superconsumers” of fake news sources—those accountable for 80% of fake news sharing or exposure—dwarfed typical users in their affinity for fake news sources and, furthermore, in most measures of activity. For example, on average per day, the median super- sharer of fake news (SS-F) tweeted 71.0 times, whereas the median panel member tweeted only 0.1 times. The median SS-F also shared an average of 7.6 political URLs per day, of which 1.7 were from fake news sources. Similarly, the median superconsumer of fake news sources had almost 4700 daily exposures to political URLs, as compared with only 49 for the median panel member (additional statistics in SM S.9). The SS-F members even stood out among the overall supersharers and superconsumers, the most politically active accounts in the panel (Fig. 2). Given the high volume of posts shared or consumed by superspreaders of fake news, as well as indicators that some tweets were authored by apps, we find it likely that many of these accounts were cyborgs: partially automated accounts controlled by humans (15) (SM S.8 and S.9). Their tweets included some self-authored content, such as personal commentary or photos, but also a large volume of political re-tweets. For subsequent analyses, we set aside the supersharer and superconsumer outlier accounts and focused on the remaining 99% of the panel.
Who is determining what is and isn’t fake news?
I’d check the paper, but it’s paywalled
