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
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.
There is a lot happening on that graph with not nearly enough metrics to tell you what it's presenting
Isn't this "Who is exposed to fake news sources", not "who shared fake news sources"?
Yes, the irony if mislabelling data about misinformation is fun
This is just referring to completely fabricated stories right? I assume very biased stories are a lot more common.
Sure does look like gullibility is a factor in politics.
George Soros told you to say that didn’t he?!
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
Sounds like fake news to me. /s