Title : "Pro-Trump users were about three times more likely to visit fake news sites supporting their candidate than Clinton partisans were to visit bogus sites promoting her."
link : "Pro-Trump users were about three times more likely to visit fake news sites supporting their candidate than Clinton partisans were to visit bogus sites promoting her."
"Pro-Trump users were about three times more likely to visit fake news sites supporting their candidate than Clinton partisans were to visit bogus sites promoting her."
So says a study done by 3 political scientists at Dartmouth College and reported in the NYT, in "‘Fake News’: Wide Reach but Little Impact, Study Suggests."But how did they decide which sites were "fake news sites"? Does this "three times more likely" finding have more to do with the sort of mind that embraces Donald Trump or more to do with how they went about classifying websites as "fake news sites"?
The team defined a visited website as fake news if it posted at least two demonstrably false stories, as defined by economists Hunt Allcott and Matthew Gentzkow in research published last year. On 289 such sites, about 80 percent of bogus articles supported Mr. Trump.Does that mean that there was more bogosity in support of Trump or that those doing the classifying were more likely to see bogosity when it supported Trump? Once there were more pro-Trump websites in the set of fake news sites, did that skew the finding that pro-Trumpers were 3 times more likely to visit fake news sites? There were more than 3 times as many pro-Trump sites as pro-Clinton sites (80%, not 75%), so perhaps means that pro-Trumpers were less likely to go to pro-Trump fake news sites that pro-Clinton people were to got to pro-Clinton sites.
And, by the way, I don't see how a website deserves to be called a "fake news site" just because it publishes "two demonstrably false stories." I'd assume that the most respected news sites, including the NYT, published "two demonstrably false stories." Or is "demonstrably" a technical term that works to exclude the kinds of falsities that make it into the Times? I know there were some blatantly made up things like Pope Francis endorses Donald Trump, so maybe that's what these researchers counted as "demonstrably false."
I know, I could read the older article by economists Hunt Allcott and Matthew Gentzkow to find out what definition of fake news was used in this newer study, but isn't it annoying that we're not just told the definition?! It's not as though there's any reason to believe that Allcott and Gentzkow nailed down the true meaning of fake.
Now, I actually am scanning Allcott/Gentzkow. They don't use the words "demonstrably false." Okay, here's the relevant text (boldface added):
We define “fake news” to be news articles that are intentionally and verifiably false, and could mislead readers. We focus on fake news articles that have political implications, with special attention to the 2016 US presidential elections. Our definition includes intentionally fabricated news articles, such as a widely shared article from the now-defunct website denverguardian.com with the headline, “FBI agent suspected in Hillary email leaks found dead in apparent murder-suicide.” It also includes many articles that originate on satirical websites but could be misunderstood as factual, especially when viewed in isolation on Twitter or Facebook feeds...If that's the definition, I'd say it's impossible to avoid subjectivity in making the classification. And the definition itself contains bias. For one thing, it's designed to get mainstream media off the hook. Anything false will be presumed to be "unintentional reporting mistakes."
Our definition rules out several close cousins of fake news: 1) unintentional reporting mistakes, such as a recent incorrect report that Donald Trump had removed a bust of Martin Luther King Jr. from the Oval Office in the White House; 2) rumors that do not originate from a particular news article; 3) conspiracy theories (these are, by definition, difficult to verify as true or false, and they are typically originated by people who believe them to be true); 4) satire that is unlikely to be misconstrued as factual; 5) false statements by politicians; and 6) reports that are slanted or misleading but not outright false (in the language of Gentzkow, Shapiro, and Stone 2016, fake news is “distortion,” not “filtering”).
But what matters more than whether people clicked through to various low-quality articles is whether they read competently and maintained their critical thinking. And critical thinking is even more important when the distortions and dishonesty of a website is outside of the Allcott/Gentzkow definition.
The NYT article about the Dartmouth study stresses that it found that "fake news paled in influence beside mainstream news coverage." But the kind of "fake news" that's in mainstream media is much more difficult to discern and defend yourself from than these outright fabrications and misunderstood satires that fit the Allcott/Gentzkow definition. I assume most people are learning how to spot crude and obvious fakery and not embarrass themselves by passing along stuff that their Facebook friends will tell them is satire or a fabrication. It takes a much high level of critical thinking to resist the "fake news" that's excluded from the Allcott/Gentzkow definition.
One more thing in the NYT article that I wanted to highlight: "Perhaps confusingly, moderately left-leaning people viewed more pro-Trump fake news than they did pro-Clinton fake news." I don't find that confusing. Just switch the term "pro-Trump" to "anti-Clinton" and it makes perfect sense. Lots of lefties were against Clinton.
Thus articles "Pro-Trump users were about three times more likely to visit fake news sites supporting their candidate than Clinton partisans were to visit bogus sites promoting her."
that is all articles "Pro-Trump users were about three times more likely to visit fake news sites supporting their candidate than Clinton partisans were to visit bogus sites promoting her." This time, hopefully can provide benefits to all of you. Okay, see you in another article posting.
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