Stream: Real Versus Fake Fake News
On the last day of bad old year, Lake Superior State University released its 43rd annual List of Words Banished from the Queen’s English for Misuse, Overuse and General Uselessness.
The ripest for excision was “fake news”. “Let that sink in”.
I mean, “let that sink in” was another “impactful” phrase needing banning. As was (can we get an Amen?) “impactful”. (Most banned words are those the fill the air in corporate meetings.)
“Fake news” has to go not because there is no such thing, but because (a) people have the habit of calling real but unwanted or undesirable news “fake”, and (b) because defining it isn’t so easy.
“Fake news” is thus like bad yet award-winning art. You know it when you see it, but good luck developing an unambiguous definition.
Facebook head fake
Facebook discovered the second point the hard and expensive way. They had been testing “fake news” detection algorithms by putting “disputed” flags on certain stories. But a funny thing happened. The red flags had “the reverse effect of making people want to click [stories] even more”.
This doesn’t mean the complete disappearance of “fake news” from Facebook. On stories meeting their selection criteria for fakiness, they will link counter stories called “related articles”. Call this the he-said-she-said approach.
Facebook’s wanting to weaken “deeply held beliefs” is curious. It implies that Facebook has stored in their massive computer banks a list of correct beliefs to which its customers’ false but “deeply held beliefs” can be compared. […]
Not that people aren’t trying.
Some college kids think they have developed a browser plug-in that can alert users to “fake news”. And a group of folks at the Fake News Challenge believe they can harness “artificial intelligence” (statistical models that have undergone dull-knifed plastic surgeries and name changes) to identify made-up stories with “an intention to deceive”. Which is to say, propaganda.
It is charming the simple faith many have in the ability of computers to mimic human thinking. Yet all these algorithms can do is to note patterns in data, which when fed a new observation classifies it into one of the patterns. That means somebody has to create a list of news stories which have been without error or controversy placed into “truth” and “propaganda” bins.
Place a reactionary and progressive in a room and […]
Computers can’t think
As he was heading out the door, Google’s Eric Schmidt spoke on this.
Research shows (p < 0.050 only the most intelligent of you will click here and read the rest.
Update Macron proposes new law against fake news: Sites that distribute fake news would face punishment. Guess who gets to define what is “fake”.