3 ms·

Fake news is fake because the facts aren't real. machine learning isn't going to learn how to identify it when sentient people have difficulty.

zxcmx 7y ago
Hm. I think the kind of fake news most people are worried about on social media has properties which distinguish it from merely "factually incorrect information". It's a kind of "supernormal stimulus" - high virality, engineered to trigger tribalism and strong emotions in susceptible recipients. The properties which make it a "weapon" are the ones which you'd want to mitigate. Unfortunately those properties also happen to goose the engagement metrics of social media companies. Source reputation, rate and pattern of viral spread should be taken into account, as well as content itself and user flags. Also user engagement behaviour - e.g. average user shared this after reading for less than 10 seconds. Basically, highly partisan content spread quickly by unthinking users, from a low reputation source. Unfortunately the networks themselves have the best metadata to work this stuff out... and the least genuine desire to do so. I'm in the "solving the worst of this looks fairly tractable" camp, but I agree that bare source text is going to be hard to work with.
IronWolve 7y ago
I did state you would have to use reputation scores too. If I see its the onion or babylon bee, I can probably assume its satire.
ksaj 7y ago
Isn't that just saying machine learning is incapable of producing novel discoveries that would have been difficult for humans? Because it has already succeeded in that area. The problem with fake news is that it takes advantage of a lack of knowledge surrounding the presented facts. Which is to say, if you aren't feeding that background knowledge to the ML engine from the start, it's fooled just like we are.

3 ms·

Fake news is fake because the facts aren't real. machine learning isn't going to learn how to identify it when sentient people have difficulty.

by notfromhere 7y ago

zxcmx 7y ago
Hm. I think the kind of fake news most people are worried about on social media has properties which distinguish it from merely "factually incorrect information". It's a kind of "supernormal stimulus" - high virality, engineered to trigger tribalism and strong emotions in susceptible recipients. The properties which make it a "weapon" are the ones which you'd want to mitigate. Unfortunately those properties also happen to goose the engagement metrics of social media companies. Source reputation, rate and pattern of viral spread should be taken into account, as well as content itself and user flags. Also user engagement behaviour - e.g. average user shared this after reading for less than 10 seconds. Basically, highly partisan content spread quickly by unthinking users, from a low reputation source. Unfortunately the networks themselves have the best metadata to work this stuff out... and the least genuine desire to do so. I'm in the "solving the worst of this looks fairly tractable" camp, but I agree that bare source text is going to be hard to work with.
IronWolve 7y ago
I did state you would have to use reputation scores too. If I see its the onion or babylon bee, I can probably assume its satire.
ksaj 7y ago
Isn't that just saying machine learning is incapable of producing novel discoveries that would have been difficult for humans? Because it has already succeeded in that area. The problem with fake news is that it takes advantage of a lack of knowledge surrounding the presented facts. Which is to say, if you aren't feeding that background knowledge to the ML engine from the start, it's fooled just like we are.