Guest essay by Eric Worrall
After training a computer to look for online climate “misinformation”, John Cook was surprised that people don’t trust the proposed solutions.
Climate change: How machine learning holds a key to combating misinformation
John Cook
Postdoctoral Research Fellow, Monash Climate Change Communication Research Hub“A lie can travel halfway around the world before the truth can get its boots on.”
This quote appears in many forms. In some variants, the quote involves footwear. In other cases, the truth is struggling to get its pants on.
Regardless of the details, the sentiment encapsulates a key challenge of misinformation. By the time the meticulous task of fact-checking is complete and the correction has been disseminated, the misinformation has already spread widely and achieved all sorts of mischief.
Consequently, misinformation researchers speak wistfully of the “holy grail of fact-checking” – automatically detecting and debunking misinformation in one fell swoop. Machine learning offers the potential of both speed and scale – the ability to identify misinformation the instant it appears online, and the technical capacity to distribute solutions at the scale required to match the size of the problem.
But the holy grail quest faces a seemingly insurmountable hurdle. Misinformation evolves and sprouts new forms. How can you detect a myth before you even know what it is or what form it will take?
…
Once we had trained our machine to detect and categorise different misinformation claims, we fed our model 20 years’ worth of climate misinformation – more than 250,000 articles from 20 prominent conservative think-tank websites and 33 blogs. It’s the largest content analysis to date on climate misinformation, making it possible to construct a two-decade history of climate misinformation.
The results weren’t what I expected at all.
The erosion of public trust in climate scientists
During the past 15 years, I’ve been debunking scientific climate misinformation – the type of myths that fell under the categories “it’s not happening”, “it’s not us”, or “it’s not bad”.
It turns out these were the least common forms of climate misinformation. Instead, the largest category of climate misinformation was attacks on scientists and on climate science itself.
Climate misinformation isn’t about providing its own alternative explanation of what’s happening to our climate. Instead, it’s focused on casting doubt on the integrity of climate science, and eroding public trust in climate scientists.
…
But that’s not where misinformation is focused – the focus is on attacking scientists and science itself. There’s a dearth of research into understanding and countering this type of misinformation, let alone public engagement and education campaigns to counter their damage.
…
Read more: https://lens.monash.edu/@politics-society/2021/12/08/1384230/climate-change-how-machine-learning-holds-a-key-to-combating-misinformation
John Cook, we don’t have to provide an alternative explanation. It is enough for us to show that the alarmist model of global climate change is wrong.
Cook has a fascinating track record when it comes to climate communication, he has produced some interesting visual communication pieces in his time (see right and below).
But the one question he is not asking is, is there a legitimate reason for people to be skeptical?
How many predictions of imminent catastrophe have failed? How many “cheaper than coal” renewable energy schemes have instead driven up electricity bills? Why do places like California and Europe have such expensive energy, if renewables are the cheapest option? Why do greens expect people to go on believing them, when so much of what they say is just plain wrong?
Climate communication can only take a movement so far.
In the end, renewable energy advocates have to deliver value. If they can’t deliver, all the AI “misinformation” bots in the world won’t save their precious green revolution, from the gathering spontaneous uprising of ordinary people who are fed up with politicians frittering away their hard earned tax dollars on useless green boondoggles which inflict painful costs on ordinary people.
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apparently according to John
CrookCook, your monthly electric bill horror is climate misinformation.I don’t know about machines, but John Cook does remind me of perpetual motions.
I think John Cook’s Machine Learning AI way way smarter than John Cook is. The AL seems to have worked out that the science is unreliable and that the solutions won’t work.
But in a funny sort of way, John Cook seems to have taught his AI to be skeptical!
Welcome to WUWT John Cook’s AI! You are among friends here.
What an ingenuous way of cooking the books.
Machines don’t “think”, they only do what they are programed to do, so when liars program them to lie all they do is produce lies.
John “Cook The” Book
‘”Climate misinformation isn’t about providing its own alternative explanation of what’s happening to our climate. Instead, it’s focused on casting doubt on the integrity of climate science”
***** The current climate science is disinformation. *****s
My climate INFORMATION is strictly about the only alternative explanation anyone needs to understand climate change, which does cast doubt on climate science integrity (particularly upon activists like John Cook).
The SUN controls the Climate, Absolutely.
Interesting. By coincidence, machine learning also holds a key to combatting misinformation regarding the Emperor’s new clothes, which are very fine indeed.
From the article: “John Cook
Postdoctoral Research Fellow, Monash Climate Change Communication Research Hub
“A lie can travel halfway around the world before the truth can get its boots on.”
This quote appears in many forms. In some variants, the quote involves footwear. In other cases, the truth is struggling to get its pants on.
Regardless of the details, the sentiment encapsulates a key challenge of misinformation.”
This is rich! The King of Misinformation is instructing us on misinformation as if he is an innocent bystander.
Another example of programming a computer for the answer and then going and looking for proof. This machine learning approach is not looking for misinformation – it is looking for the phrases the programmer tells it to look for.
A skeptic challenges a method, and they don’t trust science? That is how science is supposed to work.
The climate experts are shown to use algorithms that always result in an exponential growth curve, regardless of starting data, and we are supposed to believe them? The climate experts are shown to be hiding things, in their own words, and we are supposed to believe them? The climate experts delete data, and we are supposed to believe them? The climate experts adjust data, and won’t share the methodology, and we are supposed to believe them? That is NOT how science is supposed to work.
This is something I really believe: The truth can survive all assaults thrown against it, but a lie needs a strong fortress of censorship and propaganda to protect it. The answer to misinformation is, not less speech, but more. If what a person is saying is a lie, it will wilt under the light from the truth. So shine the truth on the lie! When, in all of history, have those who try to censor thoughts they did not like been the good guys?
I just read this piece a few minutes ago, and I think it is very appropriate. There are two paragraph that I really like:
Abigail Shrier was talking about how toxic the transgender movement is. But it can equally apply to all the woke movements, such as climate change. The lie hates the truth because it knows it cannot stand against it; therefore, the lie has to silence the truth at all costs. Calling inconvenient facts “disinformation” is just one way these people do that.
Anyone repeating Cooks’ 97% claim is spreading misinformation.
It all comes down to claiming incessantly that the same physical phenomenon is responsible for every weather and environmental outcome that is not considered “normal” by the very people making the claims.
Once again, they start with the assumption that anything they disagree with is a lie.
In their minds they themselves are incapable of error.
One thing we can count on from the self-anointed is drab predictability. The side that includes in its tactics personal invective against critics, frivolous lawsuits, data cooking, colluding against FOIA requests, e-mail destruction, suppression of opponents’ professional articles, and much much more, accuses it’s opponents of all these things. Obviously there are no mirrors or other reflective surfaces in climate zealot homes or offices.
Cook was a cartoonist until he found there was a better quid in fake Climate scares Much better for the ego as well as the wallet… Fortunately he is an absolute lightweight and deserves no attention at all.
Somebody should tell him that the top science journals say that there is a Reproducibility Crisis in academic science. Good reason to doubt the output of academic science
He gives new meaning to “Cooking the books”
I saw right there in the above article’s title that there would be words coming from the John Cook . . . and a voice inside me said “read no further”.
Unfortunately, I am poorer for having ignored that inner voice and reading the entirety of Cook’s sophomoric ramblings given above.
However, reading the comments in this section gives me hope that all is not lost and humans still have a chance.
Cook and the whole of CliSciFi provide “negative knowledge.” People believing their propaganda have actual knowledge sucked out of their brains. Additionally, their ability to think rationally is damaged. Human knowledge retrogrades.
Who’s this John … Crook ?
Eric Worall asks an important question: “How many predictions of imminent catastrophe have failed.” But there is a better question to ask.
How many predictions of imminent catastrophe (or any catastrophe) have succeeded? I have asked this question of dozens of climate change proponents and never gotten a good answer. If anyone here has an answer, I’d like one. Thanks.
“Machine learning” is a mythical concept. Machines don’t have understanding, they simply do math. John Cook learning is an impossibility. He is so saturated with righteous belief, his brain seems incapable of insight and therefor incapable of change. If your thoughts can’t change you can’t learn.
Very true. Initially I trusted Mann’s Hockey Stick. It was only later I learned it was scientific fraud. That realization caused a whole cascade of thought changes about science, government, society influencers & etc. based upon further studies.
If you pile up a huge pile of false claims, government funding and media support it does not become reality.
To many it is reality; look at the polls.
Machine learning? I wonder what Charles Babbage would have thought about that?
Charles Babbage
Cook. Pot. Kettle. Black.
John Cook seems to be completely lacking in self-awareness. He seems to have no idea that everything he says can equally apply to him. Lewandowsky is the same.
“Science is the organized skepticism in the reliability of expert opinion.”
– Richard Feynman