After nearly five years of developing and tweaking, John Cook and his colleagues debuted their project: a machine-learning algorithm that can detect climate misinformation on the web.
The algorithm sounds like science fiction: It “reads” a site and flags those with claims perpetrating false and misleading information about climate change science and solutions.
But something ironic happened around two weeks after the software was made public in Nature Scientific Reports. The algorithm detected a blog post about its own methods, attempting to discredit them.
“It’s quite funny. It was kind of a bit of a meta moment,” said Cook, a research fellow at the Climate Change Communication Research Hub at Monash University in Australia.
The team programmed the artificial intelligence to detect false claims in five categories: global warming is not happening; human greenhouse gases are not causing global warming; climate impacts are not bad; climate solutions won’t work; and climate science and scientists are unreliable.
Cook said the machine detected at least half a dozen claims spanning several categories in the blog post about itself.
“Ultimately, our goal is the Holy Grail of fact-checking, which is being able to detect and debunk misinformation in real time,” said Cook, who partly developed the framework previously at George Mason University. “Ideally, I would have social...
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