{"id":10953,"date":"2021-12-10T10:27:23","date_gmt":"2021-12-10T10:27:23","guid":{"rendered":"https:\/\/rationalemagazine.com\/?p=10953"},"modified":"2022-09-07T23:05:26","modified_gmt":"2022-09-07T13:05:26","slug":"climate-change-how-machine-learning-holds-a-key-to-combating-misinformation","status":"publish","type":"post","link":"https:\/\/rationalemagazine.com\/index.php\/2021\/12\/10\/climate-change-how-machine-learning-holds-a-key-to-combating-misinformation\/","title":{"rendered":"Climate change: How machine learning holds a key to combating misinformation"},"content":{"rendered":"

\u201cA lie can travel halfway around the world before the truth can get its boots on.\u201d This quote appears in many forms<\/a>. In some variants, the quote involves footwear. In other cases, the truth is struggling to get its pants on.<\/p>\n

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.<\/p>\n

Consequently, misinformation researchers speak wistfully of the \u201choly grail of fact-checking\u201d<\/a> \u2013 automatically detecting and debunking misinformation in one fell swoop. Machine learning offers the potential of both speed and scale \u2013 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.<\/p>\n

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?<\/p>\n

Misinformation and climate change<\/h3>\n

When it comes to misinformation about climate change, you often hear the terms \u201cwhack-a-mole\u201d<\/a> or \u201cclimate zombies\u201d<\/a> \u2013 typically expressed through clenched teeth. These refer to the fact that climate myths never seem to die, persistently rearing up to be debunked over and over. Indeed, the misleading arguments found in climate misinformation in the early 1990s are the same myths we now hear in 2021.<\/p>\n

While this can be annoying, climate zombies present a research opportunity. The fact that climate misinformation shows so much stability makes it possible to train a machine to detect misinformation claims.<\/p>\n

A number of years ago, myself and my colleagues Travis Coan and Mirjam Nanko from Exeter University, as well as Constantine Boussalis from Trinity College Dublin, began our quest for the fact-checking holy grail<\/a> \u2013 specifically focused on misinformation about climate change.<\/p>\n

The first step in this process was building a taxonomy of contrarian claims<\/a>. As we developed and refined the many claims we were seeing in climate misinformation, five main categories became clear \u2013 it\u2019s not happening; it\u2019s not us; it\u2019s not bad; solutions won\u2019t work; and experts are unreliable.<\/p>\n

These five categories of climate misinformation are noteworthy because they directly mirror the five key climate beliefs<\/a> developed from survey data by Ed Maibach<\/a> \u2013 it\u2019s happening; it\u2019s us; it\u2019s bad; there\u2019s hope; and experts agree. Consequently, we called our five categories of climate misinformation the five key climate disbeliefs.<\/p>\n

Once we had our taxonomy, it was time to roll up our sleeves and start training the machine.<\/p>\n

The principle of supervised machine learning is straightforward \u2013 take a paragraph of text from known sources of climate misinformation, and match it to a contrarian claim in our taxonomy (if there is a match). Then repeat that same process tens of thousands of times, until our machine is sufficiently trained to detect each misinformation claim. (Easy, right?).<\/p>\n

Fortunately, we were able to draw upon the help of the climate-literate Skeptical Science<\/a> team (which had form on crowd-sourcing content analysis of large climate datasets<\/a>).<\/p>\n

Once we had trained our machine to detect and categorise different misinformation claims, we fed our model 20 years\u2019 worth of climate misinformation \u2013 more than 250,000 articles from 20 prominent conservative think-tank websites and 33 blogs. It\u2019s the largest content analysis to date on climate misinformation, making it possible to construct a two-decade history of climate misinformation.<\/p>\n

The results weren\u2019t what I expected at all.<\/p>\n

The erosion of public trust in climate scientists<\/h3>\n

During the past 15 years, I\u2019ve been debunking scientific climate misinformation<\/a> \u2013 the type of myths that fell under the categories \u201cit\u2019s not happening\u201d, \u201cit\u2019s not us\u201d, or \u201cit\u2019s not bad\u201d.<\/p>\n

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.<\/p>\n

Climate misinformation isn\u2019t about providing its own alternative explanation of what\u2019s happening to our climate. Instead, it\u2019s focused on casting doubt on the integrity of climate science, and eroding public trust in climate scientists.<\/p>\n

This has significant consequences for scientists, educators, and fact-checkers. The majority of our efforts have focused on debunking scientific myths such as \u201cglobal warming isn\u2019t happening\u201d or \u201cclimate change is caused by the sun\u201d.<\/p>\n

But that\u2019s not where misinformation is focused \u2013 the focus is on attacking scientists and science itself. There\u2019s a dearth of research into understanding and countering this type of misinformation, let alone public engagement and education campaigns to counter their damage.<\/p>\n

\"\"<\/a><\/p>\n

Another strong trend was a growing prevalence of misinformation targeting climate solutions \u2013 claims that climate policies were harmful, attacking renewables, or spruiking fossil fuels. This category is becoming an increasingly dominant proportion of climate misinformation. This is particularly the case with conservative think-tanks, which tend to focus more on climate policy than science denial.<\/p>\n

The overall pattern in our data is clear \u2013 solutions denial is the future of climate misinformation.<\/p>\n

Our research was recently published in the Nature<\/em> journal Scientific Reports<\/a>. This was an important first step on our quest for the fact-checking holy grail. The next step is to synthesise our machine learning research with critical thinking research into deconstructing and analysing climate misinformation<\/a>.<\/p>\n

This task requires bringing together the vastly different disciplines of computer science and critical thinking philosophy. This is challenging, but interdisciplinary solutions are essential when dealing with complex, interconnected issues like misinformation.<\/a><\/p>\n

We still have a long way to go, but for now it\u2019s important to recognise the lessons already learnt while pursuing this quest. Not to mention the friends made along the way.<\/p>\n

 <\/p>\n

This article was originally published on <\/strong><\/em>Monash Lens<\/strong><\/a>.<\/strong><\/em><\/p>\n

Photo by Mika Baumeister on Unsplash.<\/strong><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

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