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Computer-Assisted Detection and Classification of Misinformation

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Computer-Assisted Detection and Classification of Misinformation 1 Computer-assisted detection and classification of 2 misinformation about climate change 1 2 3,4,* 1 3 Travis G. Coan , Constantine Boussalis , John Cook , and Mirjam O. Nanko 1 4 Department of Politics and the Exeter Q-Step Centre, University of Exeter, United Kingdom. 2 5 Department of Political Science, Trinity College Dublin, Ireland. 3 6 Monash Climate Change Communication Research Hub, Monash University, Australia. 4 7 Center for Climate Change Communication, George Mason University, USA. * 8 Corresponding author email: [email protected] 9 ABSTRACT A growing body of scholarship investigates the role of misinformation in shaping the debate on climate change. Our research builds on and extends this literature by 1) developing and validating a comprehensive taxonomy of climate misinformation, 2) conducting the largest content analysis to date on contrarian claims, 3) developing a computational model to accurately detect specific claims, and 4) drawing on an extensive corpus from conservative think-tank (CTTs) websites and contrarian blogs to 10 construct a detailed history of misinformation over the past 20 years. Our study finds that climate misinformation produced by CTTs and contrarian blogs has focused on attacking the integrity of climate science and scientists and, increasingly, has challenged climate policy and renewable energy. We further demonstrate the utility of our approach by exploring the influence of corporate and foundation funding on the production and dissemination of specific contrarian claims. 11 Organised climate change contrarianism has played a significant role in the spread of misinformation and the delay of 1 12 meaningful action to mitigate climate change. Research suggests that climate misinformation leads to a number of negative 2 3 4 13 outcomes such as reduced climate literacy, public polarization, canceling out accurate information, reinforcing climate 5 6 14 silence, and influencing how scientists engage with the public. While experimental research offers valuable insight into 3, 7, 8 15 effective interventions for countering misinformation, researchers increasingly recognise that interdisciplinary approaches 9 16 are required to develop practical solutions at a scale commensurate with the size of online misinformation efforts. These 17 solutions not only require the ability to categorize and detect relevant contrarian claims at a level of specificity suitable for 18 debunking, but also to achieve these objectives at a scale consistent with the realities of the modern information environment. 19 An emerging interdisciplinary literature examines the detection and categorization of climate misinformation, with the vast 20 majority relying on manual content analysis. Studies have focused on claims associated with challenges to mainstream positions 10, 11 12, 13 21 on climate science (i.e., trend, attribution, and impact contrarianism), doubt about mitigation policies and technologies, 14, 15 22 and outright attacks on the reliability of climate science and scientists. Researchers, moreover, have examined the prevalence 14, 16 17, 18 23 of contrarian claims in conservative think tank (CTT) communications, congressional testimonies, fossil fuel industry 19 20, 21 24 communications, and legacy and social media. Given the significant costs associated with manual approaches for content 25 analysis, several recent studies have explored computational methods for examining climate misinformation, ranging from 15, 22 26 applications of unsupervised machine learning methods to measure climate themes in conservative think-tank articles, to 23 27 supervised learning of media frames such as economic costs of mitigation policy, free market ideology, and uncertainty. 28 Our work builds on and extends existing computational approaches by developing a model to detect specific contrarian 29 claims, as opposed to broad topics or themes. We develop a comprehensive taxonomy of contrarian claims that is sufficiently 30 detailed to assist in monitoring and counteracting climate misinformation. We then conduct the largest content analysis of 24 31 contrarian claims to date on CTTs and blogs—two key cogs in the so-called climate change “denial machine” —and employ 32 these data to train a state-of-the-art deep learning model to classify specific contrarian claims (Methods). Next, we construct a 33 detailed history of climate change contrarianism over the past two decades, based on a corpus of 249,413 documents from 20 34 prominent CTTs and 33 central contrarian blogs. Lastly, we demonstrate the utility of our computational approach by observing 25 35 the extent to which funding from "dark money", the fossil fuel industry, and other conservative donors correlates with the use 36 of particular claims against climate science and policy by CTTs. 37 A taxonomy of climate contrarian claims 38 Figure1 displays the taxonomy of claims used to categorize attacks on climate science and policy. To develop this framework, 39 we consulted the extant literature on climate misinformation to identify relevant claims, while further extending and refining Super-claims Sub-claims Sub-sub-claims Super-claims Sub-claims Sub-sub-claims 1. 1.1. Ice isn’t melting 1.1.1. Antarctica isn’t melting 4. 4.1. Policies are harmful 4.1.1. Policy increases costs Global warming Climate solutions is not happening 1.2. Heading into ice age 1.1.2. Greenland isn’t melting won’t work 4.1.2. Policy weakens security 1.3. Weather is cold 1.1.3. Arctic isn’t melting 4.1.3. Policy harms environment 1.4. Hiatus in warming 1.1.4. Glaciers aren’t vanishing 4.1.4. Rich future generations 1.5. Oceans are cooling 4.1.5. Limits freedom 1.6. Sea level rise is exaggerated 4.2. Policies are ineffective 4.2.1. Green jobs don’t work 1.7. Extremes aren’t increasing 4.2.2. Markets more efficient 1.8. Changed the name 4.2.3. Policy impact is negligible 4.2.4. One country is negligible 2. 2.1. It’s natural cycles 2.1.1. It’s the sun Human 4.2.5. Better to adapt Greenhouse Gases 2.2. Non-Greenhouse Gas forcings 2.1.2. It’s geological are not causing 4.2.6. China’s emissions global warming 2.1.3. It’s the ocean 4.2.7. Techno fix 2.1.4. Past climate change 2.1.5. Tiny CO 2 emissions 4.3. Too hard 4.3.1. Policy too difficult 4.3.2. Low public support 2.3. No evidence for Greenhouse 2.3.1. CO 2 is trace gas Effect 2.3.2. Greenhouse Effect is 4.4. Clean energy won’t work 4.4.1. Clean energy unreliable 2.4. CO 2 not rising saturated 4.4.2. Carbon Capture and 2.5. Emissions not raising CO 2 2.3.3. CO 2 lags climate Sequestration is unproven levels 2.3.4. Water vapor 4.5. We need energy 4.5.1. Fossil Fuels are plentiful 2.3.5. Tropospheric hot spot 4.5.2. Fossil Fuels are cheap 2.3.6. CO 2 high in past 4.5.3. Nuclear is good 3. 3.1. Sensitivity is low Climate impacts 5. 5.1. Science is unreliable 5.1.1. No consensus are not bad 3.2. No species impact 3.2.1. Species can adapt Climate movement/science 5.1.2. Proxies are unreliable 3.2.2. Polar bears ok is unreliable 5.1.3. Temp is unreliable 3.2.3. Oceans are ok 5.1.4. Models are unreliable 3.3. Not a pollutant 3.3.1. CO 2 is plant food 5.2. Movement is unreliable 5.2.1. Climate is religion 3.4. Only a few degrees 5.2.2. Media is alarmist 3.5. No link to conflict 5.2.3. Politicians are biased 3.6. No health impacts 5.2.4. Environmentalists are alarmist 5.2.5. Scientists are biased 5.3. Climate is conspiracy 5.3.1. Policy is conspiracy 5.3.2. Science is conspiracy Figure 1. Taxonomy of climate contrarian claims. This figure displays the three layers of claim-making by climate change contrarian actors. 40 this initial set by reading thousands of randomly selected paragraphs from prominent CTTs and contrarian blogs (see Methods). 41 This process yielded five major categories: (1) it’s not happening, (2) it’s not us, (2) it’s not bad, (4) solutions won’t work, 42 and (5) climate science/scientists are unreliable. We describe these categories as the five key climate disbeliefs, mirroring 26 43 the five key climate beliefs identified in survey research. Nested within these top-level categories were two sub-levels (27 44 sub-claims, 49 sub-sub-claims), allowing a detailed delineation of different specific arguments (see Supplementary Material and 45 Methods for additional information on how we developed the taxonomy). This work is, to our knowledge, the first framework 46 incorporating climate science misinformation, arguments against climate solutions, and attacks undermining climate science 47 and scientists in a single, comprehensive taxonomy. 48 While assessing the veracity of each of these claims is beyond the scope of this study, existing work has scrutinized subsets 27 49 of our taxonomy. Cook, Ellerton, and Kinkead analyzed denialist claims in categories 1 to 3, finding they all contained 50 reasoning fallacies. The veracity of some category 4 (climate policy) statements are more ambiguous, with some arguments 2/16 (b) Super-Claim Prevalence (CTTs) (a) Sub-claim Prevalence (CTTs and Blogs) 0.6 Solutions won't work Climate policies are harmful Climate movement is unreliable 0.4 Climate science is unreliable Science is unreliable Clean energy technology won't work People need fossil fuels 0.2 It's not us It's not happening It's natural cycles Proportion of Claims Extreme weather It's not bad isn't increasing 0.0 Climate policies are ineffective 2000 2005 2010 2015 2020 Climate hasn't warming over the last (few) decades (c) Super-Claim Prevalence (Blogs) CO2 is beneficial 0.6 Species aren't showing impacts or benefitting No evidence CO2 is driving climate change Science is unreliable Ice isn't melting 0.4 Climate sensitivity is low It's not us It's not happening Sea level rise is exaggerated /not accelerating 0.2 We're heading into global cooling Proportion of Claims Weather is cold Solutions won't work It's not bad 0.0 0.00 0.05 0.10 0.15 0.20 0.25 Proportion of Claims 2000 2005 2010 2015 2020 CTTs Blogs Figure 2.
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