Misinformation about climate change is a complex societal issue requiring holistic, interdisciplinary solutions at the intersection between technology and psychology. One proposed solution is a "technocognitive" approach, involving the synthesis of psychological and computer science research. Psychological research has identified that interventions in response to misinformation require both fact-based (e.g., factual explanations) and technique-based (e.g., explanations of misleading techniques) content. However, little progress has been made on documenting and detecting fallacies in climate misinformation. In this study, we apply a previously developed critical thinking methodology for deconstructing climate misinformation, in order to develop a dataset mapping different types of climate misinformation to reasoning fallacies. This dataset is used to train a model to detect fallacies in climate misinformation. Our study shows F1 scores that are 2.5 to 3.5 better than previous works. The fallacies that are easiest to detect include fake experts and anecdotal arguments, while fallacies that require background knowledge, such as oversimplification, misrepresentation, and slothful induction, are relatively more difficult to detect. This research lays the groundwork for development of solutions where automatically detected climate misinformation can be countered with generative technique-based corrections.

Misinformation about climate change poses a significant threat to societal well-being, prompting the urgent need for effective mitigation strategies. However, the rapid proliferation of online misinformation on social media platforms outpaces the ability of fact-checkers to debunk false claims. Automated detection of climate change misinformation offers a promising solution. In this study, we address this gap by developing a two-step hierarchical model, the Augmented CARDS model, specifically designed for detecting contrarian climate claims on Twitter. Furthermore, we apply the Augmented CARDS model to five million climate-themed tweets over a six-month period in 2022. We find that over half of contrarian climate claims on Twitter involve attacks on climate actors or conspiracy theories. Spikes in climate contrarianism coincide with one of four stimuli: political events, natural events, contrarian influencers, or convinced influencers. Implications for automated responses to climate misinformation are discussed.

Over the 20 years to 2017, the network of actors spreading scientific misinformation about climate change has been increasingly integrated into US political philanthropy. That's according to a study that used natural language processing to analyse connections between the two fields. "The study introduces a new and broader pathway through which climate change misinformation travels, beyond the tendency of research to narrowly focus on the activities of think-tanks and fossil-fuel interests, often in isolation from mainstream American institutions like philanthropy," writes Justin Farrell of Yale University, US, in Environmental Research Letters (ERL). "Yet, as this study also shows, the impact of funding from fossil-fuel sources still plays an important role, revealing that the strength of the relationship between the misinformation network and philanthropy is strongest for people and organizations directly tied to such funding." Farrell employed novel machine learning capabilities to recognise and classify repeating themes and links in lists of attendees and speakers at philanthropic meetings, millions of words of written materials, and lists of board members and lifetime achievement award winners.