Abstract
Misinformation about climate change is a complex societal issue that requires 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 that counter misinformation require both fact-based (e.g., factual explanations) and technique-based (e.g., explanations of misleading techniques and logical fallacies) 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 examples of climate misinformation to reasoning fallacies. This dataset is used to train a model to detect fallacies in climate misinformation. We evaluate the model's performance using the F1 score, which measures how well the model detects relevant cases while avoiding irrelevant ones. Our study shows F1 scores that are 2.5-3.5 times 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.