  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.
