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.

气候变化的错误信息是一个复杂的社会问题，在技术和心理学交叉领域需要综合性、跨学科的解决方案，本研究提出了一种 “技术认知” 方法，将心理学和计算机科学研究综合起来，通过解构气候变化的错误信息进行分类，培训模型以检测这些错误信息中的谬误。