检测气候错误信息中的谬误：一种识别误导性论证的技术认知方法
Detecting Fallacies in Climate Misinformation: A Technocognitive Approach to Identifying Misleading Argumentation
AI总结：
本文构建气候错误信息与推理谬误映射数据集并训练检测模型，F1较既往工作提升2.5至3.5，为生成式技巧纠正奠定基础。
摘要：
关于气候变化的错误信息是一个复杂的社会问题，需要在技术与心理学交叉领域采取整体性、跨学科的解决方案。一种被提出的方案是“技术认知”方法，即综合心理学与计算机科学研究。心理学研究已经发现，针对错误信息的干预既需要基于事实的内容（例如事实性解释），也需要基于技巧的内容（例如对误导性技巧的解释）。然而，在记录和检测气候错误信息中的谬误方面，目前进展甚微。在本研究中，我们应用此前开发的用于解构气候错误信息的批判性思维方法论，以构建一个将不同类型的气候错误信息映射到推理谬误的数据集。该数据集被用于训练一个检测气候错误信息中谬误的模型。我们的研究显示，F1分数比以往工作高出2.5至3.5。最容易检测的谬误包括虚假专家和轶事论证，而需要背景知识的谬误，例如过度简化、歪曲表述和懒于归纳，则相对更难检测。本研究为开发相关解决方案奠定了基础，使自动检测到的气候错误信息能够通过生成式的、基于技巧的纠正来加以应对。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
