Detecting Fallacies in Climate Misinformation: A Technocognitive Approach to Identifying Misleading Argumentation
<|reference_start|>Detecting Fallacies in Climate Misinformation: A Technocognitive Approach to Identifying Misleading Argumentation: 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.<|reference_end|>
@article{zanartu2024detecting,
    title={Detecting Fallacies in Climate Misinformation: A Technocognitive
  Approach to Identifying Misleading Argumentation},
    author={Francisco Zanartu, John Cook, Markus Wagner, Julian Garcia},
    journal={arXiv preprint arXiv:2405.08254},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.08254},
    primaryClass={cs.CL}
}