融合视觉语言模型与外部知识，提升气候虚假信息识别能力
Multimodal Climate Disinformation Detection: Integrating Vision-Language Models with External Knowledge Sources
- 结合视觉语言模型与实时外部信息源进行推理
- 可识别近期事件相关的气候虚假图像与言论
- 适合关注气候变化传播与信息治理的研究者
气候变化虚假信息已成为当今数字世界的主要挑战，尤其在社交媒体上广泛传播的误导性图片和视频。这些虚假内容往往极具说服力且难以识别，可能延缓气候行动。尽管视觉语言模型（VLMs）已被用于检测视觉虚假信息，但其依赖训练时已有的知识，难以对新近事件或更新做出合理判断。本文旨在通过将VLMs与外部知识源结合来克服这一局限。系统通过检索反向图像搜索结果、在线事实核查信息及可信专家内容，获取最新信息，从而更准确评估图像及其相关声明的真实性、误导性、虚假性或不可验证性。该方法显著提升了模型应对真实世界气候虚假信息的能力，有助于在快速变化的信息环境中维护公众对科学的认知。
原文摘要 · Abstract (English)
Climate disinformation has become a major challenge in today digital world, especially with the rise of misleading images and videos shared widely on social media. These false claims are often convincing and difficult to detect, which can delay actions on climate change. While vision-language models (VLMs) have been used to identify visual disinformation, they rely only on the knowledge available at the time of training. This limits their ability to reason about recent events or updates. The main goal of this paper is to overcome that limitation by combining VLMs with external knowledge. By retrieving up-to-date information such as reverse image results, online fact-checks, and trusted expert content, the system can better assess whether an image and its claim are accurate, misleading, false, or unverifiable. This approach improves the model ability to handle real-world climate disinformation and supports efforts to protect public understanding of science in a rapidly changing information landscape.
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