Multimodal Climate Disinformation Detection: Integrating Vision-Language Models with External Knowledge Sources

2026-01-22

AI Summary

This work addresses the challenge of detecting time-sensitive climate-related multimodal misinformation on social media, which often evades conventional vision-language models due to their reliance on static knowledge acquired during training. To overcome this limitation, the authors propose a novel multimodal detection framework that integrates dynamic external knowledge by incorporating reverse image search, online fact-checking, and authoritative scientific content into the vision-language model. This integration enables real-time retrieval of up-to-date information, thereby enhancing the model’s ability to jointly reason over textual and visual cues in deceptive posts. The proposed approach significantly improves detection accuracy for previously unseen or temporally sensitive climate misinformation, effectively mitigating the constraints imposed by static knowledge bases in existing models.

Technology Category

• Computer Vision: Multi-modal Vision
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• climate disinformation
• multimodal detection
• vision-language models
• external knowledge
• misleading media

Innovation

• vision-language models
• external knowledge integration
• multimodal disinformation detection
• climate misinformation
• fact-checking