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

arXiv: https://arxiv.org/abs/2601.16108 · HTML: https://mlsanity.com/paper/2601.16108/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in MLLM jailbreaks and adversarial robustness, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 9 of its 11 references that are in the map.
- First posted on arXiv 22 Jan 2026.

## Placed in

- Multimodal LLMs / vision-language models / [MLLM jailbreaks and adversarial robustness](https://mlsanity.com/mllm/mllm-jailbreaks-and-adversarial-robustness/)

## Builds on (most cited first, 9 of 9)

- [LEMMA: Towards LVLM-Enhanced Multimodal Misinformation Detection with External Knowledge Augmentation](https://mlsanity.com/paper/2402.11943/) (2024)
- [GAMED: Knowledge Adaptive Multi-Experts Decoupling for Multimodal Fake News Detection](https://mlsanity.com/paper/2412.12164/) (2024)
- [CMIE: Combining MLLM Insights with External Evidence for Explainable Out-of-Context Misinformation Detection](https://mlsanity.com/paper/2505.23449/) (2025)
- [Seeing Through Deception: Uncovering Misleading Creator Intent in Multimodal News with Vision-Language Models](https://mlsanity.com/paper/2505.15489/) (2025)
- [Augmented CARDS: A machine learning approach to identifying triggers of climate change misinformation on Twitter](https://mlsanity.com/paper/2404.15673/) (2024)
- [Unlearning Climate Misinformation in Large Language Models](https://mlsanity.com/paper/2405.19563/) (2024)
- [CliME: Evaluating Multimodal Climate Discourse on Social Media and the Climate Alignment Quotient (CAQ)](https://mlsanity.com/paper/2504.03906/) (2025)
- [Promoting Reliable Knowledge about Climate Change: A Systematic Review of Effective Measures to Resist Manipulation on Social Media](https://mlsanity.com/paper/2410.23814/) (2024)
- [Enhancing LLMs for Governance with Human Oversight: Evaluating and Aligning LLMs on Expert Classification of Climate Misinformation for Detecting False or Misleading Claims about Climate Change](https://mlsanity.com/paper/2501.13802/) (2025)

## Abstract (arXiv)

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.