# Augmented CARDS: A machine learning approach to identifying triggers of climate change misinformation on Twitter

arXiv: https://arxiv.org/abs/2404.15673 · HTML: https://mlsanity.com/paper/2404.15673/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 43 references that are in the map.
- First posted on arXiv 24 Apr 2024.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [Multitask Instruction-based Prompting for Fallacy Recognition](https://mlsanity.com/paper/2301.09992/) (2023)
- [Spread of Tweets in Climate Discussions](https://mlsanity.com/paper/2010.09801/) (2020)
- [Did the Musk Takeover Boost Contentious Actors on Twitter?](https://mlsanity.com/paper/2212.10646/) (2022)

## Built on by (most cited first, 4 of 4)

- [Detecting Fallacies in Climate Misinformation: A Technocognitive Approach to Identifying Misleading Argumentation](https://mlsanity.com/paper/2405.08254/) (2024)
- [Generative Debunking of Climate Misinformation](https://mlsanity.com/paper/2407.05599/) (2024)
- [Multimodal Climate Disinformation Detection: Integrating Vision-Language Models with External Knowledge Sources](https://mlsanity.com/paper/2601.16108/) (2026)
- [ClimateCheck 2026: Scientific Fact-Checking and Disinformation Narrative Classification of Climate-related Claims](https://mlsanity.com/paper/2603.26449/) (2026)

## Abstract (arXiv)

Misinformation about climate change poses a significant threat to societal well-being, prompting the urgent need for effective mitigation strategies. However, the rapid proliferation of online misinformation on social media platforms outpaces the ability of fact-checkers to debunk false claims. Automated detection of climate change misinformation offers a promising solution. In this study, we address this gap by developing a two-step hierarchical model, the Augmented CARDS model, specifically designed for detecting contrarian climate claims on Twitter. Furthermore, we apply the Augmented CARDS model to five million climate-themed tweets over a six-month period in 2022. We find that over half of contrarian climate claims on Twitter involve attacks on climate actors or conspiracy theories. Spikes in climate contrarianism coincide with one of four stimuli: political events, natural events, contrarian influencers, or convinced influencers. Implications for automated responses to climate misinformation are discussed.