  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.
