Summary of Augmented Cards: a Machine Learning Approach to Identifying Triggers Of Climate Change Misinformation on Twitter, by Cristian Rojas et al.
Augmented CARDS: A machine learning approach to identifying triggers of climate change misinformation on Twitter
by Cristian Rojas, Frank Algra-Maschio, Mark Andrejevic, Travis Coan, John Cook, Yuan-Fang Li
First submitted to arxiv on: 24 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper develops an AI model, the Augmented CARDS model, to detect and mitigate climate change misinformation on social media platforms, specifically Twitter. The authors find that over 50% of contrarian climate claims involve attacks on climate actors or conspiracy theories, and identify four stimuli that drive spikes in climate contrarianism: political events, natural events, contrarian influencers, or convinced influencers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Augmented CARDS model is a two-step hierarchical model designed to detect contrarian climate claims. The authors apply this model to five million climate-themed tweets over six months in 2022 and identify the factors driving climate contrarianism. This study demonstrates the importance of automated detection and mitigation strategies for countering misinformation about climate change. |
