# Affective Polarization in Online Climate Change Discourse on Twitter

arXiv: https://arxiv.org/abs/2008.13051 · HTML: https://mlsanity.com/paper/2008.13051/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2020.

## Built on by (most cited first, 8 of 8)

- [Filter bubbles and affective polarization in user-personalized large language model outputs](https://mlsanity.com/paper/2311.14677/) (2023)
- [A More Advanced Group Polarization Measurement Approach Based on LLM-Based Agents and Graphs](https://mlsanity.com/paper/2411.12196/) (2024)
- [Understanding Opinions Towards Climate Change on Social Media](https://mlsanity.com/paper/2312.01217/) (2023)
- [Two-Stage Stance Labeling: User-Hashtag Heuristics with Graph Neural Networks](https://mlsanity.com/paper/2404.10228/) (2024)
- [A Few Hypocrites: Few-Shot Learning and Subtype Definitions for Detecting Hypocrisy Accusations in Online Climate Change Debates](https://mlsanity.com/paper/2409.16807/) (2024)
- [Affective Polarization across European Parliaments](https://mlsanity.com/paper/2508.18916/) (2025)
- [Evaluating Online Moderation Via LLM-Powered Counterfactual Simulations](https://mlsanity.com/paper/2511.07204/) (2025)
- [Measuring Social Media Polarization Using Large Language Models and Heuristic Rules](https://mlsanity.com/paper/2601.00927/) (2026)

## Abstract (arXiv)

Online social media has become an important platform to organize around different socio-cultural and political topics. An extensive scholarship has discussed how people are divided into echo-chamber-like groups. However, there is a lack of work related to quantifying hostile communication or textitaffective polarization between two competing groups. This paper proposes a systematic, network-based methodology for examining affective polarization in online conversations. Further, we apply our framework to 100 weeks of Twitter discourse about climate change. We find that deniers of climate change (Disbelievers) are more hostile towards people who believe (Believers) in the anthropogenic cause of climate change than vice versa. Moreover, Disbelievers use more words and hashtags related to natural disasters during more hostile weeks as compared to Believers. These findings bear implications for studying affective polarization in online discourse, especially concerning the subject of climate change. Lastly, we discuss our findings in the context of increasingly important climate change communication research.