Affective Polarization in Online Climate Change Discourse on Twitter
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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.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Aug 2020.
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Citing papers per year: 2023: 2; 2024: 3; 2025: 2; 2026: 1.
8 papers.
- 2023Filter bubbles and affective polarization in user-personalized large language model outputscited by 6
- 2024A More Advanced Group Polarization Measurement Approach Based on LLM-Based Agents and Graphscited by 1
- 2023Understanding Opinions Towards Climate Change on Social Mediacited by 0
- 2024Two-Stage Stance Labeling: User-Hashtag Heuristics with Graph Neural Networkscited by 0
- 2024A Few Hypocrites: Few-Shot Learning and Subtype Definitions for Detecting Hypocrisy Accusations in Online Climate Change Debatescited by 0
- 2025Affective Polarization across European Parliamentscited by 0
- 2025Evaluating Online Moderation Via LLM-Powered Counterfactual Simulationscited by 0
- 2026Measuring Social Media Polarization Using Large Language Models and Heuristic Rulescited by 0
- 2026Measuring Social Media Polarization Using Large Language Models and Heuristic Rules2 Jan 2026
- 2025Evaluating Online Moderation Via LLM-Powered Counterfactual Simulations10 Nov 2025
- 2025Affective Polarization across European Parliaments26 Aug 2025
- 2024A More Advanced Group Polarization Measurement Approach Based on LLM-Based Agents and Graphs19 Nov 2024
- 2024A Few Hypocrites: Few-Shot Learning and Subtype Definitions for Detecting Hypocrisy Accusations in Online Climate Change Debates25 Sep 2024
- 2024Two-Stage Stance Labeling: User-Hashtag Heuristics with Graph Neural Networks16 Apr 2024
- 2023Understanding Opinions Towards Climate Change on Social Media2 Dec 2023
- 2023Filter bubbles and affective polarization in user-personalized large language model outputs31 Oct 2023