Growing polarization around climate change on social media

Abstract

Climate change and political polarization are two of the twenty-first century’s critical socio-political issues. Here we investigate their intersection by studying the discussion around the United Nations Conference of the Parties on Climate Change (COP) using Twitter data from 2014 to 2021. First, we reveal a large increase in ideological polarization during COP26, following low polarization between COP20 and COP25. Second, we show that this increase is driven by growing right-wing activity, a fourfold increase since COP21 relative to pro-climate groups. Finally, we identify a broad range of ‘climate contrarian’ views during COP26, emphasizing the theme of political hypocrisy as a topic of cross-ideological appeal; contrarian views and accusations of hypocrisy have become key themes in the Twitter climate discussion since 2019. With future climate action reliant on negotiations at COP27 and beyond, our results highlight the importance of monitoring polarization and its impacts in the public climate discourse.

Emotional framing in online environmental activism: Pairing a Twitter study with an offline experiment

“…An extensive body of literature has accumulated in recent years on how environmental issues have been presented and discussed on social media platforms. A major finding of this work has been the extent to which environmental discourses tend toward controversy (Wiest et al, 2015 ; Olausson, 2018 ; Sanford et al, 2021 ), negativity (Dahal et al, 2019 ; Loureiro and Alló, 2020 ; Tyagi et al, 2020 ; Sanford et al, 2021 ), and polarization (Jang and Hart, 2015 ; Williams et al, 2015 ; Garcia et al, 2019 ; Falkenberg et al, 2022 ). However, very few studies have examined the role of specific thought or opinion leaders in influencing these trends.…”

Facilitating system-level behavioural climate action using computational social science

“…Restoring public trust in climate research using behavioral science can be facilitated using large population-scale modeling that produces insights that can drive individual-level attitudinal and behavioral change 1,10 . CSS provides the tools to analyze population-scale data to reinforce and coach public and policy actors to distribute trustworthy information 4 . For example, approaches using human-in-theloop deep learning systems, called Reinforcement Learning with Human Feedback (RLHF) 7 , can take advantage of AI models and human evaluators to analyze large datasets and generate more trustworthy behavioral insights.…”

“…Computational social science (CSS) can act as a critical interdisciplinary bridge that advances theories of human behavior using large and sometimes disparate datasets, generating key insights using observational, experimental, and machine learning methods 2 . For instance, recent studies have used CSS approaches to measure the reactiveness of climate policies in the hard-todecarbonize sectors 3 , identifying growing polarization in public opinions regarding climate change 4 and classifying contrarian claims in communication networks 5 .…”

The Systemic Impact of Deplatforming on Social Media

“…We now measure whether the banned and matched cohorts are structurally segregated (or polarized) to assess whether the cohorts share the same, or different, audiences on Gettr. We measure segregation using the latent ideology, a well established method which constructs a synthetic ideological spectrum from user interactions on the platform [36][37][38] (see Methods). This measure orders the network of interactions between a set of influencer accounts (the banned and matched cohorts combined) and a set of accounts who interact with them (the nonverified cohort).…”