2021
DOI: 10.48550/arxiv.2112.12137
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Growing polarisation around climate change on social media

Abstract: Climate change and political polarisation are two of the critical social and political issues of the 21st century. However, their interaction remains understudied. Here, we investigate the online discussion around the UN Conference of The Parties on Climate Change (COP) using social media data from 2014 to 2021. First, we highlight that cross-platform engagement peaked during COP26. Second, focusing on Twitter, we reveal low ideological polarisation between COP20 -COP25, with a large increase in polarisation d… Show more

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“…In recent works (Ramaciotti Morales et al, 2020;Cointet et al, 2021) we have suggested that these multi-dimensional parameters might be related to attitudes towards several issues of public debate, beyond the classical onedimensional attitude scale from left-right or liberal-conservatives cleavages. Several recent works leverage ideological inference, for example for measuring polarization in politics (Flamino et al, 2021) or around particular issues, such as climate change (Falkenberg et al, 2021). In this article, we further explore the extraction of multi-dimensional attitudinal indicators suggested by Ramaciotti Morales et al (2020), and later by Ramaciotti , detailing the inference of latent multidimensional parameters.…”
Section: Related Workmentioning
confidence: 99%
“…In recent works (Ramaciotti Morales et al, 2020;Cointet et al, 2021) we have suggested that these multi-dimensional parameters might be related to attitudes towards several issues of public debate, beyond the classical onedimensional attitude scale from left-right or liberal-conservatives cleavages. Several recent works leverage ideological inference, for example for measuring polarization in politics (Flamino et al, 2021) or around particular issues, such as climate change (Falkenberg et al, 2021). In this article, we further explore the extraction of multi-dimensional attitudinal indicators suggested by Ramaciotti Morales et al (2020), and later by Ramaciotti , detailing the inference of latent multidimensional parameters.…”
Section: Related Workmentioning
confidence: 99%