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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.Polarization and the resulting political deadlock have become key barriers to more ambitious climate action. Using Twitter data between Conferences of the Parties, this research identifies a trend of increasing polarization driven by growing right-wing activity alongside accusations of political hypocrisy.

Founded in India in 2020, the microblogging site ‘Koo’ launched as an alternative to mainstream social media platforms, with the explicit aim of catering to non-Western communities in their vernacular languages, and capitalising on a period of tension between the Indian government and Twitter which led many users to seek Twitter-alternatives. Drawing on a near-complete dataset totalling over 71M posts and 399M user interactions, we show how Koo attracted users from several countries including India, Nigeria and Brazil, but with variable levels of sustained user engagement. We highlight how Koo’s interaction network was shaped by multiple country-specific migrations displaying strong divides between linguistic and cultural communities, for instance, with English-speaking communities from India and Nigeria largely isolated from one another. Finally, we analyse the content shared by different linguistic communities and identify cultural patterns which, we speculate, promoted similar discourses across language groups. Our results show that for language groups of similar sizes, Indian languages fostered higher discourse diversity than non-Indian languages, possibly highlighting synergistic effects which boosted the uptake and retention of these groups. Despite this, Koo failed to capitalise on this synergy and ceased operations in July 2024. With this context, our study points to some of the possible reasons why the multilingual and politically diverse platform Koo struggled to remain sustainable, failing to stave off competition from its US-based competitors, despite its commitment to cultivating support for the different vernacular communities of Indian social media users.

Node copying is an important mechanism for network formation, yet most models assume uniform copying rules. Motivated by observations of heterogeneous triadic closure in real networks, we introduce the concept of a hidden network model—a generative two-layer model in which an observed network evolves according to the structure of an underlying hidden layer—and apply the framework to a model of heterogeneous copying. Framed in a social context, these two layers represent a node’s inner social circle, and wider social circle, such that the model can bias copying probabilities towards, or against, a node’s inner circle of friends. Comparing the case of extreme inner circle bias to an equivalent model with uniform copying, we find that heterogeneous copying suppresses the power-law degree distributions commonly seen in copying models, and results in networks with much higher clustering than even the most optimum scenario for uniform copying. Similarly large clustering values are found in real collaboration networks, lending empirical support to the mechanism. Node duplication is an established model of network formation, whereby an existing node is duplicated, and edges are formed with uniform probability to the neighbours of the duplicated node. Here, the author proposes a copying model where links are copied depending on hidden interactions between nodes, and shows analytically and numerically that this leads to higher network clustering than in the uniform copying case, a property that is also found in real collaboration networks.

Existing studies of political polarization are often limited to a single country and one form of polarization, hindering a comprehensive understanding of the phenomenon. Here we investigate patterns of polarization online across nine countries (Canada, France, Germany, Italy, Poland, Spain, Turkey, UK, USA), focusing on the structure of political interaction networks, the use of toxic language targeting out-groups, and how these factors relate to user engagement. First, we show that political interaction networks are structurally polarized on Twitter (currently X). Second, we reveal that out-group interactions, defined by the network, are more toxic than in-group interactions, indicative of affective polarization. Third, we show that out-group interactions receive lower engagement than in-group interactions. Finally, we identify a common ally-enemy structure in political interactions, show that political mentions are more toxic than apolitical mentions, and highlight that interactions between politically engaged accounts are limited and rarely reciprocated. These results hold across countries and represent a step towards a stronger cross-country understanding of polarization. Identifying patterns of polarization is important for understanding its root cause. Here, using Twitter data from 9 countries, the authors show that out-group mentions use more toxic language than than in-group mentions, and political mentions are more toxic than apolitical mentions.

We explore the discourse on climate change across multiple social media platforms, examining the evolution of user engagement with climate-related content and whether this content links to reliable or unreliable news media sources. Through a detailed examination of over 20 million posts on Facebook, Instagram, Twitter, and YouTube over five years (2018–2022), we identify trends in engagement, distinguishing between unreliable and reliable content to assess the impact of misinformation. Further, we investigate the relationships among various discussion topics and their association with information quality, employing a network-based method to quantify the semantic distances between these categories. Our findings reveal diverse trends in engagement that align with global events, suggesting that social media discussions promptly reflect the resonance of real life events concerning climate change such as COP26, the Climate Action Week and climate strikes associated with the Fridays for Future movement. Notably, despite the lower volume of content linking to unreliable sources, we observe significantly greater relative engagement with these sources compared to content from reliable sources on all platforms except Twitter. This highlights a persistent challenge in the online discourse surrounding climate misinformation.

The abundance of information on social media has reshaped public discussions, shifting attention to the mechanisms that drive online discourse. This study analyzes large-scale Twitter (now X) data from three global debates—Climate Change, COVID-19, and the Russo-Ukrainian War—to investigate the structural dynamics of engagement. Our findings reveal that discussions are not primarily shaped by specific categories of actors, such as media or activists, but by shared ideological alignment. Users consistently form polarized communities, where their ideological stance in one debate predicts their positions in others. This polarization transcends individual topics, reflecting a broader pattern of ideological divides. Furthermore, the influence of individual actors within these communities appears secondary to the reinforcing effects of selective exposure and shared narratives. Overall, our results underscore that ideological alignment, rather than actor prominence, plays a central role in structuring online discourse and shaping the spread of information in polarized environments.

Micro-anatomical reentry has been identified as a potential driver of atrial fibrillation (AF). In this paper, we introduce a novel computational method which aims to identify which atrial regions are most susceptible to micro-reentry. The approach, which considers the structural basis for micro-reentry only, is based on the premise that the accumulation of electrically insulating interstitial fibrosis can be modelled by simulating percolation-like phenomena on spatial networks. Our results suggest that at high coupling, where micro-reentry is rare, the micro-reentrant substrate is highly clustered in areas where the atrial walls are thin and have convex wall morphology, likely facilitating localised treatment via ablation. However, as transverse connections between fibres are removed, mimicking the accumulation of interstitial fibrosis, the substrate becomes less spatially clustered, and the bias to forming in thin, convex regions of the atria is reduced, possibly restricting the efficacy of localised ablation. Comparing our algorithm on image-based models with and without atrial fibre structure, we find that strong longitudinal fibre coupling can suppress the micro-reentrant substrate, whereas regions with disordered fibre orientations have an enhanced risk of micro-reentry. With further development, these methods may be useful for modelling the temporal development of the fibrotic substrate on an individualised basis.

Analysis of tweets relating to the Conference of the Parties (COP) climate summits reveals greater polarization during COP26 than during previous summits. This increase in polarization is associated with growing right-wing engagement and emerged following the global climate strikes in 2019. Surprisingly, one topic unites pro-climate and climate-sceptic groups — ‘political hypocrisy’ — accusations of which have increased since 2019.

Abstract Deplatforming, or banning malicious accounts from social media, is a key tool for moderating online harms. However, the consequences of deplatforming for the wider social media ecosystem have been largely overlooked so far, due to the difficulty of tracking banned users. Here, we address this gap by studying the ban-induced platform migration from Twitter to Gettr. With a matched dataset of 15M Gettr posts and 12M Twitter tweets, we show that users active on both platforms post similar content as users active on Gettr but banned from Twitter, but the latter have higher retention and are 5 times more active. Our results suggest that increased Gettr use is not associated with a substantial increase in user toxicity over time. In fact, we reveal that matched users are more toxic on Twitter, where they can engage in abusive cross-ideological interactions, than Gettr. Our analysis shows that the matched cohort are ideologically aligned with the far-right, and that the ability to interact with political opponents may be part of Twitter’s appeal to these users. Finally, we identify structural changes in the Gettr network preceding the 2023 Brasília insurrections, highlighting the risks that poorly regulated social media platforms may pose to democratic life.