Ideological biases in social sharing of online information about climate change

Abstract

Exposure to media content is an important component of opinion formation around climate change. Online social media such as Twitter, the focus of this study, provide an avenue to study public engagement and digital media dissemination related to climate change. Sharing a link to an online article is an indicator of media engagement. Aggregated link-sharing forms a network structure which maps collective media engagement by the user population. Here we construct bipartite networks linking Twitter users to the web pages they shared, using a dataset of approximately 5.3 million English-language tweets by almost 2 million users during an eventful seven-week period centred on the announcement of the US withdrawal from the Paris Agreement on climate change. Community detection indicates that the observed information-sharing network can be partitioned into two weakly connected components, representing subsets of articles shared by a group of users. We characterise these partitions through analysis of web domains and text content from shared articles, finding them to be broadly described as a left-wing/environmentalist group and a right-wing/climate sceptic group. Correlation analysis shows a striking positive association between left/right political ideology and environmentalist/sceptic climate ideology respectively. Looking at information-sharing over time, there is considerable turnover in the engaged user population and the articles that are shared, but the web domain sources and polarised network structure are relatively persistent. This study provides evidence that online sharing of news media content related to climate change is both polarised and politicised, with implications for opinion dynamics and public debate around this important societal challenge.

On network backbone extraction for modeling online collective behavior

“…Such many-to-many interactions can be modeled as a network by building bipartite, a hypergraph or, more broadly, higher order models [53]. Yet, a large number of backbone extraction models work directly on projected networks where edges are added to represent interactions among pairs of users [10,12,19,20,[56][57][58]. This is the case of Disparity Filter, Noise Corrected, among others [33,36,77].…”

“…By applying a probabilistic backbone extraction method called Disparity Filter [ 77 ], they revealed the formation of communities of users that cross the boundaries of existing groups, favoring dissemination of information at large [ 76 ], including misinformation [ 21 ]. Similarly, different backbone extraction methods have been applied to uncover patterns describing the dynamics of online discussions on Instagram, Twitter, Facebook and Reddit [ 19 , 22 , 23 , 56 – 58 , 78 , 79 ].…”

“…These wellestablished network metrics and algorithms rely on a graph model where users are vertices connected by edges representing their interactions. Such interactions may involve several individuals simultaneously, e.g., multiple users sharing the same information [8,10,[18][19][20][21] or engaging in discussions around the same posts [22,23], creating many-to-many relationships.…”

Climate Change and Eco-Anxiety in the US: Predictors, Correlates, and Potential Solutions

“…Political orientation, eco-skepticism, and eco-anxiety may also be related. Cann, Weaver and Williams (2021) conducted social media sharing analysis of climate change information and found that levels of climate skepticism were associated with political ideology and information network polarization. As climate change continues to affect the planet, ecoanxiety and broad-scale research to understand the demographic and information exposure factors associated with it have increasing importance.…”

“…Political orientation, eco-skepticism, and eco-anxiety may also be related. Cann, Weaver and Williams (2021) conducted social media sharing analysis of climate change information survey, respondents answered questions regarding their demographics. At the end of the survey, respondents were thanked for their participation.…”