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气候变化网络信息社会分享中的意识形态偏见。

Ideological biases in social sharing of online information about climate change.

机构信息

Department of Computer Science, University of Exeter, Exeter, Devon, United Kingdom.

出版信息

PLoS One. 2021 Apr 23;16(4):e0250656. doi: 10.1371/journal.pone.0250656. eCollection 2021.

DOI:10.1371/journal.pone.0250656
PMID:33891669
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8064565/
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.

摘要

媒体内容的接触是气候变化相关意见形成的一个重要组成部分。以本研究为重点的在线社交媒体,如 Twitter,为研究与气候变化相关的公众参与和数字媒体传播提供了途径。分享在线文章的链接是媒体参与的一个指标。聚合链接共享形成了一个网络结构,通过用户群体映射集体媒体参与。在这里,我们构建了一个链接用户和他们分享的网页的双边网络,使用的是大约 530 万条英语推文的数据集,这些推文由近 200 万用户在一个为期七周的活动期间发布,该活动集中在美国宣布退出气候变化的《巴黎协定》。社区检测表明,观察到的信息共享网络可以分为两个弱连接组件,代表了一组用户分享的文章的子集。我们通过分析共享文章的网页域名和文本内容来描述这些分区,发现它们大致可分为一个左翼/环保主义者群体和一个右翼/气候怀疑论者群体。相关分析表明,左翼/右翼政治意识形态与环保主义者/怀疑论者的气候意识形态之间存在显著的正相关关系。从时间上看,参与的用户群体和分享的文章都有相当大的变化,但网页来源和两极化的网络结构相对持久。这项研究提供了证据,表明与气候变化相关的新闻媒体内容的在线共享既两极分化又政治化,这对围绕这一重要社会挑战的舆论动态和公共辩论产生了影响。

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本文引用的文献

1
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2
Anatomy of news consumption on Facebook.脸书上新闻消费的剖析。
Proc Natl Acad Sci U S A. 2017 Mar 21;114(12):3035-3039. doi: 10.1073/pnas.1617052114. Epub 2017 Mar 6.
3
Modeling confirmation bias and polarization.建模确认偏误和极化。
社交媒体使人们能够在难以脱碳的建筑领域采取以人为本的气候行动。
Sci Rep. 2022 Nov 17;12(1):19017. doi: 10.1038/s41598-022-23624-9.
4
On network backbone extraction for modeling online collective behavior.网络骨干提取用于在线集体行为建模。
PLoS One. 2022 Sep 15;17(9):e0274218. doi: 10.1371/journal.pone.0274218. eCollection 2022.
Sci Rep. 2017 Jan 11;7:40391. doi: 10.1038/srep40391.
4
Tracking the release of IPCC AR5 on Twitter: Users, comments, and sources following the release of the Working Group I Summary for Policymakers.追踪 IPCC AR5 在 Twitter 上的发布情况:政策制定者摘要发布后,用户、评论和来源的跟踪。
Public Underst Sci. 2017 Oct;26(7):815-825. doi: 10.1177/0963662516628477. Epub 2016 Feb 11.
5
The spreading of misinformation online.网上错误信息的传播。
Proc Natl Acad Sci U S A. 2016 Jan 19;113(3):554-9. doi: 10.1073/pnas.1517441113. Epub 2016 Jan 4.
6
Global warming's five Germanys: A typology of Germans' views on climate change and patterns of media use and information.全球变暖的五个德国:德国人对气候变化的看法以及媒体使用和信息模式的类型学。
Public Underst Sci. 2017 May;26(4):434-451. doi: 10.1177/0963662515592558. Epub 2015 Jul 3.
7
Political science. Exposure to ideologically diverse news and opinion on Facebook.政治学。在 Facebook 上接触意识形态多样的新闻和观点。
Science. 2015 Jun 5;348(6239):1130-2. doi: 10.1126/science.aaa1160. Epub 2015 May 7.
8
ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the Gephi software.ForceAtlas2,一种为Gephi软件设计的用于便捷网络可视化的连续图布局算法。
PLoS One. 2014 Jun 10;9(6):e98679. doi: 10.1371/journal.pone.0098679. eCollection 2014.
9
Finding community structure in very large networks.在超大型网络中寻找社区结构。
Phys Rev E Stat Nonlin Soft Matter Phys. 2004 Dec;70(6 Pt 2):066111. doi: 10.1103/PhysRevE.70.066111. Epub 2004 Dec 6.
10
Finding and evaluating community structure in networks.在网络中寻找并评估社区结构。
Phys Rev E Stat Nonlin Soft Matter Phys. 2004 Feb;69(2 Pt 2):026113. doi: 10.1103/PhysRevE.69.026113. Epub 2004 Feb 26.