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虚假新闻狂热者:新闻认知对在线分享行为的影响。

Fake news zealots: Effect of perception of news on online sharing behavior.

作者信息

t'Serstevens François, Piccillo Giulia, Grigoriev Alexander

机构信息

Department of Data Analytics and Digitilisation, Maastricht University, Maastricht, Netherlands.

Department of Economics, Maastricht University, Maastricht, Netherlands.

出版信息

Front Psychol. 2022 Jul 26;13:859534. doi: 10.3389/fpsyg.2022.859534. eCollection 2022.

DOI:10.3389/fpsyg.2022.859534
PMID:35959002
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9361856/
Abstract

Why do we share fake news? Despite a growing body of freely-available knowledge and information fake news has managed to spread more widely and deeply than before. This paper seeks to understand why this is the case. More specifically, using an experimental setting we aim to quantify the effect of veracity and perception on reaction likelihood. To examine the nature of this relationship, we set up an experiment that mimics the mechanics of Twitter, allowing us to observe the user perception, their reaction in the face of shown claims and the factual veracity of those claims. We find that perceived veracity significantly predicts how likely a user is to react, with higher perceived veracity leading to higher reaction rates. Additionally, we confirm that fake news is inherently more likely to be shared than other types of news. Lastly, we identify an activist-type behavior, meaning that belief in fake news is associated with significantly disproportionate spreading (compared to belief in true news).

摘要

我们为什么分享假新闻?尽管可免费获取的知识和信息越来越多,但假新闻却比以往传播得更广、更深。本文旨在探究为何会出现这种情况。更具体地说,我们利用一个实验环境来量化真实性和认知对反应可能性的影响。为了研究这种关系的本质,我们设置了一个模拟推特机制的实验,使我们能够观察用户认知、他们面对所展示的声明时的反应以及这些声明的事实真实性。我们发现,感知到的真实性能显著预测用户做出反应的可能性,感知到的真实性越高,反应率越高。此外,我们证实假新闻本质上比其他类型的新闻更有可能被分享。最后,我们识别出一种激进主义者类型的行为,即对假新闻的相信与显著不成比例的传播(与对真实新闻的相信相比)相关联。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5912/9361856/02c226773d83/fpsyg-13-859534-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5912/9361856/83bcf0ac4128/fpsyg-13-859534-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5912/9361856/55bf8a69ff9a/fpsyg-13-859534-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5912/9361856/02c226773d83/fpsyg-13-859534-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5912/9361856/83bcf0ac4128/fpsyg-13-859534-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5912/9361856/55bf8a69ff9a/fpsyg-13-859534-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5912/9361856/02c226773d83/fpsyg-13-859534-g0003.jpg

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本文引用的文献

1
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2
Shifting attention to accuracy can reduce misinformation online.将注意力转移到准确性上可以减少网络上的错误信息。
Nature. 2021 Apr;592(7855):590-595. doi: 10.1038/s41586-021-03344-2. Epub 2021 Mar 17.
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Inoculating Against Fake News About COVID-19.防范关于新冠病毒的虚假消息
Front Psychol. 2020 Oct 23;11:566790. doi: 10.3389/fpsyg.2020.566790. eCollection 2020.
4
Fighting COVID-19 Misinformation on Social Media: Experimental Evidence for a Scalable Accuracy-Nudge Intervention.社交媒体上抗击 COVID-19 错误信息:可扩展的准确性提示干预的实验证据。
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Sci Adv. 2020 Apr 3;6(14):eaay3539. doi: 10.1126/sciadv.aay3539. eCollection 2020 Apr.
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Fake news on Twitter during the 2016 U.S. presidential election.2016年美国总统大选期间推特上的假新闻。
Science. 2019 Jan 25;363(6425):374-378. doi: 10.1126/science.aau2706.
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Less than you think: Prevalence and predictors of fake news dissemination on Facebook.远低于你的想象:脸书上虚假新闻传播的流行程度和预测因素。
Sci Adv. 2019 Jan 9;5(1):eaau4586. doi: 10.1126/sciadv.aau4586. eCollection 2019 Jan.
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Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning.懒惰而非偏见:党派虚假新闻的易感性可以更好地用缺乏推理来解释,而不是用动机推理来解释。
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Science. 2018 Mar 9;359(6380):1146-1151. doi: 10.1126/science.aap9559.
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The science of fake news.假新闻的科学。
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