Influence of fake news in Twitter during the 2016 US presidential election

被引：477

作者：

Bovet, Alexandre ^{[1
,2
,3
,4
,5
]}

Makse, Hernan A. ^{[1
,2
]}

机构：

[1] CUNY, Levich Inst, New York, NY 10031 USA

[2] CUNY, Phys Dept, New York, NY 10031 USA

[3] Catholic Univ Louvain, ICTEAM, Ave George Lemaitre 4, B-1348 Louvain La Neuve, Belgium

[4] Univ Namur, NaXys, Rempart Vierge 8, B-5000 Namur, Belgium

[5] Univ Namur, Dept Math, Rempart Vierge 8, B-5000 Namur, Belgium

来源：

NATURE COMMUNICATIONS | 2019年 / 10卷 / 1期

基金：

瑞士国家科学基金会;

关键词：

CONFIRMATION; AGE;

D O I：

10.1038/s41467-018-07761-2

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

The dynamics and influence of fake news on Twitter during the 2016 US presidential election remains to be clarified. Here, we use a dataset of 171 million tweets in the five months preceding the election day to identify 30 million tweets, from 2.2 million users, which contain a link to news outlets. Based on a classification of news outlets curated by www.opensources.co, we find that 25% of these tweets spread either fake or extremely biased news. We characterize the networks of information flow to find the most influential spreaders of fake and traditional news and use causal modeling to uncover how fake news influenced the presidential election. We find that, while top influencers spreading traditional center and left leaning news largely influence the activity of Clinton supporters, this causality is reversed for the fake news: the activity of Trump supporters influences the dynamics of the top fake news spreaders.

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